Towards A Multi-Agent System for Network Decision Analysis Jan Dijkstra

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Towards A Multi-Agent System for Network Decision
AnalysisJan Dijkstra
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Agenda

• 1. Introduction of the Model
• 3. Essentials of Cellular Automata
• 4. Agent Characteristics
• 5. Multi Agent Simulation Models
• 6. Towards the Framework

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Introduction of the Model
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• 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
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Essentials of Cellular Automata
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• Cellular automata are discrete dynamical systems
  whose behavior is completely specified in terms
  of a local relation

Cellular automata are characterized by the
following features

• Grid

• Time

• Cell

• State

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Cellular Automata Model of Traffic Flow
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Agent Characteristics
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Agent 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).
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Agent 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

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Agent Architecture
State
Perception
Action
Sensors
Effectors
Production System
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Multi Agent Simulation Models
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Offers the promise of simulating autonomous
agents and the interaction between them.
behaviors evolve dynamically during the simulation

• Evolution capabilities
• evolution of the agents environment

• evolution of the agents behavior during the
  simulation
• anticipated behavior
• unplanned behavior

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Towards the Framework
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Artificial Intelligence
Cellular Automata
Distributed Artificial Intelligence
Multi Agent Simulation Models
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Motivation

• 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.

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Network Model
The network is the three-dimensional cellular
automata model representation of a state at a
certain time.
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transition of a state of a cell
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different neighborhoods
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Agent Model
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User Agent
Define an user-agent as U lt R S gt, where

• R is finite set of role identifiers actor,
  subject

• S scenario , defined by S ltB, I, A, F, Tgt,
  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

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The Integration of Cellular Automata and Multi
Agent Technology
Initially, we will realize different graphic
representations of our simulation

• a network-based view

• a main node-based view

• an actor-based view

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network grid and decision points
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main node-based view
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actor-based view / network-based view
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Simulation 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 ...
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