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A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios
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A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios

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Presentation of the Distributed Building Evacuation Simulator at the OOAMAS Workshop, in the AAMAS Conference

Presentation of the Distributed Building Evacuation Simulator at the OOAMAS Workshop, in the AAMAS Conference

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  • 1. International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008 1 A Simulation Framework for the Investigation of Adaptive Behaviours in Largely Populated Building Evacuation Scenarios Daniele Gianni, G. Loukas, E. Gelenbe Intelligent Systems and Networks Imperial College London presented by Daniele Gianni gianni@imperial.ac.uk
  • 2. 2 Objective To provide a simulation framework that eases the investigation of adaptive behaviours in the context of building evacuation scenarios International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 3. 3 Presentation Overview  Building Evacuation Scenarios  Motivations  Simulated Model  Simulation Framework – Building Evacuation Simulator (BES)  Incorporating new and possibly adaptive models  Preliminary Validation International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 4. 4 Building Evacuation Scenario Scenario characteristics: • Multi-storey building • Civilians • Technical Resources in and Around the Building • Emergency Event • Emergency Personnel and Resources Civilians try to evacuate following the quickest and safest path to the exit, while adapting to the events Emergency personnel enter the building trying to rescue civilians and extinguish fire International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 5. 5 Motivations  In such scenarios, decentralised optimisation techniques are needed to support actors to adapt to unforeseen and changing scenarios  We want to carry out systematic investigations of such techniques in largely populated scenarios  Moreover, we want to do it in a cost-effective manner International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 6. 6 Motivations  Technically speaking, we need a SW framework that allows: 1. Reproducibility of experiments (systematic investigations) 2. Extendibility to diverse scenarios (cost-effective) 3. Distributed operation (largely populated scenarios) i.e. a distributed simulation framework for building evacuation scenarios that raises the users from the underlying technical details International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 7. 7 Motivations  The development of such simulators, however, requires skills and efforts that might not be available or suitable in particular situations  Skills and knowledge required • Modelling, parallel programming, theory of simulation, and simulation infrastructure  Efforts: • Local / distributed synchronization • Distributed synchronization and communication • Implementation of model logic International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 8. 8 Simulated Model  The model includes: • A world Model, which represents the physical space inside the building and its status • One or more hazard agents, which affect the status of the world • A population of human agents, which compete and cooperate for the use of the physical space according to personal characteristics International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 9. 9 Simulation Framework  The framework is a distributed discrete event simulator and is based on a layered architecture named SimArch [1]  In this context, SimArch: • Allows the transparent interchange of the layers’ implementation to fit specific requirements • Reduces the effort in the development of distributed simulator by the introduction of several abstraction layers  Available SimArch implementation on top of IEEE 1516 High Level Architecture standard [1] D. Gianni, A. D’Ambrogio, and G. Iazeolla, “A Layered Architecture for the Model-driven Development of Distributed Simulators”, The first International Conference on Simulation Tools and Techniques for Communications, Networks and Systems (SIMUTools08), 3rd – 7th March, Marseille, France, ACM, 2008. International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 10. 10 SimArch Distributed Discrete Event Simulation Layer Discrete Event Simulation Service Layer Simulation Components Layer Simulation Model Layer Layer 0 Layer 1 Layer 2 Layer 3 Layer 4 Distributed Computing Infrastructure HLA DIS CORBA WSGrid General Purpose Simulation Oriented International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 11. 11 Layer 2: SimJADE  SimJADE is: • A Java framework for Agent-based M&S • JADE-based, thus FIPA compliant  It offers a formulation of discrete event simulation systems in terms of MAS through its components  It also provides a uniform interface for MAS and Agent-based M&S, easing therefore the development of such simulators International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 12. 12 Layer 3: Agents  The agents are defined by independently specifying: • A behavioural logic that specifies the interactions with the external world • A set of parameters that do not affect the logic As the principle of “Separation of Concerns” suggests International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 13. 13 Types of Agents  Three type of agents • Resource Manager, which manages the access to the world nodes • Human agents, which compete for the use of physical resources • Hazard agents, which affect the status of the world International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 14. 14 Human Agents (HAs)  HAs are provided with: • Personal view of the world • Models (decision, motion, and health) • Goal (simple or composite)  HAs present a standard behavioural logic that bases on movements because they can only interact with the surrounding world International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 15. 15 Going Distributed  Why? The amount of computational resources grows at least as polynomial function of the number of simulated agents  Two major modelling issues to face: • Model partitioning • Model adaptation to the distributed environment International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 16. 16 Incorporating New Models  The incorporation of new and possibly adaptative models is easy and quick as defining them  These can be decision models (goals and how to achieve them), motion model and health model International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 17. 17 Incorporating New Models  For example, we can effortlessly set a graph- based decision model that computes the shortest path from the current position to the exit and which weights are adapted according to the following definition:       )0.( )0.(. )( fireedgeif fireedgeifngthphysicalLeedge edgeweight International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 18. 