[PPT] - Multiagent Systems and Agent-based Simulation From theory to PowerPoint Presentation

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Multiagent Systems and Agent-based Simulation

From theory to transport application

Prof. dr. St´

ephane GALLAND

MTS 2015 Summer School – Hasselt, Belgium

IRTES-SeT, UTBM 90010 Belfort cedex, France stephane.galland@utbm.fr – http://www.multiagent.fr

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

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 a model of social organizational environment,

Example: Carpooling simulation;

4 a model of physic environment,

Examples: highway and pedestrian simulation;

5 a model of based on recursive agents, the holons,

Examples: virtual-enterprise simulation;

6 a cyber-physic model,

Examples: intelligent autonomous vehicle.

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

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1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

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1 SARL Agent Programming Language Agent Technology - (really!) Brief Overview Programming Multiagent System in SARL Environment in Multiagent Systems 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

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1 SARL Agent Programming Language Agent Technology - (really!) Brief Overview Programming Multiagent System in SARL Environment in Multiagent Systems 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

History of Computing

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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.

S. Galland

MTS 2015 – 14/07/2015

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Agent: a first Definition

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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.

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Agents and Environment

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An agent: is located in an environment (situatedness) perceives the environment through its sensors. acts upon that environment through its effectors. tends to maximize progress towards its goals by acting in the environment.

Agent

Environment Interface Agent body

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

Agent mind

Agent memory Behavior Action filter Perception filter Filtered perception Action Influence Perception

S. Galland

MTS 2015 – 14/07/2015

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Programming progression

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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.

assembler procedural

bject

actor agent level of abstraction

S. Galland

MTS 2015 – 14/07/2015

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Multiagent-oriented approach

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Multiagent systems: a new view? Which characteristics?

Multiagent-based approach (metaphor or paradigm) is represents a new way of analyzing, designing and implementing software systems, especially complex systems It strongly improves/impacts the way in which people conceptualizes and implements a large number of systems. Strong interdisciplinary inspiration: social and biological sciences, Economics and Game theory, control theory. Large panel of applications

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

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1 SARL Agent Programming Language Agent Technology - (really!) Brief Overview Programming Multiagent System in SARL Environment in Multiagent Systems 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Overview of SARL Concepts

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Multiagent System in SARL

A collection of agents interacting together in a collection of shared distributed spaces.

4 main concepts Agent Capacity Skill Space 3 main dimensions Individual:: the Agent abstraction (Agent, Capacity, Skill) Collective:: the Interaction abstraction (Space, Event, etc.) 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]

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Agent

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Agent

An agent is an autonomous entity having some intrinsic skills to implement the capacities it exhibits. An agent initially owns native capacities called Built-in Capacities. An agent defines a Context.

Agent

Capacity1 Capacity2 Capacity3 Capacity4

Skill Container

Built-in Capacities

Space 1 Space 2

Addr1 Addr1 Addr2

Default Space

Behaviors Behavior 1 Behavior 2 Behavior n

Inner Context

agent HelloAgent { uses L i f e c y c l e , Schedules

n

I n i t i a l i z e { p r i n t l n (” H e l l o World ! ” ) i n (2000) [ k i l l M e ] }

n

Destroy { p r i n t l n (” Goodbye World ! ” ) } }

S. Galland

MTS 2015 – 14/07/2015

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Capacities and Skill

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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

f its specification, the capacity.
S. Galland

MTS 2015 – 14/07/2015

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Capacities and Skill

15 c a p a c i t y Logging { def debug ( s : S t r i n g ) def i n f o ( s : S t r i n g ) } s k i l l BasicConsoleLogging implements Logging { def debug ( s : S t r i n g ) { p r i n t l n (”DEBUG: ” + s ) } def i n f o ( s : S t r i n g ) { p r i n t l n (”INFO :” + s ) } } agent HelloAgent { uses L i f e c y c l e , Schedules , Logging

n

I n i t i a l i z e { s e t S k i l l ( Logging , new BasicConsoleLogging ) i n f o (” H e l l o World ! ” ) i n (2000) [ k i l l M e ] }

n

Destroy { i n f o (” Goodbye World ! ” ) } }

S. Galland

MTS 2015 – 14/07/2015

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Interactions between Agents

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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.

S. Galland

MTS 2015 – 14/07/2015

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Context and Interactions

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Context

Defines the boundary of a sub-system. Collection of Spaces. Every Context has a Default Space. Every Agent has a Default Context, the context where it was spawned. Context

Default space Space 1 Space 2 Space 3

S. Galland

MTS 2015 – 14/07/2015

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Interactions between Agents

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Default Space: an Event Space

Event-driven interaction space. Default Space of a context, contains all agents of the considered context. Event: the specification of some

ccurrence in a Space that may

potentially trigger effects by a participant.

