A Multi-Agent System for Visualization Simulated User Behaviour B. de Vries, J. Dijkstra.
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Transcript of A Multi-Agent System for Visualization Simulated User Behaviour B. de Vries, J. Dijkstra.
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 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.
different neighborhoodsdifferent neighborhoods
von Neum ann
r = 1 r = 2
Moore
r = 1 r = 2
transition of a state of a celltransition of a state of a cell
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 ...
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.