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AI Magazine
Communication in reactive multiagent robotic systems
Autonomous Robots
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WWW7 Proceedings of the seventh international conference on World Wide Web 7
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Communications of the ACM
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Autonomous Robots
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Autonomous Robots
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IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
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Enterprise Search: Tough Stuff
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Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Cooperative Multi-Agent Learning: The State of the Art
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Zerber: r-confidential indexing for distributed documents
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A Short Introduction to Computational Social Choice
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Overcoming the Network Bottleneck in Mobile Computing
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
Explicit and Emergent Cooperation Schemes for Search Algorithms
Learning and Intelligent Optimization
The adaptive web
Intelligent search on the internet
Reasoning, Action and Interaction in AI Theories and Systems
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The effects of cooperation between autonomous electronic or physical agents are widely studied in computational science literature. We concentrate on a homogenous population of agents in a multi-agent system (MAS) to explore the effects of useful memory on goal achievement. We use simulations to consider two-dimensional planar surfaces upon which N targets are randomly scattered. N agents exist each with a maximal interest in one specific target. Agents may observe the positions of ''uninteresting'' targets in the environment and communicate this information to other agents encountered within the environment. The benefits of cooperation can be approximated by pure probabilistic analysis for theoretical search success, but the introduction of real-world cost factors (e.g. fuel, energy, transmission time) associated with movement within the environment renders these predictions unusable. In pure probabilistic terms, higher numbers of cooperative agents can greatly increase search effectiveness. In systems where positive costs are associated with search, internal agent memory factors can allow agent density to approximate pure probabilistic effectiveness. Practical applications for this research include real-time electronic document search, problems in robotic multi-agent systems (e.g. ''foraging'' or ''consumption'' problems), and network coverage for wireless communication devices.