Agent-based Modeling and Agent-Oriented

Agent-based Modeling and Agent-Oriented Programming with the SARL Language SARL Lectures - UHasselt - 2016

St´ ephane GALLAND

Universit´ e de Bourgogne Franche-Comt´ e - UTBM 90010 Belfort cedex, France - http://www.multiagent.fr

Outline

A Introduction to Agent-Based Systems and SARL Language B Agent Environment: Definition and Examples C Organizational and Holonic Modeling D Design and Implementation of an Agent Platform

2

Introduction to Agent-Based Systems and SARL Language St´ ephane GALLAND


Goals of this Lecture

2

During this lecture, I will present: 1

the basics of the SARL programming language;

2

the basics of the multiagent simulation;

3

models of physic environment, Examples: highway and crowd simulations;

4

a cyber-physic model, Examples: intelligent autonomous vehicle.

Introduction

Multiagent Simulation

Physic Environment

Cyber-physical System

Conclusion

Outline

3

1 Introduction to Multiagent Systems 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Cyber-physical System

Introduction


Physic Environment


Conclusion

Outline

4

1 Introduction to Multiagent Systems Agents Multiagent Systems Programming Multiagent Systems with SARL Development Environment Execution Environment 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Cyber-physical System

Introduction


Physic Environment


Conclusion

History of Computing

5

Five ongoing trends have marked the history of computing Ubiquity; Interconnection; Intelligence; Delegation; Human-orientation: easy/natural to design/implement/use. Other Trends in Computer Science Grid Computing; Ubiquitous Computing; Semantic Web.

Introduction


Physic Environment


Conclusion

Agent: a first definition

6

Agent [Wooldridge, 2001] An agent is an entity with (at least) the following attributes / characteristics: Autonomy Reactivity Pro-activity Social Skills - Sociability No commonly/universally accepted definition.

Introduction


Physic Environment


Conclusion

Autonomy of an Agent

7

Autonomy Agents encapsulate their internal state (that is not accessible to other agents), and make decisions about what to do based on this state, without the direct intervention of humans or others; Able to act without any direct intervention of human users or other agents. Has control over his own internal state. Has control over his own actions (no master/slave relationship) Can, if necessary/required, modify his behavior according to his personal or social experience (adaptation-learning).

Introduction


Physic Environment


Conclusion

Reactivity of an Agent

8

Reactivity Agents are situated in an environment, (physical world, a user via a GUI, a collection of other agents, Internet, or perhaps many of these combined), are able to perceive this environment (through the use of potentially imperfect sensors), and are able to respond in a timely fashion to changes that occur in it; Environment static ⇒ the program can execute itself blindly. Real world as a lot of systems are highly dynamic: constantly changing, partial/incomplete information Design software in dynamic environment is difficult: failures, changes, etc. A reactive system perceives its environment and responds in a timely appropriate fashion to the changes that occur in this environment (Event-directed).

Introduction


Physic Environment


Conclusion

Pro-activity of an Agent

9

Pro-activity Agent do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking the initiative; They pursue their own personal or collective goals. Reactivity is limited (e.g. Stimulus ⇒ Response). A proactive system generates and attempts to capture objectives, it is not directed only by events, take the initiative. Recognize/Identify opportunities to act/trigger something.

Introduction


Physic Environment


Conclusion

Sociability of an Agent

10

Sociability - Social Ability Agents interact with other agents (and possibly humans), and typically have the ability to engage in social activities (such as cooperative problem solving or negotiation) in order to achieve their goals. Unity is strength. Many tasks can only be done by cooperating with others An agent must be able to interact with virtual or/and real entities Require a mechanism to exchange information either directly (Agent-to-Agent) or indirectly (through the environment). May require a specific (agent-communication) language.

Introduction


Physic Environment


Conclusion

Agents and Environment

11

An agent: Agent

is located in an environment (situatedness)

Agent mind

Filtered perception

Agent memory

Action

Behavior

perceives the environment through its sensors. Environment Interface

acts upon that environment through its effectors.

Agent body

Perception

tends to maximize progress towards its goals by acting in the environment.

Physical attributes (x,y,z), V(t) a(t)

More details will be given during Seminar #2

Introduction


Physic Environment


Conclusion

Action filter Influence

Perception filter

Other Agent’s Properties

12

Mobility: agent’s ability to move through different nodes of a network/grid. Adaptability: ability to modify his actions/behavior according to external conditions and perceptions. Versatility: ability to perform different tasks or to meet different objectives. Trustiness: level of confidence that inspires the agent to delegate tasks, perform action, collaborate with other agents. Robustness: ability to continue to operate in fault situations, even with lower performances Persistence: Ability to keep continuously running by retrieving or saving their internal state even after a crash or unexpected situations. Altruism: disposition of an agent to assist other agents in their tasks. Introduction


Physic Environment


Conclusion

Agent: another Definition

13

Agent [Ferber, 1999] Agent is a virtual (software) or physical entity which: is capable of acting in an environment; can communicate directly with other agents; is driven by a set of tendencies (in the form of individual objectives or of a satisfaction/survival function which it tries to optimize); possesses resources of its own; is capable to perceive its environment (but up to a limited extent); has only a partial representation of this environment (and perhaps none at all); possesses skills and can offer services; Add a comment to this line may be able to reproduce itself; whose behavior tends towards satisfying its objectives, taking account of the resources and skills available to it and depending on its perception, its representation and the communication it receives. Introduction


Physic Environment


Conclusion

Agent Typology

14

Reactive: Each agent has a mechanism of reaction to events, without having an explanation/understanding of the objectives nor planning mechanisms. Typical Example: The ant colony. Cognitive/Deliberative: Each agent has a knowledge base that contains all information and skills necessary for the accomplishment of their tasks/goals and the management of his interactions with other agents and his environment: reasoning, planning, normative. Typical Example: Multi-Expert Systems.

Reactive Agent

Introduction


Physic Environment

Hybrid Agent


Deliberative/Cognitive Agent

Conclusion

Agent vs Object

15

Agents are autonomous, they decide on the execution of services: “Objects do it for free; agents do it because they want to”.

Agent-based systems are inherently distributed and/or parallel (“multi-thread”). Each agent has its own resource of execution.

Introduction


Physic Environment


Conclusion

[Wooldridge, 2001]

The agents allow flexible (reactive, pro-active, social) and autonomous behavior.

Agent vs Expert System

16

Expert System Principle An Expert System (ES) maintains various facts about the world that are used to draw conclusions. Key Differences Traditional ES are not situated in an environment. There is no direct coupling with the environment and requires a user that acts as an intermediary ue. ES are usually not capable of exhibiting flexible behavior (reactive and proactive). ES are usually not provided with social skills (communication and interaction with other agents).

Introduction


Physic Environment


Conclusion

Two perspectives/approaches on Multiagent Systems

Mono-agent approach The system is composed of a single agent. Example: Personal Assistant Multi-agent approach The system is composed of multiple agents. The realization of global/collective task relies on a set of agents, on the composition of their actions. The solution emerges from the interactions of agents in an environment.

Introduction


Physic Environment


Conclusion

17

Multiagent systems: a first Definition

Multiagent systems An MultiAgent Systems (MAS) is a system composed of agents that interact together and through their environment.

Interactions: → Direct, agent to agent → Indirect, Stigmergy, through the Environment

Introduction


Physic Environment


Conclusion

18

Multiagent systems: From local to global Micro perspective (local): Agent Individual level Reactivity - Pro-activity Autonomy Delegation Macro perspective (global): Multiagent systems Society/Community level Distribution Decentralization (control and/or authority) Hierarchy Agreement technologies (coordination) Emergence, social order/pattern, norms Introduction


Physic Environment


Conclusion

19

Multiagent systems: another Definition

Multiagent Systems [Ferber, 1999] System comprising the following elements: An environment E , usually a space (with volume, 3D). An array of objects, O. These objects are situated. A set of agents, A, which are specific objects. A set of relations, R, which links the objects (and thus agents). A set of operations, Op, making it possible for agents to receive, produce, process and manipulate the objects in O. Operators with the task of representing the application of these operations and the reaction of the world to this attempt of modification.

Introduction


Physic Environment


Conclusion

20

What is the interest of MAS?

21

MAS’ interests [Wooldridge, 2001] Natural metaphors Distribution of Data or Control Legacy Systems: Wrap/Encapsulate these systems into an agent for enabling interactions Open Systems requires flexible autonomous decision making Complex Systems

Introduction


Physic Environment


Conclusion

Programming Language Evolution

22

Agent: a new paradigm ? Agent-Oriented Programming (AOP) reuses concepts and language artifacts from Actors and OOP. It also provides an higher-level abstraction than the other paradigms. level of abstraction

agent

Introduction

actor object procedural assembler


Physic Environment


Conclusion

Design Principles of SARL

23

Language All agents are holonic (recursive agents). There is not only one way of interacting but infinite. Event-driven interactions as the default interaction mode. Agent/environment architecture-independent. Massively parallel. Coding should be simple and fun. Execution Platform Clear separation between Language and Platform related aspects. Everything is distributed, and it should be transparent. Platform-independent. Introduction


Physic Environment


Conclusion

Comparing SARL to Other Frameworks

Spatial simulations General General

3

General Social/ natural sciences Social/ natural sciences General

3 3

Madkit NetLogo

Repast

SARL

3 3

C.Phys.c

3 3

3 3e

3

3

Lang.

