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