RoboCup: The Robot World Cup Initiative
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Context-specific multiagent coordination and planning with factored MDPs
Eighteenth national conference on Artificial intelligence
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An Agent-Based Approach to Distributed Data and Information Fusion
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A probabilistic approach to resource allocation in distributed fusion systems
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Decision-support for real-time multi-agent coordination
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Tool support for agent-based systems in ptolemy
Proceedings of the International Workshop on Security and Dependability for Resource Constrained Embedded Systemss
Engineering Applications of Artificial Intelligence
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Applying multi-agent systems in real world scenarios requires several essential research questions to be answered. Agents have to perceive their environment in order to take useful actions. In a multi-agent system this results in a distributed perception of partial information, which has to be fused. Based on the perceived environment the agents have to plan and coordinate their actions. The relation between action and perception, which forms the basis for planning, can be learned by perceiving the result of an action. In this paper we focus these three major research questions.First, we investigate distributed world models that describe the aspects of the world that are relevant for the problem at hand. Distributed Perception Networks are introduced to fuse observations to obtain robust and efficient situation assessments. Second, we show how coordination graphs can be applied to multi-robot teams to allow for efficient coordination.Third, we present techniques for agent planning in uncertain environments, in which the agent only receives partial information (through its sensors) regarding the true state of environment.