Beginnersd

Free

GAML, Java Java AgentSpeaks Java Logo

**[*]

3

* *

3 3

** ***

3 3

Java, Python, .Net SARL, Java, Xtend, Python

**

Native support of hierarchies of agents.

b

Could be used for agent-based simulation.

c

Could be used for cyber-physical systems, or ambient systems.

d

*: experienced developers; **: for Computer Science Students; ***: for others beginners.

e

Ready-to-use Library:

Introduction


Jaak Simulation Library

Physic Environment


Conclusion

**[*]

3

This table was done according to experiments with my students.

Simu.b

Domain

GAMA Jade Jason

a

Hierar.a

Name

24

Overview of SARL Concepts

25

Multiagent System in SARL A collection of agents interacting together in a collection of shared distributed spaces. 4 main concepts

3 main dimensions Individual:: the Agent abstraction (Agent, Capacity, Skill)

Agent Capacity Skill

Collective:: the Interaction abstraction (Space, Event, etc.)

Space

Hierarchical:: the Holon abstraction (Context)

SARL: a general-purpose agent-oriented programming language. Rodriguez, S., Gaud, N., Galland, S. (2014) Presented at the The 2014 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IEEE Computer Society Press, Warsaw, Poland. [Rodriguez, 2014] http://www.sarl.io

Introduction


Physic Environment


Conclusion

Agent

26

Agent An agent is an autonomous entity having some intrinsic skills to implement the capacities it exhibits.

Agent Behaviors Behavior 1 Behavior 2

Physic Environment

Capacity4

Capacity2

Capacity3

Capacity1

Inner Context

Addr1

Default Space

Behavior n

Built-in Capacities

Skill Container

An agent defines a Context.


Space 2

Addr1

An agent initially owns native capacities called Built-in Capacities.

Introduction

Space 1

Addr2

a g e n t HelloAgent { on Initialize { println ( ” H e l l o World ! ” ) } on Destroy { println ( ” Goodbye World ! ” ) } }


Conclusion

Example of Agent Code

27

p a c k a g e org . multiagent . example a g e n t HelloAgent { v a r myvariable : i n t v a l myconstant = ” abc ”

The content of the file will be assumed to be in the given package.

on Initialize { println ( ” H e l l o World ! ” ) } on Destroy { println ( ” Goodbye World ! ” ) } }

Introduction


Physic Environment


Conclusion


27


Define the code of all the agents of type HelloAgent


Introduction


Physic Environment


Conclusion


27


This block of code contains all the elements related to the agent.


Introduction


Physic Environment


Conclusion


27


Define a variable with name "myvariable" and of type integer


Introduction


Physic Environment


Conclusion


27


Define a constant with name "myconstant" and the given value.


Introduction


Physic Environment


Conclusion


27

p a c k a g e org . multiagent . example a g e n t HelloAgent { v a r myvariable : i n t v a l myconstant = ” abc ” on Initialize { println ( ” H e l l o World ! ” ) }

Execute the block of code when an event of type "Initialize" is received by the agent.

on Destroy { println ( ” Goodbye World ! ” ) } }

Introduction


Physic Environment


Conclusion


27

p a c k a g e org . multiagent . example a g e n t HelloAgent { v a r myvariable : i n t v a l myconstant = ” abc ” on Initialize { println ( ” H e l l o World ! ” ) }

Events predefined in the SARL language: - When initializing the agent - When destroying the agent

on Destroy { println ( ” Goodbye World ! ” ) } }

Introduction


Physic Environment


Conclusion

Capacity and Skill

28

Action A specification of a transformation of a part of the designed system or its environment. Guarantees resulting properties if the system before the transformation satisfies a set of constraints. Defined in terms of pre- and post-conditions. Capacity Specification of a collection of actions. Skill A possible implementation of a capacity fulfilling all the constraints of its specification, the capacity.

Enable the separation between a generic behavior and agent-specific capabilities. Introduction


Physic Environment


Conclusion

Example of Capacity and Skill

29

c a p a c i t y Logging { d e f debug ( s : String ) a g e n t HelloAgent { d e f info ( s : String ) u s e s Logging } on Initialize { setSkill ( Logging , new B a s i c Co n s o l e L o g gi n g ) info ( ” H e l l o World ! ” ) }

s k i l l B a s i c C o n s o l e L o g gi n g i m p l e m e n t s Logging { d e f debug ( s : String ) { println ( ”DEBUG : ” + s ) } d e f info ( s : String ) { println ( ”INFO : ” + s )

Definition of a capacity that permits on Destroy { info ( ” Goodbye to an agent to print messages into World ! ” ) } the log system. }

} }

Introduction


Physic Environment


Conclusion


29


s k i l l B a s i c C o n s o l e L o g gi n g i m p l e m e n t s Logging { d e f debug ( s : String ) { println ( ”DEBUG : ” + s ) }

Define a function that could be on Destroy { info ( ” Goodbye World ! ” ) invoked by the agent. }

d e f info ( s : String ) { println ( ”INFO : ” + s ) }

}

}

Introduction


Physic Environment


Conclusion


29

c a p a c i t y Logging { d e f debug ( s : String )

Define the skill that implements the Logging a g ecapacity. n t HelloAgent {

d e f info ( s : String ) u s e s Logging } on Initialize { setSkill ( Logging , new B a s i c Co n s o l e L o g gi n g ) info ( ” H e l l o World ! ” ) }

s k i l l B a s i c C o n s o l e L o g gi n g i m p l e m e n t s Logging { d e f debug ( s : String ) { println ( ”DEBUG : ” + s )

on Destroy { info ( ” Goodbye World ! ” )

}

} d e f info ( s : String ) { println ( ”INFO : ” + s )

}

} }

Introduction


Physic Environment


Conclusion


29

c a p a c i t y Logging { d e f debug ( s : String ) d e f info ( s : String ) }

Every function declared into the implemented capacity must a g e n t HelloAgent { be implemented in the skill. s e s Logging The current uimplementations output the message onto the on Initialize { setSkill ( Logging , standard output stream.

s k i l l B a s i c C o n s o l e L o g gi n g i m p l e m e n t s Logging {

new B a s i c Co n s o l e L o g gi n g ) info ( ” H e l l o World ! ” ) }

d e f debug ( s : String ) { println ( ”DEBUG : ” + s )


}


}

} }

Introduction


Physic Environment


Conclusion


29

use of{a capacity into the c a p a c i t yThe Logging agent code is enabled by the

d e f debug ( s keyword. : String ) "uses"

a g e n t HelloAgent {

d e f info ( s : String ) u s e s Logging } on Initialize { setSkill ( Logging , new B a s i c Co n s o l e L o g gi n g ) info ( ” H e l l o World ! ” ) }

s k i l l B a s i c C o n s o l e L o g gi n g i m p l e m e n t s Logging { d e f debug ( s : String ) { println ( ”DEBUG : ” + s )


}


}

} }

Introduction


Physic Environment


Conclusion


29


s k i l l All B afunctions s i c C o n s o ldefined e L o g gi n ginto the i m p l eused m e n t scapacities Logging {are directly

callable from the source code.

d e f debug ( s : String ) { println ( ”DEBUG : ” + s ) }

on Destroy { info ( ” Goodbye World ! ” ) }

d e f info ( s : String ) { println ( ”INFO : ” + s )

}

} }

Introduction


Physic Environment


Conclusion


29


s k i l l An B aagent s i c C o nMUST s o l e L ospecify g gi n g the i m p l eskill m e n tto s Logging use for a{

capacity (except for the

d e f debug ( s : String ) { buildin (skills that are println ”DEBUG :” + s ) provided } by the execution framework)

on Destroy { info ( ” Goodbye World ! ” ) }

d e f info ( s : String ) { println ( ”INFO : ” + s )

}

} }

Introduction


Physic Environment


Conclusion

Space as the Support of Interactions between Agents

Space Support of interaction between agents respecting the rules defined in various Space Specifications. Space Specification Defines the rules (including action and perception) for interacting within a given set of Spaces respecting this specification. Defines the way agents are addressed and perceived by other agents in the same space. A way for implementing new interaction means. . The spaces and space specifications must be written with the Java programming language

Introduction


Physic Environment


Conclusion

30

Context and Spaces

31

Context Defines the boundary of a sub-system.

Default space

Collection of Spaces.

Space 1

Every Context has a Default Space. Space 2

Every Agent has a Default Context, the context where it was spawned.

Introduction


Physic Environment


Space 3

Conclusion

Context

Default Space in Every Contexts

32

Default Space: an Event Space Agent

Event-driven interaction space.

Behaviors Behavior 1 Behavior 2


Physic Environment


Capacity4

Capacity2

Capacity3

Inner Context

Event: the specification of some occurrence in a Space that may potentially trigger effects by a participant.

Introduction

Addr2 Addr1

Capacity1

Default Space of a context, contains all agents of the considered context.

