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This paper is concerned with decentralized planning and scheduling where the information for decision making resides within local agents. When considering a decentralized approach, the goal is not primarily on achieving global optimality. For instance, [Greenstadt et al. 2006] studies the tradeoff in the Distributed Constraint Optimization (DCOP) problem on efficiency, privacy and optimality. In principle,...
Many algorithms to solve Distributed Constraint Satisfaction Problems (DisCSP) have been introduced in the literature. In this paper, we propose to compare three different algorithms to solve DisCSP. Contrary to algorithms of the literature which are evaluated on graph coloring problems or uniform random binary DisCSPs, we use a multi-robot exploration problem. We show that, for this real world application,...
We propose Multi-HDCS, a new hybrid approach for solving Distributed CSPs with complex local problems. In Multi-HDCS, each agent concurrently: (i) runs a centralised systematic search for its complex local problem; (ii) participates in a distributed local search; (iii) contributes to a distributed systematic search. Acentralised systematic search algorithm runs on each agent, finding all non-interchangeable...
Applying distributed constraint optimization problem (DCOP) solution techniques to domains such as service-oriented agent networks can violate key limiting assumptions behind standard DCOP formulations. We extend the multi-constrained (MC-) DCOP to model problems where each agent controls multiple variables, calling this multi-variable (MV-) MC-DCOP. The MV-MC-DCOP formulation abstracts away some...
Distributed Constraint Optimization Problem (DCOP) is a framework that recently emerged as one of the most successful approaches for coordination in Multi-Agent system. Dynamic Programming Optimization (DPOP) is an algorithm which requires only a linear number of messages, thus introducing exponentially less network overhead than other search algorithms when applied in a distributed setting. In DPOP...
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