Title: A Multi-Agent System for Visualization Simulated User Behaviour
1A Multi-Agent System for Visualization Simulated
User Behaviour
2Agenda
- VR-DIS research programme
- B. de Vries
- AI for visualization of human behavior
- J. Dijkstra
3VR Technology in (Architectural) Design
- Traditional process and use
- Envisioned process and use
4Traditional process Sketch
- Paper Pencil
- Reflection on Thoughts
- Vague
5Traditional process Design
- 2D/3D Modeling
- Material use
- Consultancy Installation, Construction, etc.
6Traditional process Presentation
- Convey design
- Impression of building
7Envisioned process 3D Modeling
- Direct manipulation
- Implicit relations
- Sculpturing
8Envisioned process Scene Painting
- Realistic images
- No construction material
9Envisioned process Evaluation
- Indoor climate
- Lighting
- Structural behavior
- Acoustics
- User behavior
10Example Urban plan
11Towards a Multi-Agent System for Visualizing
Simulated User Behavior
12Introduction of the Model
13- 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
14Essentials of Cellular Automata
15- 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
16Cellular Automata Model of Traffic Flow
17Agent Characteristics
18Agent 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).
19Agent 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
20Agent Architecture
State
Perception
Action
Sensors
Effectors
Production System
21Multi Agent Simulation Models
22 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
23Towards the Framework
24Artificial Intelligence
Cellular Automata
Distributed Artificial Intelligence
Multi Agent Simulation Models
25Motivation
- 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.
26Network Model
The network is the three-dimensional cellular
automata model representation of a state at a
certain time.
27different neighborhoods
28transition of a state of a cell
29Agent Model
30User 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
31The Integration of Cellular Automata and Multi
Agent Technology
Initially, we will realize different graphic
representations of our simulation
32network grid and decision points
33main node-based view
34actor-based view / network-based view
35Simulation 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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39Demo
40Conclusions
41- 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.