Addr-1

Space 2

Addr-2

Default Space

Addr-3

Behavior n

Other agent Built-in Capacities

Skill Container

Conclusion

Addr1

Space 1

Example of Interactions: Ping - Pong

33

Default Space

PingAgent

PongAgent

Wait for partner Send Ping Send Pong Wait 1 second Send Ping Send Pong Wait 1 second

Introduction


Physic Environment


Conclusion


e v e n t Ping { v a r value : Integer new ( v : Integer ) { value = v } }

a g e n t PingAgent { u s e s Schedules u s e s DefaultContextInteractions v a r count : Integer on Initialize { println ( ” S t a r t i n g P i n g A g e n t . . . ” ) count = 0 e v e n t Pong { in ( 2 0 0 0 ) [ sendPing ] v a r value : Integer } new ( v : Integer ) { d e f sendPing { value = v i f ( defaultSpace . } participants . size > 1 ) { } Definition of an event for the PING. emit ( new Ping ( count ) ) It contains the ID of the Ping message count = count + 1 a g e n t PongAgent { in its "value" field. } else { u s e s DefaultContextInteractions The field is initialized in the constructor in ( 2 0 0 0 ) [ sendPing ] on Initialize { } of the event. println ( ” W a i t i n g f o r p i n g ” ) } } on Pong { on Ping { in ( 1 0 0 0 ) [ println ( ” Recv P i n g : ”+o c c u r r e n c e . println ( ” Send P i n g : ”+count ) value ) emit ( new Ping ( count ) ) println ( ” Send Pong : ”+o c c u r r e n c e . count = count + 1 value ) ] emit ( new Pong ( o c c u r r e n c e . value ) ) } } } }

Introduction


Physic Environment


Conclusion

34



a g e n t PingAgent { Definition of an event the PONG. u s e s for Schedules It contains the IDu of s e the s D Ping e f a umessage ltContextInteractions in its "value" field. v a r count : Integer The field is initialized in the constructor on Initialize { of the event. println ( ” S t a r t i n g P i n g A g e n t . . . ” ) count = 0 e v e n t Pong { in ( 2 0 0 0 ) [ sendPing ] v a r value : Integer } new ( v : Integer ) { d e f sendPing { value = v i f ( defaultSpace . } participants . size > 1 ) { } emit ( new Ping ( count ) ) count = count + 1 a g e n t PongAgent { } else { u s e s DefaultContextInteractions in ( 2 0 0 0 ) [ sendPing ] on Initialize { } println ( ” W a i t i n g f o r p i n g ” ) } } on Pong { on Ping { in ( 1 0 0 0 ) [ println ( ” Recv P i n g : ”+o c c u r r e n c e . println ( ” Send P i n g : ”+count ) value ) emit ( new Ping ( count ) ) println ( ” Send Pong : ”+o c c u r r e n c e . count = count + 1 value ) ] emit ( new Pong ( o c c u r r e n c e . value ) ) } } } }

Introduction


Physic Environment


Conclusion

34


e v e n t Ping { v a r value : Integer new ( v : Integer ) { value = v } } e v e n t Pong { v a r value : Integer new ( v : Integer ) { value = v } The PingAgent is waiting for a partner. } Check every 2 seconds if a partner is available. a g e n t PongAgent { u s e s DefaultContextInteractions on Initialize { println ( ” W a i t i n g f o r p i n g ” ) } on Ping { println ( ” Recv P i n g : ”+o c c u r r e n c e . value ) println ( ” Send Pong : ”+o c c u r r e n c e . value ) emit ( new Pong ( o c c u r r e n c e . value ) ) } }

Introduction


Physic Environment

a g e n t PingAgent { u s e s Schedules u s e s DefaultContextInteractions v a r count : Integer on Initialize { println ( ” S t a r t i n g P i n g A g e n t . . . ” ) count = 0 in ( 2 0 0 0 ) [ sendPing ] } d e f sendPing { i f ( defaultSpace . participants . size > 1 ) { emit ( new Ping ( count ) ) count = count + 1 } else { in ( 2 0 0 0 ) [ sendPing ] } } on Pong { in ( 1 0 0 0 ) [ println ( ” Send P i n g : ”+count ) emit ( new Ping ( count ) ) count = count + 1 ] } }


Conclusion

34


e v e n t Ping { v a r value : Integer new ( v : Integer ) { value = v } } e v e n t Pong { v a r value : Integer new ( v : Integer ) { value = v } } Special syntax: a g e n t PongAgent { u s e s DefaultContextInteractions fct(param1, param2, ...) [ code ] on Initialize { println ( ” W a i t i n g f o r p i n g ” ) Call the function "fct" with the given } parameters AND on Ping { a peice (of code as parameter. println ” Recv P ithe n g :last ”+o ccurrence . value ) The function is supposed to execute println ( ” Send Pong : ”+o c c u r r e nthe ce . piece of value ) code according to its semantic. emit ( new Pong ( o c c u r r e n c e . value ) ) } }

Introduction


Physic Environment



Conclusion

34


e v e n t Ping { v a r value : Integer new ( v : Integer ) { value = v } sendPing is the function that tries to } contact the partner for the first time. e v e n t Pong { this function in 2 seconds. v a Run r value : Integer new ( v : Integer ) { The in()=function is provided by the value v } "Schedules" built-in capacity. } a g e n t PongAgent { u s e s DefaultContextInteractions on Initialize { println ( ” W a i t i n g f o r p i n g ” ) } on Ping { println ( ” Recv P i n g : ”+o c c u r r e n c e . value ) println ( ” Send Pong : ”+o c c u r r e n c e . value ) emit ( new Pong ( o c c u r r e n c e . value ) ) } }

Introduction


Physic Environment



Conclusion

34


e v e n t Ping { v a r value : Integer new ( v : Integer ) { value = v } Test if the number of participants } in the default space is greater than 1 when PingAgent, 2 when PingAgent e v e n (1 t Pong { PongAgent are inside). v a and r value : Integer new ( v : Integer ) { The defaultSpace is accessible via the value = v } built-in capacity } "DefaultContextInteraction". a g e n t PongAgent { u s e s DefaultContextInteractions on Initialize { println ( ” W a i t i n g f o r p i n g ” ) } on Ping { println ( ” Recv P i n g : ”+o c c u r r e n c e . value ) println ( ” Send Pong : ”+o c c u r r e n c e . value ) emit ( new Pong ( o c c u r r e n c e . value ) ) } }

Introduction


Physic Environment



Conclusion

34


e v e n t Ping { v a r value : Integer new ( v : Integer ) { value = v } When the PongAgent is available, } the PingAgent sends the first Ping e v e n message. t Pong { v a r value : Integer The variable)is{defined for keeping new ( vcount : Integer track valueof=the v ID of the messages. } } a g e n t PongAgent { u s e s DefaultContextInteractions on Initialize { println ( ” W a i t i n g f o r p i n g ” ) } on Ping { println ( ” Recv P i n g : ”+o c c u r r e n c e . value ) println ( ” Send Pong : ”+o c c u r r e n c e . value ) emit ( new Pong ( o c c u r r e n c e . value ) ) } }

Introduction


Physic Environment



Conclusion

34


e v e n t Ping { v a r value : Integer new ( v : Integer ) { value = v } } e v e n t Pong { v a r value : Integer new ( v : Integer ) { value = v } } the PongAgent is NOT AVAILABLE a g e nWhen t PongAgent { u sthe e s PingAgent D e f a u l t C waits o n t e x2t seconds I n t e r a cbefore tions onchecking Initialize { the PongAgent is again if println ( ” W a i t i n g f o r p i n g ” ) alive. } on Ping { println ( ” Recv P i n g : ”+o c c u r r e n c e . value ) println ( ” Send Pong : ”+o c c u r r e n c e . value ) emit ( new Pong ( o c c u r r e n c e . value ) ) } }

Introduction


Physic Environment



Conclusion

34


e v e n t Ping { a g e n t PingAgent { v a r value : Integer u s e s Schedules new ( v : Integer ) { u s e s DefaultContextInteractions value = v Definition of the PongAgent with: v a r count : Integer } - a constructor. on Initialize { } println ( ” S t a r t i n g P i n g A g e n t . . . ” ) - a event handler on the Ping messages. count = 0 e v e n t Pong { in ( 2 0 0 0 ) [ sendPing ] v a r value : Integer } new ( v : Integer ) { d e f sendPing { value = v i f ( defaultSpace . } participants . size > 1 ) { } emit ( new Ping ( count ) ) count = count + 1 a g e n t PongAgent { } else { u s e s DefaultContextInteractions in ( 2 0 0 0 ) [ sendPing ] on Initialize { } println ( ” W a i t i n g f o r p i n g ” ) } } on Pong { on Ping { in ( 1 0 0 0 ) [ println ( ” Recv P i n g : ”+o c c u r r e n c e . println ( ” Send P i n g : ”+count ) value ) emit ( new Ping ( count ) ) println ( ” Send Pong : ”+o c c u r r e n c e . count = count + 1 value ) ] emit ( new Pong ( o c c u r r e n c e . value ) ) } } } }

Introduction


Physic Environment


Conclusion

34



a g e n t PingAgent { u s e s Schedules u s e s DefaultContextInteractions v a r count : Integer on Initialize { println ( ” S t a r t i n g P i n g A g e n t . . . ” ) count = 0 e v e n t Pong { Declare an event handler: in ( 2 0 0 0 ) [ sendPing ] v a r value : Integer a piece of that is run when a}message new ( v : Integer ) { of type "Ping" is received. d e f sendPing { value = v i f ( defaultSpace . } participants . size > 1 ) { } emit ( new Ping ( count ) ) count = count + 1 a g e n t PongAgent { } else { u s e s DefaultContextInteractions in ( 2 0 0 0 ) [ sendPing ] on Initialize { } println ( ” W a i t i n g f o r p i n g ” ) } } on Pong { on Ping { in ( 1 0 0 0 ) [ println ( ” Recv P i n g : ”+o c c u r r e n c e . println ( ” Send P i n g : ”+count ) value ) emit ( new Ping ( count ) ) println ( ” Send Pong : ”+o c c u r r e n c e . count = count + 1 value ) ] emit ( new Pong ( o c c u r r e n c e . value ) ) } } } }