Agent

Capacity1 Capacity2 Capacity3 Capacity4

Skill Container

Built-in Capacities

Space 1 Space 2

Addr1 Addr1 Addr2

Default Space

Behaviors Behavior 1 Behavior 2 Behavior n

Inner Context

Addr-2 Addr-1 Addr-3

Other agent

S. Galland

MTS 2015 – 14/07/2015

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Ping - Pong – Exchanging information between agents

19 event Ping { var v a l u e : I n t e g e r new ( v : I n t e g e r ) { v a l u e = v } } event Pong { var v a l u e : I n t e g e r new ( v : I n t e g e r ) { v a l u e = v } } agent PongAgent {

n

I n i t i a l i z e { p r i n t l n (” Waiting f o r ping ”) }

n

Ping { p r i n t l n (” Recv Ping : ”+o c c u r r e n c e . v a l u e ) p r i n t l n (” Send Pong : ”+o c c u r r e n c e . v a l u e ) emit ( new Pong ( o c c u r r e n c e . v a l u e ) ) } } agent PingAgent{ var count : I n t e g e r

n

I n i t i a l i z e { p r i n t l n (” S t a r t i n g PingAgent . . . ” ) count = 0 i n (2000) [ sendPing ] } def sendPing { i f ( d e f a u l t S p a c e . p a r t i c i p a n t s . s i z e >1) { emit ( new Ping ( count ) ) count = count + 1 } e l s e { i n (2000) [ sendPing ] } }

n Pong {

i n (1000) [ p r i n t l n (” Send Ping : ”+count ) emit ( new Ping ( count ) ) count = count + 1 ] } }

S. Galland

MTS 2015 – 14/07/2015

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Spaces and Contexts

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Contexts and Holonic properties

All agents have at least

ne context: the default
ne.

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

Holonic Perspective

All agents contains an internal context. Enable to build hierarchy

f agents: holarchy.

Default Space (Holonic group)

Level n Level n-1 InnerContext

Addr-1 Addr-2 Addr-3 Addr-6 Addr-7 Addr-5 Addr-4 Addr-8 Addr-9 Addr11 Addr10 Addr12 Addr13 Addr14 Addr15 Addr16 Addr17 Addr18

Other Space (Production group) Default Space Default Space

DefaultContext ExternalContext 1

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

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1 SARL Agent Programming Language Agent Technology - (really!) Brief Overview Programming Multiagent System in SARL Environment in Multiagent Systems 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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Missions of the Environment [Weyns, 2005]

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M1 - Sharing informations: 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

f the ones of the agents.
S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Missions of the Environment [Weyns, 2005]

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M1 - Sharing informations: 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

f the ones of the agents.
S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Missions of the Environment [Weyns, 2005]

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M1 - Sharing informations: 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

f the ones of the agents.
S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Missions of the Environment [Weyns, 2005]

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M1 - Sharing informations: 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

f the ones of the agents.
S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Environment on SARL

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Dimensions of the Environment

Execution Environment, Physic Environment, Social Environment.

Key Ideas

It is omnipresent. Manages access to resources and structures. Agents can interact with it via Capacities and Spaces.

S. Galland

MTS 2015 – 14/07/2015

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Execution Dimension

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Execution Environment requirements

Handles Agent’s Lifecycle Provides Built-in Capacities Implements SARL concepts Handles resources

Janus as SARL Execution Environment

Fully distributed. Dynamic discovery of Kernels. Automatic synchronization of kernels’ data (easy recovery). Micro-Kernel implementation. http://www.janusproject.io

S. Galland

MTS 2015 – 14/07/2015

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Physic Dimension

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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 Capacities 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)

S. Galland

MTS 2015 – 14/07/2015

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Social Dimension

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Multiple ways of agent interaction: event, messages, stigmergy... Multiple models of agent relationship: authority, auction, contract-net-protocol... Supported by Space / Space Specification. Default Interaction Space: based on events (may be redefined). Programmer can create new Space Specifications (and ways

f interacting):

FIPA, Organizational (MOISE, CRIO, etc).

Social dimension could influence other dimensions [Galland, 2015].

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

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1 SARL Agent Programming Language 2 Multiagent Simulation Simulation Fundamentals Multiagent Based Simulation (MABS) Overview of a MABS Architecture Agent Architectures 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

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1 SARL Agent Programming Language 2 Multiagent Simulation Simulation Fundamentals Multiagent Based Simulation (MABS) Overview of a MABS Architecture Agent Architectures 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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A Definition of Simulation

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[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.

S. Galland

MTS 2015 – 14/07/2015

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Simulation Basics

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Real System System Model

Abstraction/Simplification/Focus Explanation/Optimization/Prediction Confrontation Observations Experimental results Simulation results/outputs Tuning model Simulation

S. Galland

MTS 2015 – 14/07/2015

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Modeling Relation: System ↔ Model [Zeigler, 2000]

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Real System System Model Simulation Modeling Implementation Modeling Relation

Is the model valid?

Simulation Relation

Is the simulator valid?

To determine if the system model is an acceptable simplificiation in terms of quality criteria and experimentation

bjectives.

Directly related to the consistency of the model simulation.

S. Galland

MTS 2015 – 14/07/2015

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Simulation Relation: Model ↔ Simulator [Zeigler, 2000]

32

Real System System Model Simulation Modeling Implementation Modeling Relation

Is the model valid?

Simulation Relation

Is the simulator valid?

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.

