A multi agent system for visualization simulated user behaviour
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A Multi-Agent System for Visualization Simulated User Behaviour. B. de Vries, J. Dijkstra. Agenda. VR-DIS research programme: B. de Vries AI for visualization of human behavior: J. Dijkstra. VR Technology in (Architectural) Design. Traditional process and use Envisioned process and use.

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A Multi-Agent System for Visualization Simulated User Behaviour

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A Multi-Agent System for Visualization Simulated User Behaviour

B. de Vries, J. Dijkstra


Agenda

VR-DIS research programme:

B. de Vries

AI for visualization of human behavior:

J. Dijkstra


VR Technology in (Architectural) Design

  • Traditional process and use

  • Envisioned process and use


Traditional process: Sketch

  • Paper & Pencil

  • Reflection on Thoughts

  • Vague


Traditional process: Design

  • 2D/3D Modeling

  • Material use

  • Consultancy: Installation, Construction, etc.


Traditional process: Presentation

  • Convey design

  • Impression of building


Envisioned process: 3D Modeling

  • Direct manipulation

  • Implicit relations

  • Sculpturing


Envisioned process: Scene Painting

  • Realistic images

  • No construction material


Envisioned process: Evaluation

  • Indoor climate

  • Lighting

  • Structural behavior

  • Acoustics

  • User behavior


Example: Urban plan


Towards a Multi-Agent System for Visualizing Simulated User Behavior


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 systems whose behavior is completely specified in terms of a local relation

Cellular automata are characterized by the following features:

  • Grid

  • Time

  • Cell

  • State


Cellular Automata Model of Traffic Flow


Agent Characteristics


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).


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


Agent Architecture

State

Perception

Action

Sensors

Effectors

Production

System


Multi Agent Simulation Models


Offers the promise of simulating 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


Artificial Intelligence

Cellular

Automata

Distributed

Artificial

Intelligence

Multi Agent Simulation Models


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.


Network Model

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


different neighborhoods


transition of a state of a cell


Agent Model


User Agent

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

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

  • Sscenario , 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 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


network grid and decision points


main node-based view


actor-based view / network-based view


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 ...


Demo


Conclusions


  • Complex behavior can be simulated by using the concept of cellular automata in the context of multi-agent technology.

  • The development of multi-agent models offers the promise of simulating autonomous individuals.

  • A multi-agent model can be used for visualizing simulated user behavior to support the assignment of design performance.

  • The proposed concept potentially has a lot to offer in architecture and urban planning when visual and active environments may impact user behavior and decision-making processes.


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