Introduction


Physic Environment


Conclusion

34



a g e n t PingAgent { u s e s Schedules u s e s DefaultContextInteractions v a r count : Integer on Initialize { println ( ” S t a r t i n g P i n g A g e n t . . . ” ) count = 0 e v e n t Pong { in ( 2 0 0 0 ) [ sendPing ] v a r value : Integer } new ( v : Integer ) { d e f sendPing { value = v i f ( defaultSpace . } participants . size > 1 ) { } Instructions to execute when a message emit ( new Ping ( count ) ) of type "Ping" is received. count = count + 1 a g e n t PongAgent { } else { u s e s DefaultContextInteractions in ( 2 0 0 0 ) [ sendPing ] on Initialize { } println ( ” W a i t i n g f o r p i n g ” ) } } on Pong { on Ping { in ( 1 0 0 0 ) [ println ( ” Recv P i n g : ”+o c c u r r e n c e . println ( ” Send P i n g : ”+count ) value ) emit ( new Ping ( count ) ) println ( ” Send Pong : ”+o c c u r r e n c e . count = count + 1 value ) ] emit ( new Pong ( o c c u r r e n c e . value ) ) } } } }

Introduction


Physic Environment


Conclusion

34


e v e n t Ping { a g e n t PingAgent { v a r value : Integer u s e s Schedules new ( v : Integer ) { u s e s DefaultContextInteractions value = v v a r count : Integer Emit a Pong in the default space of the } on Initialize { } default context. println ( ” S t a r t i n g P i n g A g e n t . . . ” ) count = 0 e v e n t Pong { The Pong message has the same valuein ( 2 0 0 0 ) [ sendPing ] v a r value : Integer of the received Ping event. } new ( v : Integer ) { d e f sendPing { value = v "occurrence" is the variable that contains i f ( defaultSpace . } the received Ping event. participants . size > 1 ) { } emit ( new Ping ( count ) ) count = count + 1 a g e n t PongAgent { } else { u s e s DefaultContextInteractions in ( 2 0 0 0 ) [ sendPing ] on Initialize { } println ( ” W a i t i n g f o r p i n g ” ) } } on Pong { on Ping { in ( 1 0 0 0 ) [ println ( ” Recv P i n g : ”+o c c u r r e n c e . println ( ” Send P i n g : ”+count ) value ) emit ( new Ping ( count ) ) println ( ” Send Pong : ”+o c c u r r e n c e . count = count + 1 value ) ] emit ( new Pong ( o c c u r r e n c e . value ) ) } } } }

Introduction


Physic Environment


Conclusion

34


e v e n t Ping { v a r value : Integer new ( v : Integer ) { value = v } } e v e n t Pong { v a r value : Integer new ( v : Integer ) { value = v } } When the PingAgent is receiving a Pong a g e n t message, PongAgent { it waits 1 second and sends u s e s DefaultContextInteractions a Ping message with an incremented ID. on Initialize { println ( ” W a i t i n g f o r p i n g ” ) } on Ping { println ( ” Recv P i n g : ”+o c c u r r e n c e . value ) println ( ” Send Pong : ”+o c c u r r e n c e . value ) emit ( new Pong ( o c c u r r e n c e . value ) ) } }

Introduction


Physic Environment



Conclusion

34

Holarchy: multi-hierarchies of agents

35

Contexts and Holonic properties DefaultContext Default Space

All agents participate in the default space of all contexts they belong to.

Addr15

Addr16

Addr12

Addr13

ExternalContext 1 Addr-2

Addr-1

Addr10

Addr11

Addr17

Addr18

InnerContext

Holonic Perspective

Addr-4

Addr-8

Addr-5

Addr-3

Addr-6

Addr-7

Addr-9

All agents contains an internal context. Enable to build hierarchy of agents: holarchy.

More details will be given during Seminar #3 Introduction


Physic Environment

Default Space

Addr14


Conclusion

Default Space (Holonic group) Other Space (Production group)

Level n-1 Level n

All agents have at least one context: the default one.

SARL in the Eclipse IDE

Introduction


Physic Environment


36

Conclusion

Use the Java Perspective

37

It is recommended to use the "Java" perspective for developping with the SARL programming language.

Introduction


Physic Environment


Conclusion

Create a SARL Project

38

1. Enter the project name. 2. Select the execution environment, if present.

3. Open the next page.

Introduction


Physic Environment


Conclusion

Create a SARL Project (cont.)

39

4. Check the paths.

5. Create the project.

Introduction


Physic Environment


Conclusion

Create a SARL Project (cont.)

Introduction


Physic Environment


Conclusion

40

Create Your First Agent

41

2. Select the super type, if your agent type must inherit from a specific agent type.

1. Enter the agent type name.

3. Create the agent code

Introduction


Physic Environment


Conclusion

Create Your First Agent (cont.)

Introduction


Physic Environment


Conclusion

42

Define the Execution Environment 2. Check if one SARL Runtime Environment (SRE) was installed

1. Open the preference page: > SARL > Installed SREs

3. Add SRE if needed.

4. Save & Close

Introduction


Physic Environment


Conclusion

43

Executing the Agent with Janus

2. Click on "SARL Application" & Create a new configuration. 1. Open the dialog box of the "Run Configurations"

Introduction


Physic Environment


Conclusion

44

Executing the Agent with Janus (cont.)

3. Enter the name of the configuration. 4. Enter the name of the project.

6. Run the agent. 5. Enter the agent to launch.

Introduction


Physic Environment


Conclusion

45

Runtime Environment for SARL Runtime Environment Requirements Implements SARL concepts. Provides Built-in Capacities. Handles Agent’s Lifecycle. Handles resources. Janus as a SARL Runtime Environment Fully distributed. Dynamic discovery of Kernels. Automatic synchronization of kernels’ data (easy recovery). Micro-Kernel implementation. Official website: http://www.janusproject.io Other SREs may be created. See Seminar #4.

Introduction


Physic Environment


Conclusion

46

Outline

47

1 Introduction to Multiagent Systems 2 Multiagent Simulation Simulation Fundamentals Multiagent Based Simulation (MABS) Overview of a MABS Architecture Agent Architectures 3 Simulation with a Physic Environment 4 Cyber-physical System

Introduction


Physic Environment


Conclusion

A Definition of Simulation

48

[Shannon, 1977] The process of designing a model of a real system and conducting experiments with this model for the purpose either of understanding the behavior of the system or of evaluating various strategies (within the limits imposed by a criterion or a set of criteria) for the operation of the system. Why simulate? Understand / optimize a system. Scenarii/strategies evaluation, testing hypotheses to explain a phenomenom (decision-helping tool). Predicting the evolution of a system, e.g. metrology.

Introduction


Physic Environment


Conclusion

Simulation Basics

49

Real System

System Model Abstraction/Simplification/Focus

n

Observations Experimental results

Introduction


Physic Environment

Simulation

Co nfr on tat io

Tuning model

Explanation/Optimization/Prediction

Simulation results/outputs


Conclusion

Modeling Relation: System ↔ Model

50

Modeling Relation

Simulation Relation

Is the model valid?

Is the simulator valid?

System Model

Modeling

Simulation

Implementation

To determine if the system model is an acceptable simplificiation in terms of quality criteria and experimentation objectives. Directly related to the consistency of the model simulation.

Introduction


Physic Environment


Conclusion

[Zeigler, 2000]

Real System

Simulation Relation: Model ↔ Simulator

51

Modeling Relation

Simulation Relation

Is the model valid?

Is the simulator valid?

System Model

Modeling

Simulation

Implementation

To guarantee that the simulator, used to implement the model, correctly generates the behavior of the model. To be sure that the simulator reproduces clearly the mechanisms of change of state are formalized in the model.

Introduction


Physic Environment


Conclusion

[Zeigler, 2000]

Real System

Classical Typology of the Simulation (1/4)

-accurate -expensive

The system structure is viewed as emergent from the interactions between the individuals.

Meso

+accurate +expensive

Introduction


Physic Environment


Conclusion

Macro

Micro

[Hoogendoorn, 2001, Davidsson, 2000]

Microscopic Simulation Explicitly attempts to model the behaviors of each individual.

52


Mesoscopic Simulation

53


Macro

Based on small groups, within which elements are considered homogeneous. Examples: vehicle platoon dynamics and household-level travel behavior.


Introduction


Physic Environment


Conclusion

Micro


Meso


Macroscopic Simulation

54


Macro

Based on mathematical models, where the characteristics of a population are averaged together. Simulate changes in these averaged characteristics for the whole population.

Meso


Introduction


Physic Environment


Conclusion

Micro


The set of individuals is viewed as a structure that can be characterized by a number of variables.


Multilevel Simulation

55


Macro

Combines various levels. Specify how the different levels are interacting together: one is input of the other, dynamic selection of the level. Meso

Population

Éxécute les holons membre

21

1

2

3

Mère


Physic Environment


Groupe spatial

4

Enfant

Père

Introduction

22

Famille

1

2

Piétons Enfant

3 Piétons

Piétons


Conclusion

Micro

[Hoogendoorn, 2001, Davidsson, 2000, Galland, 2014b]


31


Multilevel Simulation

55


Macro

Combines various levels. Specify how the different levels are interacting together: one is input of the other, dynamic selection of the level. Meso


31 Population


21

22

Famille

1

2

3

4

Enfant

Père Mère


Groupe spatial

1

2

Piétons Enfant

Micro

3 Piétons

Piétons

Multiagent-based Simulation (MABS), aka. ABS, is traditionnally considered as a special form of microscopic simulation, but not restricted to.

Introduction


Physic Environment


Conclusion

MABS: General Idea

56

Create an artificial world composed of interacting agents. The behavior of an agent results from: its perceptions/observations; its internal motivations/goals/beliefs/desires; its eventual representations; its interaction with the environment (indirect interactions, ressources) and the other agents (communications, direct interactions, stimuli).

Agents act and modify the state of the environment through their actions. We observe the results of the interactions like in a Virtual Lab ⇒ Emergence.