S. Galland

MTS 2015 – 14/07/2015

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Classical Topology of the Simulation

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Topology according to the granularity of the simulation, the level of details that is possible in the model [Hoogendoorn, 2001, Davidsson, 2000]. Microscopic Simulation:

Explicitly attempts to model the behaviors of each individual. The system structure is viewed as emergent from the interactions between the individuals.

Macroscopic Simulation: Mesoscopic Simulation: Multilevel Simulation:

S. Galland

MTS 2015 – 14/07/2015

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Classical Topology of the Simulation

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Topology according to the granularity of the simulation, the level of details that is possible in the model [Hoogendoorn, 2001, Davidsson, 2000]. Microscopic Simulation: Macroscopic Simulation:

Based on mathematical models, where the characteristics of a population are averaged together. Simulate changes in these averaged characteristics for the whole population. The set of individuals is viewed as a structure that can be characterized by a number of variables.

Mesoscopic Simulation: Multilevel Simulation:

S. Galland

MTS 2015 – 14/07/2015

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Classical Topology of the Simulation

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Topology according to the granularity of the simulation, the level of details that is possible in the model [Hoogendoorn, 2001, Davidsson, 2000]. Microscopic Simulation: Macroscopic Simulation: Mesoscopic Simulation:

Something in between.

Multilevel Simulation:

S. Galland

MTS 2015 – 14/07/2015

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Classical Topology of the Simulation

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Topology according to the granularity of the simulation, the level of details that is possible in the model [Hoogendoorn, 2001, Davidsson, 2000]. Microscopic Simulation: Macroscopic Simulation: Mesoscopic Simulation: Multilevel Simulation:

combines various levels (micro-macro for example).

S. Galland

MTS 2015 – 14/07/2015

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Classical Topology of the Simulation

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Topology according to the granularity of the simulation, the level of details that is possible in the model [Hoogendoorn, 2001, Davidsson, 2000]. Microscopic Simulation: Macroscopic Simulation: Mesoscopic Simulation: Multilevel Simulation: Multiagent-based Simulation (MABS) is traditionnally considered as a special form of microscopic simulation, but not restricted to.

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

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1 SARL Agent Programming Language 2 Multiagent Simulation Simulation Fundamentals Multiagent Based Simulation (MABS) Overview of a MABS Architecture Agent Architectures 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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MABS: Main Characteristics and Advantages

35

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.

S. Galland

MTS 2015 – 14/07/2015

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MABS: Limitations and Drawbacks

36

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.

S. Galland

MTS 2015 – 14/07/2015

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MABS: General Idea

37

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.

S. Galland

MTS 2015 – 14/07/2015

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Outline

38

1 SARL Agent Programming Language 2 Multiagent Simulation Simulation Fundamentals Multiagent Based Simulation (MABS) Overview of a MABS Architecture Agent Architectures 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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Multiagent Simulation

39

Agent Agent Environment

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

Direct interaction Perceptions Actions

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MTS 2015 – 14/07/2015

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Designing a Multiagent Simulation Model [Michel, 2004]

40

Behaviors

Internal Agent Architecture Modeling of agent deliberation processes (agent mind)

Environment

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

Scheduling

Temporal dynamic of the system Modeling of the time progress, and of the agent scheduling

Interaction

Modeling the concurrent events Modeling the results of actions and interactions at a given time

manages implies implies manages

Definitions of action, perception, and conflict resolution

S. Galland

MTS 2015 – 14/07/2015

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

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1 SARL Agent Programming Language 2 Multiagent Simulation Simulation Fundamentals Multiagent Based Simulation (MABS) Overview of a MABS Architecture Agent Architectures 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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Classical Agent Architectures

42

There is plenty of agent architectures from the literature. Subsomption Architecture: [Brooks, 1990] Priority-ordering sequence of condition-action tuples.

if condition1 then action1 else if condition2 then action2 else . . . end if end if condition1 action1 condition2 action2 conditionn actionn ∅ inputs

utput

Force-based model: [Helbing, 1997] [Reynolds, 1999] Belief, Desire, Intention (BDI): [Rao, 1995] Others:

S. Galland

MTS 2015 – 14/07/2015

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Classical Agent Architectures

42

There is plenty of agent architectures from the literature. Subsomption Architecture: [Brooks, 1990] Force-based model: [Helbing, 1997] [Reynolds, 1999]

f =

n

i=1

αi. p − ai |−

− − → p − ai|

+ β.

−

− − − − − − − → n

i=1 ai

n − p

p

a1 a2 a3

+

p a1 a2 a3

+

p a1 a2 a3

separation alignment cohesion

Belief, Desire, Intention (BDI): [Rao, 1995] Others:

S. Galland

MTS 2015 – 14/07/2015

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Classical Agent Architectures

42

There is plenty of agent architectures from the literature. Subsomption Architecture: [Brooks, 1990] Force-based model: [Helbing, 1997] [Reynolds, 1999] Belief, Desire, Intention (BDI): [Rao, 1995]

Belief recognition Control flow Generate

ptions

Filtering Action selection Outputs Beliefs Desires Intentions Inputs

Others:

S. Galland

MTS 2015 – 14/07/2015

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Classical Agent Architectures

42

There is plenty of agent architectures from the literature. Subsomption Architecture: [Brooks, 1990] Force-based model: [Helbing, 1997] [Reynolds, 1999] Belief, Desire, Intention (BDI): [Rao, 1995] Others: state machines, activity diagrams, expert systems, goal-oriented algorithms. . .

