Towards A Multi-Agent System for Network Decision Analysis Jan Dijkstra

1. Towards A Multi-Agent System for Network Decision AnalysisJan Dijkstra

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

3. Introduction of the Model

4. 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

5. Essentials of Cellular Automata

6. 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

7. Cellular Automata Model of Traffic Flow

8. Agent Characteristics

9. 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).

10. 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

11. Agent Architecture State Perception Action Sensors Effectors Production System

12. Multi Agent Simulation Models

13. 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

14. Towards the Framework

15. Artificial Intelligence Cellular Automata Distributed Artificial Intelligence Multi Agent Simulation Models

16. 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.

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

18. transition of a state of a cell

19. different neighborhoods

20. Agent Model

21. 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

22. 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

23. network grid and decision points

24. main node-based view

25. actor-based view / network-based view

26. 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 ...