Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Ethics of artificial intelligence wikipedia , lookup
Behaviorism wikipedia , lookup
Ecological interface design wikipedia , lookup
Mathematical model wikipedia , lookup
Perceptual control theory wikipedia , lookup
Agent (The Matrix) wikipedia , lookup
Agent-based model wikipedia , lookup
Agent-based model in biology wikipedia , lookup
Towards A Multi-Agent System for Network Decision Analysis Jan Dijkstra Agenda 1. 3. 4. 5. 6. Introduction of the Model Essentials of Cellular Automata Agent Characteristics Multi Agent Simulation Models Towards the Framework 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: • Cell • Grid • State • Time 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 Perception Action Production System Effectors Sensors State 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 Cellular Automata Artificial Intelligence 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. transition of a state of a cell different neighborhoods von Neumann r=1 Moore r=2 r=1 r=2 Agent Model Conjoint Measurement Agent Virtual Environment Decision Support Agent Technical Communication Actor Agent n Actor Agent 1 Simulation Model Interface Agency Virtual Interaction Subject Agent Intuitive Communication User 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 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 S1 S2 S3 S4 S5 ° ° ° ° ° E1 E2 ° S16 ° S15 ° S13 main decision point remaining walkway section decision point ° S17 S14 ° ° S6 S18 S20 ° S19 ° ° ° S7 S12 ° ° S10 section bound S11 ° E3 ° S9 ° S8 main node-based view links actual path actual decision point 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 ...