S. Galland

MTS 2015 – 14/07/2015

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Outline

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1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment General Principles Traffic Simulation Pedestrian Simulation 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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Outline

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1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment General Principles Traffic Simulation Pedestrian Simulation 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

S. Galland

MTS 2015 – 14/07/2015

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Situated Environment Model [Galland, 2009]

45

Agent Agent Environment

Resources
Physical structures
Physical rules

Direct interaction Behavioral component Agent mind Environment Interface Environmental Object Collection Environment's State Influence Solver

ensure valid environment state according to environment laws

Perception data structure

spatial tree, grid, graph

Physical component Agent body Environment Dynamics Engine Perception Influence Influence

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Body-Mind Distinction [Galland, 2009]

46

State variables of the decisional component

Readable/modifiable

nly by the agent

Agent

Environment Interface Agent body

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

Agent mind

Agent memory Behavior Action filter Perception filter Filtered perception Action Influence Perception

State variables of the physical component

Readable by the agent Modifiable by the environment

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Physic Environment in SARL

47

The agent has the capacity to use its body. The body supports the interactions with the environment.

event P e r c e p t i o n { v a l

b j e c t

: Object v a l r e l a t i v e P o s i t i o n : Vector } c a p a c i t y E n v i r o n m e n t I n t e r a c t i o n { moveTheBody ( motion : Vector ) move ( o b j e c t : Object , motion : Vector ) executeActionOn ( o b j e c t : Object , actionName : String , parameters : Object ✯) } space PhysicEnvironment { move ( o b j e c t : Object , motion : Vector ) } s k i l l PhysicBody implements E n v i r o n m e n t I n t e r a c t i o n { v a l env : PhysicEnvironment v a l body : Object def moveTheBody ( motion : Vector ) { move ( t h i s . body , motion ) } def move ( o b j e c t : Object , motion : Vector ) { env . move ( object , motion ) } } c l a s s PhysicEnvironmentImpl implements PhysicEnvironment { p u b l i c void move ( Object

bject ,

Vector motion ) { // . . . } }

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Outline

48

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment General Principles Traffic Simulation Pedestrian Simulation 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Driving Activity

49

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]:

1 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.

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Model of the Environment [Galland, 2009]

50

Road Network

Road polylines: S =

path, objects
path = (x0, y0) · · ·
Graph: G = {S, S → S, S → S} = {segments, entering, exiting}

Operations

Compute the set of objects perceived by a driver (vehicles, roads...): P =   o

distance(d, o) ≤ ∆∧
∈ O∧

∀(s1, s2), path = s1.p, O.s2    where path is the roads followed by a driver d. Move the vehicles, and avoid physical collisions.

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Architecture of the Driver Agent

51

ENVIRONMENT Car Path planning Collision avoidance

path to follow instant acceleration new position perceived objects

JaSim model [Galland, 2009]

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Path Planning

52

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.

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Collision Avoidance

53

Principle: compute the acceleration of the vehicle to avoid collisions with the other vehicles. Intelligent Driver Model [Treiber, 2000] followerDriving =        −(v∆v)2 4b∆p2 if the ahead object is far −a(s + vw)2 ∆p2 if the ahead object is near Free driving: freeDriving = a

1 −

v vc 4

See driver algorithm

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Highway Simulation

54

What is simulated?

1 Vehicles on a French highway. 2 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.

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Highway Simulation

54

What is simulated?

1 Vehicles on a French highway. 2 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.

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Highway Simulation

54

What is simulated?

1 Vehicles on a French highway. 2 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.

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Highway Simulation

54

What is simulated?

1 Vehicles on a French highway. 2 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.

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Video

55 These videos were realized on the SIMULATE tool  Voxelia S.A.S

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

56

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment General Principles Traffic Simulation Pedestrian Simulation 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System

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Pedestrian Simulation

57

What is simulated?

1 Movements of pedestrians at a microscopic level. 2 Force-based model for avoiding collisions.

[Buisson, 2013]

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Pedestrian Simulation

57

What is simulated?

1 Movements of pedestrians at a microscopic level. 2 Force-based model for avoiding collisions.

[Buisson, 2013]

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Force to Apply to Each Agent

58

The force to apply to each agent is:

Fa =

F + wa.δ

F

pt − pa
pt − pa
F =
i∈M

U(ti

c) · ˆ

Si

F: collision-avoidance force.

ˆ Si: a sliding force. ti

c: time to collision to object i.

U(t): scaling function of the time to collision. M: set objects around (including the other agents). wa: weight of the attractive force. δxg: is g if x ≤ 0, 0 otherwise.

Pa Pt ˆ S1 ˆ S2 ˆ S2

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Sliding Force

59

The sliding force Si is:

sj = (pj − pa) × ˆ

y ˆ Sj = sign( sj · ( pt − pa)) sj

sj

ˆ y: vertical unit vector.

