Meta-level coordination for solving negotiation chains in semi-cooperative multi-agent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Auction-based multi-robot task allocation in COMSTAR
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Dynamic Pricing Algorithms for Task Allocation in Multi-agent Swarms
Massively Multi-Agent Technology
Resource federation in grid using automated intelligent agent negotiation
Future Generation Computer Systems
Overlay networks for task allocation and coordination in large-scale networks of cooperative agents
Autonomous Agents and Multi-Agent Systems
Solving distributed CSPs using dynamic, partial centralization without explicit constraint passing
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
A multi-agent architectural solution for coherent distributed reconfigurations of function blocks
Journal of Intelligent Manufacturing
Improving the privacy of the asynchronous partial overlay protocol
Multiagent and Grid Systems - Principles and Practice of Multi-Agent Systems
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In this paper, we present a cooperative mediation-based protocol that solves a distributed resource allocation problem while conforming to soft real-time constraints in a dynamic environment. Two central principles are used in this protocol that allow it to operate in constantly changing conditions. First, we frame the allocation problem as an optimization problem, similar to a partial constraint satisfaction problem (PCSP), and use relaxation techniques to derive conflict (constraint violation) free solutions. Second, by using overlapping mediation sessions to conduct the search, we are able to prune large parts of the search space by using a form of arc-consistency. This allows the protocol to both quickly identify situations when the problem is over-constrained, and to determine the appropriate repair. From the global perspective, the protocol has a hill climbing behavior and because it was designed to work in dynamic environments, is approximate. We describe the domain which inspired the creation of this protocol, as well as discuss experimental results