Introduction


Physic Environment


Conclusion

MABS: Main Characteristics and Advantages More flexible than macroscopic models to simulate spatial and evolutionary phenomena. Dealing with real multiagent systems directly: real Agent = simulated Agent. Allows modelling of adaptation and evolution. Heterogeneous space and population. Multilevel modeling: integrate different levels of observation, and of agent’s behaviors.

Introduction


Physic Environment


Conclusion

57

MABS: Limitations and Drawbacks Offer a significant level of accuracy at the expense of a larger computational cost.

Require many and accurate data for their initialization.

It is difficult to apply to large scale systems.

Actual simulation models are costly in time and effort.

Introduction


Physic Environment


Conclusion

58

General Architecture

Agent

59

Direct interaction Perceptions

Agent Actions

Environment - Resources, services, objects - Rules, laws - Physical structures (spatial and topological) - Communication structures (stigmergy, implicit communication) - Social structure

Introduction


Physic Environment


Conclusion


Agent

59

Direct interaction Perceptions

Agent Actions

Environment - Resources, services, objects - Rules, laws - Physical structures (spatial and topological) - Communication structures (stigmergy, implicit communication) - Social structure

Introduction


Physic Environment


Conclusion

Simulation Controller


59

Direct interaction

Agent

Agent

Perceptions

Actions

Environment - Resources, services, objects - Rules, laws - Physical structures (spatial and topological) - Communication structures (stigmergy, implicit communication) - Social structure Change events

Rendering Software Modules (1D, 2D or 3D) Observer

Introduction


Physic Environment


Conclusion



59

Direct interaction

Agent

Agent

Perceptions

Actions

Environment Avatar Immersed User

- Resources, services, objects - Rules, laws - Physical structures (spatial and topological) - Communication structures (stigmergy, implicit communication) - Social structure Change events

Rendering Software Modules (1D, 2D or 3D) Observer

Introduction


Physic Environment


Conclusion


Designing a Multiagent Simulation Model

Behaviors Internal Agent Architecture

Definitions of action, perception, and conflict resolution

Modeling of agent deliberation processes (agent mind)

60

Environment Physical objects of the world, their structuring, environment dynamics (evaporation...)

implies manages

implies

Scheduling Temporal dynamic of the system

Interaction manages

Modeling the results of actions and interactions at a given time [Michel, 2004]

Modeling of the time progress, and of the agent scheduling

Modeling the concurrent events

Introduction


Physic Environment


Conclusion

Three-Layer Architecture

61

Strategic Layer: general planning stage that includes the determination of goals, the route and the modal choice as well as a cost-risk evaluation.

Tactic Layer

Operational Layer perceived objects

Body

Operational Layer: Fundamental body controlling processes such as controlling speed, following the path, etc. Introduction


Physic Environment


ENVIRONMENT

Conclusion

[Michon, 1985, Hoogendoorn, 2001, Hoogendoorn, 2004]

Tactic Layer: Maneuvering level behavior. Examples: obstacle avoidance, gap acceptance, turning, and overtaking.

Strategic Layer

Subsomption Agent Architecture

62

Priority-ordering sequence of condition-action pairs.

condition1

action1

condition2

action2

conditionn

actionn

output

if condition1 then action1 else if condition2 then action2 else ... end if end if

inputs

Generalization of the three-layer architecture: each pair is a layer.

[Brooks, 1990]

∅

Introduction


Physic Environment


Conclusion

(Social) Force Models

63

Repulsive forces are computed and summed for obtaining the safer direction. May contribute to the too lower layers of the three-layer architecture. n X αi .p\ − ai i=1

+ β.

−−→ |− p − ai |

a2

−P −−−−−−−→ n i=1 ai

n

−p

a2

a1

a2

a1

p

a1

p

a3

+

separation

Introduction

!


p

+

a3

alignment

Physic Environment


Conclusion

a3

cohesion

[Helbing, 1997, Reynolds, 1999, Buisson, 2013]

~f =

Belief, Desire, Intention (BDI) Architecture

64

Beliefs: the informational state of the agent, in other words its beliefs about the world (including itself and other agents). Goals: a desire that has been adopted for active pursuit by the agent. Intentions: the deliberative state of the agent – what the agent has chosen to do. Plans: sequences of actions for achieving one or more of its intentions. Events: triggers for reactive activity by the agent. BDI may contribute to the too upper layers of the three-layer architecture.

Control flow

Belief recognition

Desires

Generate options

Intentions

Filtering

Action selection Outputs

Introduction


Physic Environment


Conclusion

[Rao, 1995]

Beliefs

Inputs

State-Transition Diagrams

65

Define the behavior in terms of states (of the agent knowledge) and transitions. Actions may be trigerred on transitions or states. Markov models [Baum, 1966] or activity diagrams [Rumbaugh, 1999] may be used in place of state-transition diagrams. agent A { v a r state = State : : NO_TRASH on Perception [ o c c u r r e n c e . contains ( Trash ) ] { state = State : : TRASH_IN_FOV }

[Harel, 1987]

on Perception [ ! o c c u r r e n c e . contains ( Trash ) ] { randomMove ( ) } ... }

Introduction


Physic Environment


Conclusion

Outline

66

1 Introduction to Multiagent Systems 2 Multiagent Simulation 3 Simulation with a Physic Environment General Principles Example 1: Traffic Simulation Example 2: Pedestrian Simulation 4 Cyber-physical System

Introduction


Physic Environment


Conclusion

Situated Environment Model

Agent

Agent

Direct interaction

Behavioral component Agent mind

Environment Interface

Environment Dynamics Engine

Influence

Environment - Resources - Physical structures - Physical rules

67

Physical component Agent body Influence Influence Solver ensure valid environment state according to environment laws

Perception Perception data structure spatial tree, grid, graph

[Galland, 2009]

Environmental Object Collection Environment's State

Introduction


Physic Environment


Conclusion

Body-Mind Distinction

68

Agent Agent mind

State variables of the decisional component

Agent memory

Action

Filtered perception

Behavior

Readable/modifiable only by the agent

Environment Interface Agent body Action filter

State variables of the physical component Readable by the agent Modifiable by the environment

[Galland, 2009]

Influence

Perception

Perception filter


Introduction


Physic Environment


Conclusion

Simultaneous Actions: Influence-Reaction

69

How to support simultaneous actions from agents? 1 An agent does not change the state of the environment directly. 2 Agent gives a state-change expectation to the environment: the influence. 3 Environment gathers influences, and solves conflicts among them for

obtaining its reaction. 4 Environment applies reaction for changing its state.

MICRO LEVEL

MACRO LEVEL

Agent 1

Agent 2

δ(t)

Reaction

δ(t+dt)

...

t

Introduction


Phase 1: INFLUENCE

Physic Environment


Phase 1: REACTION

Conclusion

t+dt

[Michel, 2007]

Agent n

Physic Environment in SARL

70

The agent has the capacity to use its body. The body supports the interactions with the environment. e v e n t Perception { v a l object : Object v a l r e l a t ivePosition : Vector } c a p a c i t y EnvironmentInteraction { moveTheBody ( motion : Vector ) move ( object : Object , motion : Vector ) e xe cu te A ctionOn ( object : Object , actionName : String , parameters : Object * ) }

s k i l l PhysicBody i m p l e m e n t s EnvironmentInteraction { v a l env : Ph ysi c Env iro nme nt v a l body : Object d e f moveTheBody ( motion : Vector ) { move ( t h i s . body , motion ) } d e f move ( object : Object , motion : Vector ) { env . move ( object , motion ) } }

Introduction


Physic Environment


Conclusion

[Galland, 2015]

Event that contains the information extracted from the physic environment

s p a c e P h y s i cEn viro nme nt { perceived by the agent. d e f move ( object : Object ,and motion : Vector ) { //... } }


70



s p a c e P h y s i cEn viro nme nt { that enables the agent to d e f move ( object : Object , motion Capacity : Vector ) { change the state of its body and to //... act into the physic environment. } }

Introduction


Physic Environment


Conclusion


70


s k i l l PhysicBody i m p l e m e n t s EnvironmentInteraction { v a l env : Ph ysi c Env iro nme nt v a l body : Object d e f moveTheBody ( motion : Vector ) { move ( t h i s . body , motion ) } d e f move ( object : Object , motion : Vector ) { env . move ( object , motion ) } Define the interaction space that is }

supporting the physic environment, with actions callable from skills.

s p a c e P h y s i cEn viro nme nt { d e f move ( object : Object , motion : Vector ) { //... } }

Introduction


Physic Environment


Conclusion


70



Provide an implementation ofi cEn theviro capacity space Phys nme ntthat { is binded the physical environment. d e f moveto( object : Object , motion : Vector ) { //... } }

Introduction


Physic Environment


Conclusion

Driving Activity

71

Each vehicle is simulated but road signs are skipped ⇒ mesoscopic simulation. The roads are extracted from a Geographical Information Database. The simulation model is composed of two parts [Galland, 2009]: the environment: the model of the road network, and the vehicles. 2 the driver model: the behavior of the driver linked to a single vehicle. 1

Introduction


Physic Environment


Conclusion

Model of the Environment

72

Road Network Road polylines: S = hpath, objectsi path = h(x0 , y0 ) · · · i Graph: G = {S, S 7→ S, S 7→ S} = {segments, entering, exiting} Operations Compute the set of objects perceived by a driver (vehicles, roads...):   distance(d, o) ≤ ∆∧   o ∈ O∧ P = o   ∀(s1 , s2 ), path = s1 .hp, Oi.s2

Introduction


Physic Environment


Conclusion

[Galland, 2009]

where path is the roads followed by a driver d. Move the vehicles, and avoid physical collisions.

