6313593
Solving distributed constraint optimization problems using cooperative mediation
Distributed Constraint Optimization Problems (DCDP) have, for a long time, been considered an important research area for multi-agent systems because a vast number of real-world situations can be modeled by them. The goal of many of the researchers interested in DCOP has been to find ways to solve them efficiently using fully distributed algorithms which are often based on existing centralized techniques. In this paper, we present an optimal, distributed algorithm called optimal asynchronous partial overlay (OptAPO) for solving DCOPs that is based on a partial centralization technique called cooperative mediation. The key ideas used by this algorithm are that agents, when acting as a mediator, centralize relevant portions of the DCDP, that these centralized subproblems overlap, and that agents increase the size of their subproblems as the problem solving unfolds. We present empirical evidence that shows that OptAPO performs better than other known, optimal DCOP techniques.
7638865
A Cooperative mediation-based protocol for dynamic distributed resource allocation
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
14711959
Using Partial Global Plans to Coordinate Distributed Problem Solvers
Communicating problem solvers can cooperate in various ways, such as negotiating over task assignments, exchanging partial solutions to converge on global results, and planning interactions that help each other perform their tasks better. We introduce a new framework that supports different styles of cooperation by using partial global plana to specify effective, coordinated actions for groups of problem solvers. In this framework, problem solvers summarise their local plans into node-plant that they selectively exchange to dynamically model network activity and to develop partial global plans. However, because network and problem characteristics can change and communication channels have delays and limited capacity, problem solvers' models and partial global plans may be incomplete, out-of-date, and inconsistent. Our mechanisms allow problem solvers to agree on consistent partial global plans when possible, and to locally form partial global plans that lead to satisfactory cooperation even in rapidly changing environments where complete agreement is impossible. In this paper, we describe the mechanisms, knowledge representations, and algorithms that we have developed for generating and maintaining partial global plans in a distributed system. We use experiments to illustrate how these mechanisms improve and promote cooperation in a variety of styles.