Pa pj pt ˆ Sj − ˆ Sj

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Scaling the Sliding Force

60

How to scale ˆ Sj to obtain the repulsive force? Many force-based models use a monotonic decreasing function

f the distance to an obstacle.

But it does not support the velocity of the agent. Solution: Use time-based force scaling function. U(t) =

σ

tφ − σ tmax φ

if 0 ≤ t ≤ tmax if t > tmax

t: estimated time to collision. tmax: the maximum anticipation time. σ and φ are constants, such that U(tmax) = 0.

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Videos

61 These videos were realized on the SIMULATE tool  Voxelia S.A.S

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

62

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment Organizational Modeling Carpooling Simulation 5 Holon-based Simulation 6 Cyber-physical System

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

63

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment Organizational Modeling Carpooling Simulation 5 Holon-based Simulation 6 Cyber-physical System

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Organizational Approach

64

Recently, a particular interest has been given to the use of

rganizational concepts within MAS where the concepts of

“organizations”, “groups”, “communities”, “roles”, “functions”, etc. play an important role. They permits to break the design complexity down by focusing on the organizations, the roles, and the interactions between the roles. Several works from literature:

Agent-Group-Role (AGR) [Ferber, 1998, Ferber, 2004] Moise [Hannoun, 2000, H¨ ubner, 2002] Capacity-Role-Interaction-Organization [Hilaire, 2000, Cossentino, 2010]

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

CRIO Concepts

65

Organization

An organization is defined by a collection of roles that take part in systematic institutionalized patterns of interactions with other roles in a common context. This context consists in shared knowledge and social rules/norms, social feelings, etc and is defined according to an

ntology.

The aim of an organization is to fulfill some requirements.

Source: http://www.aspecs.org, and [Cossentino, 2010]

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CRIO Concepts

66

Role

An expected behavior (a set of role tasks ordered by a plan) and a set of rights and obligations in the organization context. The goal of each role is to contribute to the fulfillment of (a part of) the requirements of the organization within which it is defined. A role can be instantiated either as a Common Role or Boundary Role.

Source: http://www.aspecs.org, and [Cossentino, 2010]

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CRIO Concepts

67

Group

An instance [. . . ] of an Organization [. . . ]. It is used to model an aggregation of roles played by agents.

Source: http://www.aspecs.org, and [Cossentino, 2010]

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

CRIO Concepts

68

Agent

An entity that adopts a decision in order to obtain the satisfaction of one or more of its own goals. An agent may play a set of roles within various groups. These roles interact each other in the specific context provided by the entity itself. The entity context is given by the knowledge, the capacities

wned by the entity itself.

Roles share this context by the simple fact of being part of the same entity.

Source: http://www.aspecs.org, and [Cossentino, 2010]

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Example: Market-like Community

69

1 Client is requesting to Broker the best offer for a service. 2 Broker is contacting Providers for offer submission and select

the best one.

3 Broker is giving back to Client the best offer. 4 Client and Provider are contracting.

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Example: SARL implementation

70

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Example: SARL implementation

71 b e h a v i o r P r o v i d e r B r o k e r { uses P r o p o s a l S e l e c t i o n var p r o p o s a l s : C o l l e c t i o n <Proposal> var p r o v i d i n g : ProvidingSpace

n

C l i e n t R e q u e s t { p r o v i d i n g . emit ( new C a l l ( o c c u r r e n c e ) ) i n ( 1 . hours ) [ var best = s e l e c t ( p r o p o s a l s ) var r e p l y E v e n t = new C o n t r a c t i n g { r e q u e s t = > occurrence , p a r t n e r = > best . source } wake ( r e p l y E v e n t ) ] }

n

Proposal { p r o p o s a l s = p r o p o s a l s + o c c u r r e n c e } }

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

72

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment Organizational Modeling Carpooling Simulation 5 Holon-based Simulation 6 Cyber-physical System

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Carpooling Simulation Model

73

For each day, for each individual

1 Select the best transport mode according to the individual

characteristics.

2 Create a motive to carpool. 3 Communicate this motive with other agents. 4 Negotiate a plan with the interested agents. 5 Execute the agreed plans. 6 Provide a feedback to all concerned agents.

[Galland, 2013]

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Activity Diagram for an Agent

74

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Social Exploration

75

Goal

Explore the social network of the agent to determine potential carpooling partners. If no partner was found, explore the global trip database (website...)

Partner Selection

Three similarities are used for potential matchings:

1 Profile similarity, 2 Path similarity, and 3 Time window similarity.

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Profile Similarity

76

Profile of the agent A

Set of attributes, named aA with |aA| = NA

Profile Similarity

Distance between two attribute sets a0 and a1: d(a0, a1) =

i∈a0∩a1

(a0[i] − a1[i])2 |a0 ∩ a1| Continuous variables are combined into a single distance value dC Discrete variables are combined into a single value dD ∈ [0, 1] Similarities:

sC = 1 − dC sD = 1 − dD

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Path Similarity

77

pathSim(pa, pb) = c(Oa, Da) c(Oa, Ob) + c(Ob, Db) + c(Db, Da) where c(i, j) denote the cost from the length of the path from i to j and the corresponding travel duration.

