MAAMAW '92 Selected papers from the 4th European Workshop on on Modelling Autonomous Agents in a Multi-Agent World, Artificial Social Systems
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
An online POMDP algorithm for complex multiagent environments
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Comparison of different coordination strategies for the RoboCupRescue simulation
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Distributed decision-making and task coordination in dynamic, uncertain and real-time multiagent environments
An artificial maieutic approach for eliciting experts' knowledge in multi-agent simulations
MABS'05 Proceedings of the 6th international conference on Multi-Agent-Based Simulation
Designing Agent Behaviour in Agent-Based Simulation through Participatory Method
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
New protocol supporting collaborative simulation
Proceedings of the Second Symposium on Information and Communication Technology
Towards a methodology for the participatory design of agent-based models
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Agent-based simulation for large-scale emergency response: A survey of usage and implementation
ACM Computing Surveys (CSUR)
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The goal of our work is to build a DSS (Decision Support System) to support resource allocation and planning for natural disaster emergencies in urban areas such as Hanoi in Vietnam. The first step has been to conceive a multi-agent environment that supports simulation of disasters, taking into account geospatial, temporal and rescue organizational information. The problem we address is the acquisition of situated expert knowledge that is used to organize rescue missions. We propose an approach based on participatory techniques, interactive learning and machine learning. This paper presents an algorithm that incrementally builds a model of the expert knowledge by online analysis of its interaction with the simulator's proposed scenario.