Artificial Intelligence - Special issue on knowledge representation
Optimal composition of real-time systems
Artificial Intelligence
Implementation and evaluation of rational communicative behavior in coordinated defense
Proceedings of the third annual conference on Autonomous Agents
Bayesian Update of Recursive Agent Models
User Modeling and User-Adapted Interaction
Reflections on the Nature of Multi-Agent Coordination and Its Implications for an Agent Architecture
Autonomous Agents and Multi-Agent Systems
Rational Coordination in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
Towards Flexible Multi-Agent Decision-Making Under Time Pressure
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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Decision-making in multiagent settings requires significant computational resources. Agents need to model each other to decide how to coordinate - this sometimes may require solving nested models of many other agents and may be impractical to perform in an acceptable time. In this paper, we investigate ways in which the agents can be equipped with flexible decision-making procedures to allow multiagent decision-making under time pressure. One of the techniques we implemented uses iterative deepening algorithm guided by performance profiles generated offline. When the interaction involves many agents, the algorithm iteratively enhances the quality of coordinated decision-making by incrementally adding the levels of nesting considered, but with the additional penalty of increased running time. To identify the appropriate scope of modeling online we use the concept of urgency which represents cost of delaying decisions. We validate our framework with experiments in a simulated anti-air defense domain. The contribution of our framework is that it endows our autonomous agents with flexibility to cope with time pressure in complex multiagent settings.