Towards A Multi-Agent System for Network Decision

Analysis

Jan Dijkstra

Agenda

1. Introduction of the Model

3. Essentials of Cellular Automata

4. Agent Characteristics

5. Multi Agent Simulation Models

6. Towards the Framework

Introduction of the Model

• Architects and urban planners are often faced with the problem to assess how their design or planning decisions will affect the behavior of individuals.

• One way of addressing this problem is the use of models simulating the navigation of users in buildings and urban environments.

A Multi-Agent System based on Cellular Automata

Essentials of Cellular Automata

Cellular automata are discrete dynamical Cellular automata are discrete dynamical systems whose behavior is completely systems whose behavior is completely specified in terms of a local relationspecified in terms of a local relation

• Cell

Cellular automata are characterized by the following features:

• Grid • State • Time

Cellular Automata Model of Cellular Automata Model of Traffic FlowTraffic Flow

Agent Characteristics

Agent DefinitionsAgent Definitions

Agents are computational systems that inhibit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed (Maes).

An autonomous agent is a system situated within and part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda (Franklin & Graesser).

Agent PropertiesAgent Properties• Autonomy

- agents have some control over their actions and internal state

• Social ability- agents interact with other agents

• Reactivity- agents perceive their environment and respond to

changes in it

• Pro-activeness- agents exhibit goal-directed behavior by acting on

their own initiative

• ? Mentalistic capabilities- knowledge, belief, intention, emotion

Agent ArchitectureAgent Architecture

State

ProductionSystem

ActionPerception

Sen

sors

Eff

ecto

rs

Multi Agent Simulation Models

Offers the promise of simulatingsimulating autonomous agents and the interaction between them.

behaviors evolve dynamically during the simulation

Evolution capabilities:

• evolution of the agent’s environment

• evolution of the agent’s behavior during the simulation

• anticipated behavior

• unplanned behavior

Towards the Framework

CellularAutomata

Artificial Intelligence

DistributedArtificial

Intelligence

Multi Agent Simulation Models

MotivationMotivation

• Develop a system how people move in a particular environment.• People are represented by agents.• The cellular automata model is used to simulate

their behavior across the network.

• A simulation system would allow the designer to assess how its design decisions influence user movement and hence performance indicators.

Network ModelNetwork Model

The network is the three-dimensional cellular automata model representation of a state at a certain time.

transition of a state of a celltransition of a state of a cell

different neighborhoodsdifferent neighborhoods

von Neum ann

r = 1 r = 2

Moore

r = 1 r = 2

Agent ModelAgent Model

ConjointMeasurement

Agent

DecisionSupportAgent

ActorAgent 1

ActorAgent n

SubjectAgent

Virtual EnvironmentSimulation Model

VirtualInteraction

Interface AgencyTechnical

Communication IntuitiveCommunication

User AgentUser Agent

Define an user-agent as: U = < R | S >, where:

• R is finite set of role identifiers; {actor, subject}

• S scenario , defined by: S = <B, I, A, F, T>, where:• B represents the behavior of user-agent i • I represents the intentions of a user-agent i • A represents the activity agenda user user-agent i • F represents the knowledge of information about

the environment, called Facets• T represents the time-budget each user-agent

possesses

The Integration of Cellular Automata The Integration of Cellular Automata and Multi Agent Technologyand Multi Agent Technology

• an actor-based view

Initially, we will realize different graphic representations of our simulation:

• a network-based view

• a main node-based view

network grid and decision pointsnetwork grid and decision points

main decision point

remaining walkway section decision point

section bound

E1 E2

E3

°

°

° ° °

°

° °

° S6

S7

S8S10 S9

°

°

°

S11

S12

S13

° ° ° S14S15S16

° ° ° ° ° S1 S2 S3 S4 S5

S18S17

S19 S20

main node-based viewmain node-based view

links

actual path

actual decision point

actor-based view / network-based viewactor-based view / network-based view

Simulation ExperimentSimulation Experiment

Design of a simulation experiment of pedestrian movement.

Considering a T-junction walkway where pedestrians will be randomly created at one of the entrances.

Some impressions ...