Architecture of the Driver Agent

Path planning path to follow

Collision avoidance perceived objects

instant acceleration

Car new position

ENVIRONMENT Jasim model [Galland, 2009]

Introduction


Physic Environment


Conclusion

73

Path Planning

74

Based on the A* algorithm [Dechter, 1985, Delling, 2009]: extension of the Dijkstra’s algorithm: search shortest paths between the nodes of a graph. introduce the heuristic function h to explore first the nodes that permits to converge to the target node.

Inspired by the D*-Lite algorithm [Koenig, 2005]: A* family. supports dynamic changes in the graph topology and the values of the edges.

Introduction


Physic Environment


Conclusion

Collision Avoidance

75

Principle: compute the acceleration of the vehicle to avoid collisions with the other vehicles. Intelligent Driver Model [Treiber, 2000]  (v ∆v )2   − 4b∆p 2 followerDriving =  (s + vw )2  −a ∆p 2

if the ahead object is far if the ahead object is near

Free driving: freeDriving = a 1 −

Introduction


Physic Environment


Conclusion

v vc

4 !

Highway Simulation

76

What is simulated? 1 Vehicles on a French highway. Danger event → “an animal is crossing the highway and causes a crash”.

3

Alert events by GSM.

4

Arrival of the security and rescue services.

http://www.voxelia.com

2

Introduction


Physic Environment


Conclusion

Video

Video done with the SIMULATE tool — 2012  Voxelia S.A.S

Introduction


Physic Environment


Conclusion


77

Pedestrian Simulation

78

What is simulated? 1 Movements of pedestrians at a microscopic level. 2

Force-based model for avoiding collisions.


[Buisson, 2013]

Introduction


Physic Environment


Conclusion

Force to Apply to Each Agent

79

The force to apply to each agent is: ~ + wa .δ ~ p~t − pa F~a = F kF k kp ~t − pa k X ~ = F U(tci ) · Sî

Sˆ1 Pa

i∈M

Pt

~ : collision-avoidance force. F Sî : a sliding force. tci :

Sˆ2

time to collision to object i.

M: set objects around (including the other agents).

Sˆ2

wa : weight of the attractive force. δx g : is g if x ≤ 0, 0 otherwise. Introduction


Physic Environment


Conclusion

[Buisson, 2013]

U(t): scaling function of the time to collision.

Sliding Force

80

The sliding force S~i is: s~j = (pj − pa ) × yˆ s~j Sˆj = sign(~ sj · (p~t − pa )) k~ sj k yˆ : vertical unit vector.

−Sˆj pt

pj Pa Sˆj

Introduction


Physic Environment


Conclusion

Scaling the Sliding Force

81

How to scale Sˆj to obtain the repulsive force? Many force-based models use a monotonic decreasing function of the distance to an obstacle. But it does not support the velocity of the agent. Solution: Use time-based force scaling function. ( σ σ if 0 ≤ t ≤ tmax φ φ − t max U(t) = t 0 if t > tmax t: estimated time to collision. tmax : the maximum anticipation time. σ and φ are constants, such that U(tmax ) = 0.

Introduction


Physic Environment


Conclusion

Videos

Video done with the SIMULATE tool — 2014  Voxelia S.A.S

Introduction


Physic Environment


Conclusion


82

Outline

83

1 Introduction to Multiagent Systems 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Cyber-physical System Definition Intelligent Vehicle

Introduction


Physic Environment


Conclusion

What is a Cyber-physical System?

Definition [Cyb, 2008] A cyber-physical system is a system where computer components work together to monitor and control physical entities.

Each computer component is an agent, or a multiagent system. Perceptions from the physical sensors. Interactions for controlling the physical entity. Actions through the physical effectors.

Introduction


Physic Environment


Conclusion

84

Intelligent Vehicle

85

Intelligent Vehicle perceiving its environment: video, laser and GPS sensors, driving by itself, or taking control in urgency cases. Goals 1 Simulate the driver behavior. 2

Simulate the environment and the sensors in a virtual lab.

3

Deploy the driver software in the real vehicles without change.

Introduction


Physic Environment


Conclusion

Intelligent Vehicle

85



3


Introduction


Physic Environment


Conclusion

Intelligent Vehicle

85



3


Introduction


Physic Environment


Conclusion

Holonic Architecture of the System

Vehicle 1

86

Vehicle 2

Pedestrians

Control

Control

Sensors

Introduction


Physic Environment


Conclusion

Sensors

Video

87

[Gechter, 2012]

Introduction


Physic Environment


Conclusion

Conclusion: Typical On-going Issues

Introduction

1

Modeling of complex systems: ⇒ How to describe the interactions between agents (social, physic, etc.)? ⇒ How to model the environment? ⇒ How to manage the system ⇔ agent relationship?

2

Modeling of large-scale systems: ⇒ How to model the individuals of a large population at a microscopic level? ⇒ How to manage computational cost?

3

Initializing the properties of the agents and the environment: ⇒ How collecting data from the real world?


Physic Environment


Conclusion

88

Thank you for your attention. . .


Agent Environment: Definition and Examples St´ ephane GALLAND


Goals of this Lecture

2

During this lecture, I will present:

MAS

1

the concept of Environment;

2

the concept of Agent Environment;

3

the Coordination Artifacts and Smart Objects;

4

a library for a 2D discrete environment;

5

a model for a 3D urban environment;

6

a multidimensional environment model.

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.

Multidimensional Env.

Outline

3

1 Remainders of Multiagent Systems 2 From Environment to Agent Environment 3 Coordination Artifacts 4 Smart Objects 5 Agent Body 6 Jaak Library: simulation with 2D discrete environment 7 3D Urban Environment 8 Simulated Multidimentional Environment MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Outline

4


Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Agent

An agent is an entity with (at least) the following attributes/characteristics: Autonomy: Agents encapsulate their internal state, and make decisions about what to do based on this state, without the direct intervention of humans or others. Reactivity: Agents are situated in an environment, and are able to perceive this environment and respond in a timely fashion to changes that occur in it. Pro-activity: Agent do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking the initiative. Social Skills: Agents interact with other agents, and have the ability to engage in social activities in order to achieve their goals. MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


[Wooldridge, 2001]

5

Agent Typology

6

Reactive: Each agent has a mechanism of reaction to events, without having an explanation/understanding of the objectives nor planning mechanisms. Typical Example: The ant colony. Cognitive/Deliberative: Each agent has a knowledge base that contains all information and skills necessary for the accomplishment of their tasks/goals and the management of his interactions with other agents and his environment: reasoning, planning, normative. Typical Example: Multi-Expert Systems.

Reactive Agent

MAS

Environment

Conclusion

Artifacts

Smart Objects

Hybrid Agent

Bodies

2D Env.

3D Env.

Deliberative/Cognitive Agent


Multiagent systems

7

Multiagent systems An MultiAgent Systems (MAS) is a system composed of agents that interact together and through their environment.

Interactions: → Direct, agent to agent → Indirect, Stigmergy, through the Environment

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Stigmergy

8

Introduced by the French Biologist Pierre-Paul Grassé: Stigmergy [Grassé, 1959] Stimulation of workers by the work they perform. Stigmergy in multiagent systems [Parunak, 2003] Actions by an agent put signs on the environment. These signs are perceived by the all agents. Agents determine their next actions accordingly. → Direct coordination is too costly in time and resources. → Enable auto-organization of complex systems without plan, control nor direct interaction. MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Agents and Environment

9

An agent: Agent

is located in an environment (situatedness)

Agent mind

Filtered perception

perceives the environment through its sensors.

Agent memory

Action

Behavior


acts upon that environment through its effectors.

Agent body

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

Perception

tends to maximize progress towards its goals by acting in the environment.

3D Env.




Perception filter

Outline

10

1 Remainders of Multiagent Systems 2 From Environment to Agent Environment Definition and Properties of an Environment Missions of the Environment Dimensions of the Environment 3 Coordination Artifacts 4 Smart Objects 5 Agent Body 6 Jaak Library: simulation with 2D discrete environment 7 3D Urban Environment MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Environment

11

What is the Environment? First-class abstraction of a part of the system that contains all non-agent elements of a multiagent system. It provides: a) the surrounding conditions for agents to exist. b) an exploitable design abstraction to build MAS applications.

Key Ideas It is omnipresent. Manages access to resources and structures.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Environment: accessible vs. inaccessible

Fully observable (accessible) vs. partially observable (inaccessible) Fully observable if agent’s sensors detect all aspects of environment relevant to choice of action. Could be partially observable due to noisy, inaccurate or missing sensors, or inability to measure everything that is needed. Model can keep track of what was sensed previously, cannot be sensed now, but is probably still true.

Often, if other agents are involved, their intentions are not observable, but their actions are. Chess: the board is fully observable, as are opponent’s moves. Driving: what is around the next bend is not observable.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


12

Environment: deterministic vs. stochastic

Deterministic: the next state of the environment is completely predictable from the current state and the action executed by the agent. Stochastic: the next state has some uncertainty associated with it. Uncertainty could come from randomness, lack of a good environment model, or lack of complete sensor coverage.

Strategic environment: if the environment is deterministic except for the actions of other agents.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


13

Environment: episodic vs. sequential

The agent’s experience is divided into atomic “episodes” (each episode consists of the agent perceiving and then performing a single action). The choice of action in each episode depends only on the episode itself. Examples of episodic are expert advice systems — an episode is a single question and answer.

Sequential if current decisions affect future decisions, or rely on previous ones. Most environments (and agents) are sequential. Many are both — a number of episodes containing a number of sequential steps to a conclusion.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


14

Environment: discrete vs. continuous

Discrete: time moves in fixed steps, usually with one measurement per step (and perhaps one action, but could be no action). Example a game of chess

Continuous: Signals constantly coming into sensors, actions continually changing. Example driving a car, a pedestrian moving around

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


15

Environment: static vs. dynamic

Dynamic: if the environment may change over time. Static: if nothing (other than the agent) in the environment changes. Other agents in an environment make it dynamic. The goal might also change over time.