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Time Window Similarity

78

Time window of a trip

a trip A is defined by a path and a time window iA = [⊥iA, ⊤iA]

Time Interval Similarity

tis(iA, iB) = min(⊤iA, ⊤iB) − max(⊥iA, ⊥iB)

t t ⊥iA ⊤iA ⊥iB ⊤iB tis(iA, iB)

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Carpooling Negotiation

79

Each potential partner is invited to play the role Partner in the Negotiation Pool organization.

Organizational metamodel Capacity-Role-Interaction-Organization (CRIO) [Cossentino, 2010]

They interacts together to negotiate the time window of the trip.

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Negotiation Procotol (part of)

80

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Time Preference Function

81

Each agent A gives its preferences for the time of boarding/alighting: fA : R ⇒ R : t → fA(t) ∈ [0, 1]

1 t

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Time Interval Suitability

82

Evaluate the suitability of times for boarding/alighting: t+C

t

fA(x) · fB(x)dx if t ∈ [max(⊥iA, ⊥iB), min(⊤iA, ⊤iB) − C]

therwise

Each agent uses this suitability to slightly adapt to the time window.

1 t 1 t fB fA 1 t 1 t tis fA · fB C C

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

83

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation Definitions Virtual-enterprise Example 6 Cyber-physical System

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

84

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation Definitions Virtual-enterprise Example 6 Cyber-physical System

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Definition of a Holon

85

Holon (Philosophy) [Koestler, 1967]

A holon is something that is simultaneously a whole and a part. A holon is a self-similar structure composed of holons as sub-structures.

Default Space (Holonic group)

Level n Level n-1 InnerContext

Addr-1 Addr-2 Addr-3 Addr-6 Addr-7 Addr-5 Addr-4 Addr-8 Addr-9

Addr11 Addr10 Addr12 Addr13 Addr14 Addr15 Addr16 Addr17 Addr18

Other Space (Production group) Default Space Default Space

DefaultContext ExternalContext 1

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

86

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation Definitions Virtual-enterprise Example 6 Cyber-physical System

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Architecture of the Virtual Enterprise Application

87

Addr-1 Addr-5 Addr-6 Addr-7 Addr-4 Addr-3 Addr-2 Addr-A Addr-B Addr-9 Addr-8

Costumer Provider

2-ProductOrder 5-Product 4-Product 1-ProductOrder 3-ProductOrder 6-Product

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

SARL implementation

88 event ProductOrder { v a l q u a n t i t y : i n t v a l i d : S t r i n g new ( q : int , i : S t r i n g ) { t h i s . q u a n t i t y = q t h i s . i d = i } } event Product { v a l q u a n t i t y : i n t v a l i d : S t r i n g new ( q : int , i : S t r i n g ) { t h i s . q u a n t i t y = q t h i s . i d = i } } agent P r o v i d e r { var products : Map <String , Address>

n

ProductOrder { products . put (

c c u r r e n c e . id ,
c c u r r e n c e . source )

wake (

ccurrence ,

Scopes . notAddresses ( innerSpace . a d d r e s s ) ) }

n

Product { var adr = products . remove (

c c u r r e n c e . i d

) i f ( a !=== n u l l ) { emit (

ccurrence ,

Scopes . a d d r e s s e s ( adr ) ) } } }

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Outline

89

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System Intelligent Vehicle

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What is a Cyber-physical System?

90

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.

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Outline

91

1 SARL Agent Programming Language 2 Multiagent Simulation 3 Simulation with a Physic Environment 4 Simulation with a Social Environment 5 Holon-based Simulation 6 Cyber-physical System Intelligent Vehicle

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Intelligent Vehicle

92

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.

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Intelligent Vehicle

92

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.

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SARL Language Multiagent Simulation Physic Environment Social Environment Holons Cyber-physical System

Intelligent Vehicle

92

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.

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Holonic Architecture of the System

93

Control Sensors Sensors Control

Pedestrians Vehicle 2 Vehicle 1

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Video

94

[Gechter, 2012]

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Summary

95

1 Multiagent-based simulation model is composed of three

parts:

the behaviors of the agents, the interactions amongs the agents, the definition of the environment,

2 Organizational approach may break down the complexity of

the design.

3 The simulator should limit/avoid the biais introducing by the

running of the model.

4 Multiagent-based simulation is well-adapted for

transport/mobility system analysis and simulation.

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Typical On-going Issues

96

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?

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A Kind of Advertising

97

SARL Agent Programming Language www.sarl.io Intuitive Syntax. Develop faster. Extensible Agent Features. Keep it DRY. True Holonic Agents. The whole is greater than the sum of its parts. Fully Distributed MAS. Janus Platform for SARL. Simulation Environment. Move “turtles” with the Jaak extension.

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SLIDE 114

Thank you for your attention. . .