18 Incorporating New Models  Or, for instance, we can define the motion model on the nodes as: Where in the second branch the agent motion is influenced by the number of queued agents       )0.(. )0.( )( euelengthOfQunodeifnodeTimeeuelengthOfQunodeonstcollisionC euelengthOfQunodeifnodeTime nodemotionTime International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 19. 19 Current developments Distributed Discrete Event Simulation Layer Discrete Event Simulation Service Layer Simulation Components Layer Simulation Model Layer Layer 0 Layer 1 Layer 2 Layer 3 Layer 4 Distributed Computing Infrastructure General Purpose (CORBA, WS, Globus, etc.) Simulation oriented (DIS, HLA, ALSP) International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 20. 20 Preliminary Validation  “Absolute” validation is not possible with direct comparison of data from real world  However, it is still possible in a verifiable scenario. We considered the following scenario: • Four floors, three stairwell • Simple building plan • 80 employee distributed over the floors (20 on each) • Since they are employees, it is reasonable to assume that they know well the floor plan and that are trained to evacuate  Shortest path to evacuate  Orderly and regular motion (FIFO queuing and speed along edges U[1.2;1.5] m/s) International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 21. 21 Preliminary Validation  40 Experiments using different (independent) sequence of numbers with good statistical properties and several civilian distributions over each floor  Average building evacuation time ca 87 s  Simple and optimistic analytical models from Fire Engineering domain give a total evacuation time in the same scenario of 76 s International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 22. 22 Preliminary Validation Behavioural validation speed on the floor = U[1.6.; 1.8] m/s, speed along the stairs as in the previous experiments International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 23. Building Evacuation Simulator Floor1 Floor2 Floor3 Floor4 Stairs A Stairs C Stairs B Point of Collection … International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 24. 24 Conclusions  The use of decentralised optimisation techniques is important to quickly adapt civilians and emergency personnel behaviour in building evacuation scenarios  Such techniques, however, require a systematic investigation before being deployed in real scenarios  Cost and time effective investigations require a software framework that combines: (1) experiment reproducibility with (2) high level of extendibility, and (3) distributed operation  We presented the Building Evacuation Simulator, a simulation framework that meets such requirements, and some basic examples of use International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 25. 25 Thank you! International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 26. 26 Emergency Management  The organization and management of resources and responsibilities for dealing with all aspects of emergencies, preparation and on the field (i.e. emergency scenario)  Two major key aspects in emergency scenarios: • Actors (i.e. civilians and emergency personnel) have to continuously and quickly adapt to unpredictable developments • The behaviour of the individuals affect the final outcome of the rescue operation International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 27. 27 World Model  A graph models the physical space: • Nodes: PoI and “collision” points • Edges: path segments in the simulated world  Graph elements are: • grouped in regions, which demark the perimeter of perceivable areas • enriched with a set of attributes that represent the status of the world on it International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 28. 28 Human Agents (HAs) HAs are characterised by:  A personal view of the world  One or more goals (including the decision models on how to achieve them)  Motion model  Health model International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 29. 29 SimJADE components It is defined through:  A simulation ontology, which defines simulation time and services and which can encapsulate custom ontologies  A simulation agent society • Simulation engine (local/distributed), which orchestrates the simulation • Simulation entities, which incorporate the logic  An interaction protocol between the agents, implemented by a set of behaviours and simulation event handlers International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 30. 30 SimJADE Interface  SimJADE maintains a uniform interface with JADE: • Hold for a simulation time • Send a message to another agent at a simulation time • Wait for a message • Conditional wait for a message before a simulation time International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 31. 31 Resource Manager (RM)  RM is in charge of: • Coordinating the access to the nodes • Providing world updates for each agent  RM is defined by a simple wait event/process event logic that proceeds until the simulation ends International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 32. 32 Parameters of Human Agents update() DecisionModelUpdater ::WorldModel 1* «interface» Observable getMotionTimeOnNode() : Time getMotionTimeOnEdge() : Time MotionModel getLifeTimeOnNode(in n : Node) : Time getLifeTimeOnEdge(in e : Edge) : Time wouldDieIn() : Time willBeAliveAfter(in t : Time) HealthModel HumanAgent1* «interface» Observer workTowards() : Node Goal 1 1 1 * 1 * getNextNode() : Node DecisionModel Node SimpleGoal CompositeGoal passToNextGoal() SequenceOfGoals ConcurrentGoals workTowards() : Node recompute() isAchieved() : Boolean Goal 1 1 1 * orderGoals() GoalSelector 1 1 11 update() DecisionModelUpdater 1 1 International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 33. 33 Logic of Human Agents Reaching the Node Waiting for Movement AuthorisationMovement on the Node Standing on Initial Node Other Custom DynamicsHA dies Node Reached Ultimate Goal Achieved HA dies Start Custom Dynamics Intermediate Goal Achieved Custom Dynamics Accomplished HA dies Movement Completed Movement Authorised International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 34. 34 Model Partitioning  We follow three guidelines: • Exploiting the intrinsic parallelism of independent physical subsystem • Meeting local memory constraints • Minimising the network workload Thus, the simulated world is allocated on independent single area simulator (floor or stairs) running on a separate host International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008
  • 35. 35 Model Adaptation  The performance of the simulator are affected by the amount of data exchanged  Reduce such data by: • Locally store “constant” data • Move only individual knowledge  Agents also interact with local world only  Locally, we adopt a condensed representation of the remote world (GPoI) International Workshop on Organized and Adaptive Multi-Agent Systems at AAMAS Estoril, Portugal, May, 2008

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