Examples Playing football, other players make it dynamic. Mowing a lawn is static (unless there is a cat). Expert systems usually static (unless knowledge changes).

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


16

Missions of the Environment

M1 - Sharing informations: Agent environment is a shared structure for agents, where each of them perceives and acts. M2 - Managing agents actions and interactions: It is related to the management of agents’ simultaneous and joint actions and to the preservation of the environmental integrity: influence-reaction model [Ferber, 1996, Michel, 2004, Galland, 2009]. M3 - Managing perception and observation: Agents can manage the access to environmental informations and guarantee the partialness and localness of perceptions. M4 - Maintaining endogenous dynamics: The environment is an active entity; it can have its own processes, independently of the ones of the agents.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


[Weyns, 2005]

17


17


MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



17


MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



17


MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Environment Layers

Middleware / Virtual Machine Operating System Physical Infrastructure (Network, Computers, etc.) Real World

MAS

Environment

Conclusion

Artifacts

Execution Platform

Agent Framework (Janus, etc.)

Smart Objects

Bodies

2D Env.

3D Env.


Physical Infrastructure

ENVIRONMENT

Agent Environment

Mix of the archectures given by [Weyns, 2005, Viroli, 2007]

Agents

MAS Application

18

Environment Layers

Agents

Middleware / Virtual Machine Operating System Physical Infrastructure (Network, Computers, etc.) Real World

MAS

Environment

Conclusion

Artifacts

Execution Platform

Agent Framework (Janus, etc.)

Smart Objects

Bodies

2D Env.

3D Env.


Physical Infrastructure

ENVIRONMENT

Agent Environment

MAS Application

18

Agent Framework

19

Requirements Provides the features for running agents. Must support parallel execution of the agents. The Janus Agent Framework Fully distributed. Dynamic discovery of Kernels. Automatic synchronization of kernels’ data (easy recovery). Micro-Kernel implementation. Official website: http://www.janusproject.io Other frameworks may be created. See Seminar #4.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Agent Environment

20

Definition The agent environment (or application environment) embeds: the environmental logic of the application, and the interface to the agents. Content of the Agent Environment Resources in the agent environment are passive and reactive. They are not agents. May support a specific dimension of the environment [Odell, 2003]: a) Physical, and b) Communicational / Social.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Physical Dimension of the Environment

Physic Environment Class of real or simulated systems in which agents and objects have an explicit position, and that produce localized actions. Properties Contains all objects. Agents interact with it via dedicated model. Agents’ bodies are “managed” by the environment. Multiple “Views” of the environment can be implemented (1D, 2D, 3D). Enforces Universal Laws (e.g. Laws of physics). Should be a singleton.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


21

Communicational / Social Dimension of the Environment

Multiple ways of agent interaction: event, messages, etc. Multiple models of agent relationship: authority, auction, contract-net-protocol, etc. Social dimension could influence other dimensions [Galland, 2015]. Communicational Dimension in SARL Supported by Space in SARL. Default Interaction Space: based on events (may be redefined). Programmer can create new Space specifications (and ways of interacting):

FIPA, Organizational (MOISE, CRIO, etc).

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


22

Outline

23


Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Notion of Artifact

24

[Norman, 1991] “Artifacts play a critical role in almost all human activitiy[...]. Indeed [...] the development of artifacts, their use, and then the propagation of knowledge and skills of the artifacts to subsequent generations of humans are among the distinctive characteristics of human beings as a specie.” [Amant, 2005] “The use of tools is a hallmark in intelligent behaviour. It would be hard to describe modern human life without mentioning tools of one sort or another.”

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


The Role of Artifacts in Human Society

Artifacts and tools are essential elements for mediating and supporting human activities. Especially for social activities. First characterization of the artifact notion: Tool Perspective: artifacts as a kind of device explicitly designed to embed some kind of functionality, that can be properly used by humans to do their work tool perspective. Activity Target Perspective: artifacts as objectification of human goals and tasks.

Basic bricks of human environment.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


25

Artifact is a Pervasive Notion Psychology & human sciences Activity Theory. Artifacts as mediators of any (complex) activity. Cognitive science Distributed Cognition. Cognition spread among humans and the environment where they are situated. Computer-Supported Cooperative Work Coordinative Artifacts. Artifacts as bricks of human fields of works. Distributed Artifical Intelligence The role of environment for supporting intelligence. “Lifeworld Analysis” [Agre, 1997] MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


26

From Human Society to Agent Societies

If it is such a fundamental concept for human society and human working environments, could it be useful also in agent societies and MAS for conceiving agent “working environments?” [Ricci, 2005] Analogy between human and agent societies: agent societies / MAS conceived as systems of autonomous cognitive entities working together within some kind of social activities, situated within some kind of working environment, etc.

A&A conceptual framework: defining a notion of artifact and working environment within MAS. foundational & engineering aims: finally useful for building MAS and agent -based systems.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


27

A&A Objectives

1

28

Abstraction and Generality: defining a basic set of abstractions & related theory general enough for designing & engineering general-purpose working environments. engineering principles: encapsulaio, reuse, extendibility, etc.

effective/expressive enough to capture the essential properties of specific domains.

2

Cognition: the properties of such environment abstractions should be conceived to be suitably and effectively exploited by cognitive agents. agents as intelligent constructors, users, manipulators of such environment

analogy with human society.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


A&A Elements

29

MAS: set of agents working together in the same working environment. Working environment: set of artifacts, collected in workspaces.

ARTIFACTS

AGENTS

Agents dynamically create, share and use artifacts to support their social/ individual activities (mediation role of artifacts). MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.

WORKSPACE


Artifact Abstraction

30

Building blocks of working environments Different kinds / types of artifacts, with possibly multiple instances for each type. similar to objects but in an agent world

Representing any kind of resource or tool that agents can dynamically create, share, use, manipulate. Passive, dynamic, function-oriented entities Designed to encapsulate some kind of function: the intended purpose of the artifact vs. agent as goal/task-oriented entities

Typically stateful object: result of an action depends on the state history. MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Interaction from Agents to Artifacts

Agents-Artifacts Interaction Based on a notion of use. Agents use artifacts so as to exploit their functionalities vs. communication-based interaction. agents do not communicate with artifacts

Artifact Usage Interface Set of operations that agent can trigger on the artifact. It can change depending on the working state of the artifact.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


31

Interaction from Artifacts to Agents

Artifact Observable State and Events Artifacts as sources of observable events that can be perceived by agents using or observing them. Manual of an Artifact Equipping each artifact with the formal description of its functionality and usage modalities: a) functional description: why to use it. b) operating instructions: how to use it.

Enabling a cognitive use of artifacts with dynamic discovery, selection and learning of artifacts.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


32

Outline

33


Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


On the Notion of Smart Objects Smart Objects “When almost every object either contains a computer or can have a tab attached to it, obtaining information will be trivial.” [Weiser, 1991] Objects are smart in the sense that they act as digital information sources. Tangible Objects “Tangibles bits or tangible user interfaces that enable users to grasp & manipulate bits in the center of users’ attention by coupling the bits with everyday physical objects and architectural surfaces.” [Ishii, 1997] Objects could react to user interactions.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


34

Smart Object

35

[Kallmann, 1998] An object that can describe its own possible interactions.

Focus on Smart Object interaction with Humans and Agents in virtual worlds. These interaction information enables more general interaction schemes, and can greatly simplify the action planning of an agent.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Elements of a Smart Virtual Object

1

Object properties: physical properties and a text description.

2

Interaction information: position of handles, buttons, grips, and the like.

3

Object behavior: different behaviors based on state variables.

4

Agent behaviors: description of the behavior an agent should follow when using the object]

Example Include 3D animation information in the object information, but this is not considered to be an efficient approach [Jorissen, 2005].

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


36

Smart Physical Objects

37

[Poslad, 2009] A smart physical object: is active, is digital, is networked, can operate to some extent autonomously, is reconfigurable, and has local control of the resources it needs such as energy, data storage, etc. ⇒ may be seen has a particular type of agents.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Outline

38


Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Agent Body

39

[Saunier, 2015] The agent body is a component of the multiagent system working as an interface between mind (agent) and environment. It is embedded in the environment to enforce body rules and ecological laws, but is influenced by -and allows introspection for- the mind. An agent may have one or more bodies in the environment(s) it participates in. Body Responsabilities Representation of the agent: It is the observable part of the agent in an environment dimension (physic of social). Perception mediation: It defines the perception capabilities of the agent. Action mediation: It defines the action capabilities of the agent. Environment Component: It respects the laws of the agent environment.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Body-Mind Distinction

40

Agent Agent mind

State variables of the decisional component

Agent memory

Action

Filtered perception

Behavior

Readable/modifiable only by the agent


MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies


Perception

Perception filter


2D Env.

3D Env.