IRTES-SeT, UTBM 90010 Belfort cedex, France stephane.galland@utbm.fr – http://www.multiagent.fr

SLIDE 115

Appendix

SLIDE 116

Outline

i

1 Books Multiagent Systems Simulation Theory Games and Serious Games Transport Mathematics 2 About the Author 3 Bibliography 4 Bibliography

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Outline

ii

1 Books Multiagent Systems Simulation Theory Games and Serious Games Transport Mathematics 2 About the Author 3 Bibliography 4 Bibliography

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SLIDE 118

Multiagent Systems

iii

An Introduction to Multiagent Systems

2nd edition

Michael WOOLDRIDGE Wiley, 2009 ISBN 0-47-0519460

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Multiagent Systems

iv

Multiagent Systems: algorithmic, game-theoretic, and logical foundations Yoav SHOHAM and Kevin LEYTON-BROWN Cambridge University Press, 2008 ISBN 0-52-1899435

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Multiagent Systems

v

Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence Jacques FERBER Addison Wesley, 1999 ISBN 0-20-1360489

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Multiagent Systems

vi

Multiagent Systems: a modern approach to distributed Artificial Intelligence Gerhard WEISS MIT Press, 2000 ISBN 0-26-2731312

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Outline

vii

1 Books Multiagent Systems Simulation Theory Games and Serious Games Transport Mathematics 2 About the Author 3 Bibliography 4 Bibliography

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Simulation Theory

viii

Theory of Modeling and Simulation

2nd edition

Bernard ZEIGLER, Herbert Praehofer, and Tag Gon Kim Academic Press, 2000 ISBN 0-12-7784551

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Simulation Theory

ix

Simulation for the Social Scientist

2nd edition

Nigel GILBERT and Klaus TROITZSCH Open University Press, 2005 ISBN 0-33-5216005

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Outline

x

1 Books Multiagent Systems Simulation Theory Games and Serious Games Transport Mathematics 2 About the Author 3 Bibliography 4 Bibliography

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Games and Serious Games

xi

Artificial Intelligence for Games Ian MILLINGTON Morgan Kaufmann Publishers & Elsevier Science, 2006 ISBN 0-12-4977820

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Outline

xii

1 Books Multiagent Systems Simulation Theory Games and Serious Games Transport Mathematics 2 About the Author 3 Bibliography 4 Bibliography

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Transport

xiii

Multi-Agent Systems for Transportation Planning and Coordination Hans MOONEN Erasmus Research Institute of Management, 2009 ISBN 978-90-5892-216-8

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Outline

xiv

1 Books Multiagent Systems Simulation Theory Games and Serious Games Transport Mathematics 2 About the Author 3 Bibliography 4 Bibliography

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Mathematics

xv

Essential Mathematics for Games & Interactive Applications: a programmer’s guide

2nd edition

James VAN VERTH and Lars BISHOP Morgan Kaufmann Publishers, 2008 ISBN 0-12-3742971

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Mathematics

xvi

Calculabilit´ e, Complexit´ e et Approximation Jean-Fran¸ cois REY Vuibert France, 2004 ISBN 2-71-1748081

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Outline

xvii

1 Books 2 About the Author 3 Bibliography 4 Bibliography

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Author: Prof.dr. St´ ephane GALLAND

xviii

Professor

Institut de Recherche sur les Transports, l’´ Energie et la Soci´ et´ e Universit´ e de Technologie de Belfort-Montb´ eliard, France

Topics: Multiagent systems, Agent-based simulation, Agent-oriented software engineering, Mobility and traffic modeling

Web page: http://www.multiagent.fr/People:Galland_stephane Email: stephane.galland@utbm.fr Open-source contributions:

http://www.sarl.io http://www.janusproject.io http://www.arakhne.org

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Outline

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1 Books 2 About the Author 3 Bibliography 4 Bibliography

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Outline

xx

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Bibliography (#1)

xxi (2008). Cyber-physical systems. Brooks, R. (1990). Elephants don’t play chess. Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back. Buisson, J., Galland, S., Gaud, N., Yasar, Gon¸ calves, M., and Koukam, A. (2013). Real-time collision avoidance for pedestrian and bicyclist simulation: a smooth and predictive approach. In the 2nd International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS), Halifax, Nova Scotia, Canada. Procedia Computer Science, Elsevier. Cossentino, M., Gaud, N., Hilaire, V., Galland, S., and Koukam, A. (2010). ASPECS: an agent-oriented software process for engineering complex systems - how to design agent societies under a holonic perspective. Autonomous Agents and Multi-Agent Systems, 2(2):260–304. Davidsson, P. (2000). Multi agent based simulation: Beyond social simulation. Multi Agent Based Simulation, LNCS series, 1979. Dechter, R. and Pearl, J. (1985). Generalized best-first search strategies and the optimality of a*.

