Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Phase Transitions and Backbones of 3-SAT and Maximum 3-SAT
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Universality in Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
A survey of environments and mechanisms for human-human stigmergy
E4MAS'05 Proceedings of the 2nd international conference on Environments for Multi-Agent Systems
Information-Driven phase changes in multi-agent coordination
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
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Multi-agent systems are particularly appropriate for resource allocation, but configuring them for efficient operation requires understanding their dynamics. Concepts from statistical physics, such as phase transitions, can help. In decision problems such as constraint satisfaction, such transitions exhibit an easy-hard-easy effort profile, so that highly overconstrained problems are easier to solve than those near the transition. The conventional wisdom is that the profile in optimization problems such as resource allocation is monotonic, becoming more difficult as constraints increase. Contrary to this lore, we exhibit an easy-hard-easy profile in a multi-agent resource allocation problem. We compare problems that exhibit such a profile with others that do not and offer insights as to when such behavior can be expected and why it is desirable from a practical perspective.