State variables of the physical component Readable by the agent Modifiable by the environment

[Saunier, 2015, Galland, 2009]

Agent body

Outline 1 Remainders of Multiagent Systems 2 From Environment to Agent Environment 3 Coordination Artifacts 4 Smart Objects 5 Agent Body 6 Jaak Library: simulation with 2D discrete environment Overview Ant Colony Simulation 7 3D Urban Environment MAS

Artifacts Smart Objects Bodies 2D Env.Environment 3D Env. Multidimensional Env. 8 Simulated Multidimentional

Environment

Conclusion

41

Jaak Simulation Library

42

What is Jaak? Reactive agent execution framework (but may be cognitive with additional libraries). Provides a discrete 2D environment model. Provides a simplified agent-environment model based on LOGO-like primitives: move forward, turn left, etc.

https://github.com/gallandarakhneorg/jaak MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


From LOGO to Jaak

43

LOGO Reflexive and functional language from the MIT.

programming

Mainly known for its famous graphical turtle. Turtles move with simple instructions: move forward, turn 45 degrees left, etc. Jaak Turtles are situated agents, which is able to move on a 2D environment. Turtles are written in SARL. A specific capacity provides the simple instructions for moves. MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Architecture of a Jaak Application SARL

44

Turtle

Turtle

Turtle

Turtle

Turtle Body

Turtle Body

Turtle Body

Turtle Body

Environment Model

Java Jaak Kernel

Janus Platform

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



Turtle

Turtle

44

Turtle

Simulated agent that exhibits a behavior. Turtle Body Turtle Body Turtle Body

Turtle

Turtle Body

It is located on a situated environment. It interact with other turtles, perceive around in the environment, and act inside this environment.

Environment Model

Java Jaak Kernel

Janus Platform

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



44

Turtle

Turtle

Turtle

Turtle

Turtle Body

Turtle Body

Turtle Body

Turtle Body

Environment Model

Java Software entity that contains objects and substances that are Jaak not agents. Kernel It is governed by a set of internal rules that cannot be broken by agents.

Janus Platform

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



44

Turtle

Turtle

Turtle

Turtle

Turtle Body

Turtle Body

Turtle Body

Turtle Body

Object inside the environment and controlled by a Environment Model turtle. It is the bridge and the link between the turtle and the environment model. The body cannot break the environment's rules. It gives the perceptions to its turtle and all turtle's actions pass throughJaak it. Kernel

Java

Janus Platform

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Objects and Substances in the Environment Environment Objects Environment objects are all the entities inside the environment. Turtles are not, and cannot be environmental objects. But turtle’s bodies are special types of environmental objects. Each body is associated to one turtle agent, and contains the physical description of that agent: field of vision, maximal speed, weight, etc. Substances Substance is a special type of environmental object: It is a particular type of liquid, solid, or gas located in the environment. It is countable and may be divided or expanded.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


45

Class Diagram of the Environment Objects

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


46

Structure of the Environment The environment structure is based on a 2D grid (matrix of cells). Properties of the cells A cell contains:

At most one agent body (see burrow for exception), or at most one obstacle; and many other environmental objects. Cell’s “graphical” size is application-dependent. Properties of the grid Size is fixed at start-up. Grid could be wrapped: if an agent is located on one side of the grid and is trying to move outside; it will be moved at the opposite side of the grid.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


47

Life Cycle of a Turtle Agent

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.

48


What is a Perception?

49

Definition An environmental object, or a collection of environmental objects, that are inside, or intersecting, the field of perception of an agent. Perception is computed and given by the sensors of the agent’s body. Hypotheses 1 Agent cannot be omniscient: the scope of its perception is restricted to a limited portion of the environment.

MAS

2

Perception is for a given time.

3

Perception’s list may be influenced or changed according to the properties of the sensors, e.g. visual impaired agent.

4

An agent can perceive even if it is inside a burrow.

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


API for the Perception

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

50

3D Env.



50

All objects that could be perceived by an agent implement Perceivable. List of perceptions = collection of perceivable objects.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



50

Turtles can request to pick an object up. After the object was picked up, the agent perceived the picked object. The picked object contains the amount of substance that was picked up.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



50

Envelope object for a turtle body inside the field-of-view. Why not a reference to a turtle body nor a turtle? Avoiding direct reference from an agent to the other, and from an agent to an environmental object.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



50

- Frustum = Geometrical definition of the field of view. - A body owns at least one TurtleFrustum. - Used for selected the Perceivable objects that should be inside the perception list.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Life Cycle of a Turtle Agent

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.

51


What is an Influence?

52

Problem 1: simultaneous actions Actions decided by different agents that may be applied at the same time on the same action space. Simultaneous actions may be under conflict. Example: when two agents try to move on the same cell. Problem 2: uncertain actions An action decided by an agent may be skipped or partially applied according to the internal rules of the environment. Example: an agent cannot go inside the same cell of an obstacle.

Actions from agents are not directly applied in the environment. Conflicts among actions are detected and solved. The resolution result is applied in the environment. Influence: the expected action by the agent. MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


[Ferber, 1996, Michel, 2007]

Solution: influence


52



Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



Solution: influence


52



Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



Solution: influence

API for the Influence

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

53

3D Env.



53

- Request to move the agent body. - Linear motion: change of cell. - Angular motion: turn the agent's head

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



53

Request to pick an object up from the ground.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



53

Request to drop an object down on the ground.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Ant Colony Simulation

54

What is a Ant? Simple insects with no global knowledge and limited memory. Capable of performing simple actions: move get food from the cell sense pheromon is neightbor cells put pheromon into the current cell

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


https://github.com/gallandarakhneorg/jaak

Problem Simulating the global behavior of a colony of ants for finding the shortest routes from the nest to a food source.

Work Steps

MAS

Environment

Conclusion

Artifacts

Smart Objects

Environmental Objects

Environmental Substances

Turtle Agents

Environmental Processes

Turtle Spawners

System Initialization

Bodies

2D Env.

3D Env.



55

Definition of the Environmental Objects

General Principle For each type of object inside the environment, a subclass of EnvironmentalObject must be defined.

56



Turtle Agents


Turtle Spawners


In the ant colony problem


Nest: location where many ants may be at the same place. It is a kind of Burrow to enable this behavior. c l a s s Nest e x t e n d s Burrow { v a l colonyId : i n t new ( colonyId : i n t ) { t h i s . colonyId = colonyId } d e f getColonyId : i n t { t h i s . colonyId } } MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.


Definition of the Environmental Substances

General Principle For each substance (countable/measurable) inside the environment, a subclass of Substance must be defined.

57



Turtle Agents


Turtle Spawners


In the ant colony problem

FoodPheromone: put when the ant is going far and far from a food source. Food: a source of food.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



ColonyPheromone: put when the ant is going far and far from the ant colony.

Definition of the Environmental Substances (cont.)

Definition of a food source Contains the quantity of available food.

58



Turtle Agents


Turtle Spawners


Quantity cannot increase.

d e f decrement ( s : Substance ) : Substance { v a r oldValue = floatValue ( ) decrement ( s . floatValue ) v a r c = clone c . value = abs ( floatValue ( ) − oldValue ) return c } d e f increment ( s : Substance ) : Substance { // The food source could not be increased null } } MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



c l a s s Food e x t e n d s FloatSubstance { new ( foodQuantity : f l o a t ) { s u p e r ( foodQuantity , t y p e o f ( Food ) ) ; }

Definition of the Environmental Processes

General Principle Endogenous environmental activities are the processes that produce an evolution of the environment state outside the control of any agent.

59



Turtle Agents


Turtle Spawners


a) a autonomous endogenous process associated to an object b) the global endogenous engine In the ant colony problem Pheromones are substances that are evaporating over the time.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



Two types of endogenous environmental activities:

Definition of the Environmental Processes (cont.)

Update of the definition of a pheromone Implements the AutonomousEndogenousProcess interface. c l a s s Pheromone e x t e n d s Substance implements AutonomousEndogenousProcess {

i f ( floatValue ( ) 0 ) } d e f computeSpawnedTurtleOrientation ( timeManager : TimeManager ) : f l o a t { RandomNumber : : nextFloat ( ) * 2 * Math : : PI ; } d e f turtleSpawned ( turtle : UUID , body : TurtleBody , timeManager : TimeManager ) { t h i s . budget = t h i s . budget − 1 body . semantic = t y p e o f ( Forager . c l a s s ) } } MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



Maximal number of ants to generate.

Definition of the System Initialization

General Principle Setting up the system by defining initialization functions. In the ant colony problem

66



Turtle Agents


Turtle Spawners


Provide the spawner-agent mapping that is used when agents are manually created.

MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



Extend the Jaak kernel agent with initialization of the nests, spawners.

Definition of the System Initialization (cont.)

Initializing the system Create subtype of JaakKernelAgent.

67



Turtle Agents


Turtle Spawners


Create the environment grid. Create the spawners on the ground. Provide the spawner-agent mapping for manually created agents.

a g e n t A n t C o lonyProblem e x t e n d s JaakKernelAgent { d e f c r e a t eEnv iro nme nt ( tm : TimeManager ) : JaakEnvironment { v a r environment = new JaakEnvironment ( WIDTH , HEIGHT ) environment . timeManager = tm v a r actionApplier = environment . actionApplier ; for (i : 0..99) { actionApplier . putObject ( random , random , new Food ( 5 0 ) ) } r e t u r n environment } } MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



Initialize the UI and start the simulation.



68



Turtle Agents


Turtle Spawners



a g e n t A n t C o lonyProblem e x t e n d s JaakKernelAgent { ... d e f creat eSpawners : JaakSpawner [ ] { v a r spawners = newArrayOfSize ( A N T _ C O LONY_COUNT ) f o r ( i : 0 . . spawners . length ) { spawners . set ( i , createColony ( i+1) ) } r e t u r n spawners ; } d e f createColony ( colonyId : i n t ) : JaakSpawner { ... } } MAS

Environment

Conclusion

Artifacts

Smart Objects

Bodies

2D Env.

3D Env.



Initialize the UI and start the simulation.



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Turtle Agents


Turtle Spawners



a g e n t A n t C o lonyProblem e x t e n d s JaakKernelAgent { ... d e f g e t S p a w n a b l e A g e n t T y p e ( spawner : JaakSpawner ) : Class