J. ACM, 32(3):505–536.
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Bibliography (#2)

xxii Delling, D., Sanders, P., Schultes, D., and Wagner, D. (2009). Engineering route planning algorithms. In Lerner, J., Wagner, D., and Zweig, K., editors, Algorithmics of Large and Complex Networks, volume 5515 of Lecture Notes in Computer Science, pages 117–139. Springer Berlin Heidelberg. Ferber, J. and Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-agent systems. In Demazeau, Y., Durfee, E., and Jennings, N., editors, Third International Conference on Multi-Agent Systems (ICMAS’98), pages 128–135, Paris, France. Ferber, J., Gutknecht, O., and Michel, F. (2004). From agents to organizations: an organizational view of multi-agent systems. In Agent-Oriented Software Engineering IV 4th International Workshop, volume 2935 of LNCS, pages 214–230, Melbourne, Australia. Springer Verlag. Ferber, J. and M¨ uller, J. (1996). Influences and reactions : a model of situated multiagent systems. In Second Internationnal Conference on Multi-Agent Systems (ICMAS 96), pages 72–79. Galland, S., Balbo, F., Gaud, N., Rodriguez, S., Picard, G., and Boissier, O. (2015). Contextualize agent interactions by combining social and physical dimensions in the environment. In Demazeau, Y., Decker, K., De la prieta, F., and Bajo perez, J., editors, Advances in Practical Applications of Agents, Multi-Agent Systems, and Sustainability: The PAAMS Collection. Lecture Notes in Computer Science 9086., pages 107–119. Springer International Publishing.

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Bibliography (#3)

xxiii Galland, S., Gaud, N., Demange, J., and Koukam, A. (2009). Environment model for multiagent-based simulation of 3D urban systems. In the 7th European Workshop on Multiagent Systems (EUMAS09), Ayia Napa, Cyprus. Paper 36. Galland, S., Gaud, N., Yasar, A.-U.-H., Knapen, L., Janssens, D., and Lamotte, O. (2013). Simulation model of carpooling with the janus multiagent platform. In the 2nd International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS), Halifax, Nova Scotia, Canada. Procedia Computer Science, Elsevier. Gechter, F., Conter, J.-M., Galland, S., Lamotte, O., and Koukam, A. (2012). Virtual intelligent vehicle urban simulator: Application to vehicle platoon evaluation. Simulation Modelling Practice and Theory (SIMPAT), 24:103–114. Hannoun, M., Boissier, O., Sichman, J. S., and Sayettat, C. (2000). MOISE: An organizational model for multi-agent systems. In et Sichman J., M. M., editor, Advances in Artificial Intelligence, IBERAMIA-SBIA, pages 156–165, Brazil. Helbing, D. and Molnar, P. (1997). Self-organization phenomena in pedestrian crowds. Self-organization of complex structures: from individual to collective dynamics, pages 569–577. Hilaire, V., Koukam, A., Gruer, P., and M¨ uller, J.-P. (2000). Formal specification and prototyping of multi-agent systems. In Omicini, A., Tolksdorf, R., and Zambonelli, F., editors, Engineering Societies in the Agents’ World, number 1972 in Lecture Notes in Artificial Intelligence. Springer Verlag.

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Bibliography (#4)

xxiv Hoogendoorn, S. P. and Bovy, P. H. (2001). State-of-the-art of vehicular traffic flow modelling. Special Issue on Road Traffic Modelling and Control of the Journal of Systems and Control Engineering, 215(4):283–303. H¨ ubner, J., Sichman, J., and Boissier, O. (2002). A model for the structural, functional, and deontic specification of organizations in multiagent systems. In Bittencourt, G. and Ramalho, G., editors, Advances in Artificial Intelligence: 16th Brazilian Symposium on Artificial Intelligence (SBIA), volume 2507 of LNAI, pages 118–128. Springer. Koenig, S. and Likhachev, M. (2005). Fast replanning for navigation in unknown terrain. Robotics, IEEE Transactions on, 21(3):354–363. Koestler, A. (1967). The Ghost in the Machine. Hutchinson. Michel, F. (2004). Formalism, tools and methodological elements for the modeling and simulation of multi-agents systems. PhD thesis, LIRMM, Montpellier, France. Rao, M. P. G. (1995). BDI-agents: From theory to practice. In the First International Conference on Multiagent Systems (ICMAS’95).

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Bibliography (#5)

xxv Reynolds, C. (1999). Steering behaviors for autonomous characters. In Proceedings of the Game Developers Conference, page 763–782. Rodriguez, S., Gaud, N., and Galland, S. (2014). SARL: a general-purpose agent-oriented programming language. Warsaw, Poland. IEEE Computer Society Press. Shannon, R. E. (1977). Simulation modeling and methodology. SIGSIM Simul. Dig., 8(3):33–38. Treiber, M., Hennecke, A., and Helbing, D. (2000). Congested traffic states in empirical observations and microscopic simulations.

Phys. Rev. E, 62:1805–1824.

Weyns, D., Parunak, H. V. D., Michel, F., Holvoet, T., and Ferber, J. (2005). Environment for Multiagent Systems State-of-the-Art and Research Challenges. In Environments for Multi-Agent Systems (E4MAS), pages 1–47. Springer Berlin / Heidelberg. Wooldridge, M. and Ciancarini, P. (2001). Agent-oriented software engineering: The state of the art. In Agent-Oriented Software Engineering: First International Workshop (AOSE 2000), volume 1957 of Lecture Notes in Computer Science, page 1—28. Springer-Verlag.

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Bibliography (#6)

xxvi Zeigler, B. P., Praehofer, H., and Kim, T. G. (2000). Theory of Modeling and Simulation. Academic Press, 2nd edition edition.

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