In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in Distributed Artificial Intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weak-commitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weak-commitment search algorithm ...

Viewing cooperative distributed problem solving (CDPS) as distributed constraint satisfaction provides a useful formalism for characterizing CDPS techniques. In this paper, we describe this formalism and compare algorithms for solving distributed constraint satisfaction problems (DCSPs). In particular, we present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently, in contrast to the traditional sequential backtracking techniques employed in constraint satisfaction problems. Our experimental results show that solving DCSPs in a distributed fashion is worthwhile when the problems solved by individual agents are loosely-coupled. 1 Introduction Cooperative distributed problem solving (CDPS) is a subfield of AI that is concerned with how a set of artificially intelligent agents can work together to solve problems. Recently, [9] has presented the idea of viewing CDPS as a distributed state space search in order to develop...

. When multiple agents are in a shared environment, there usually exist constraints among the possible actions of these agents. A distributed constraint satisfaction problem (distributed CSP) is a problem to find a consistent combination of actions that satisfies these inter-agent constraints. Various application problems in multi-agent systems can be formalized as distributed CSPs. This paper gives an overview of the existing research on distributed CSPs. First, we briefly describe the problem formalization and algorithms of normal, centralized CSPs. Then, we show the problem formalization and several MAS application problems of distributed CSPs. Furthermore, we describe a series of algorithms for solving distributed CSPs, i.e., the asynchronous backtracking, the asynchronous weak-commitment search, the distributed breakout, and distributed consistency algorithms. Finally,we showtwo extensions of the basic problem formalization of distributed CSPs, i.e., handling multiple local variables, and dealing with over-constrained problems. Keywords: Constraint Satisfaction, Search, distributed AI 1.

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The paper introduces an AND/OR search space perspective for graphical models that include probabilistic networks (directed or undirected) and constraint networks. In contrast to the traditional (OR) search space view, the AND/OR search tree displays some of the independencies present in the graphical model explicitly and may sometime reduce the search space exponentially. Indeed, most

This paper presents a new algorithm for solving distributed constraint satisfaction problems (distributed CSPs) called the distributedbreakout algorithm, which is inspired by the breakout algorithm for solving centralized CSPs. In this algorithm, each agent tries to optimize its evaluation value (the number of constraint violations) by exchanging its current value and the possible amount of its improvement among neighboring agents. Instead of detecting the fact that agents as a whole are trapped in a local-minimum, each agent detects whether it is in a quasi-local-minimum, which is a weaker condition than a local-minimum, and changes the weights of constraint violations to escape from the quasi-local-minimum. Experimental evaluations show this algorithm to be much more efficient than existing algorithms for critically difficult problem instances of distributed graph-coloring problems.

A distributed constraint satisfaction problem can formalize various application problems in MAS, and several algorithms for solving this problem have been developed. One limitation of these algorithms is that they assume each agent has only one local variable. Although simple modifications enable these algorithms to handle multiple local variables, obtained algorithms are neither efficient nor scalable to larger problems. We develop a new algorithm that can handle multiple local variables efficiently, which is based on the asynchronous weak-commitment search algorithm. In this algorithm, a bad local solution can be modified without forcing other agents to exhaustively search local problems. Also, the number of interactions among agents can be decreased since agents communicate only when they find local solutions that satisfy all of the local constraints. Experimental evaluations show that this algorithm is far more efficient than an algorithm that uses the prioritization among agents. 1

Consistency techniques can significantly reduce the search space of constraint satisfaction problems (CSP). In particular, arc-consistency algorithms, such as AC-3 [7], AC-4 [8] and AC-6 [2], have been designed. In [9], we presented DisAC-4, a coarse-grained parallel algorithm designed for distributed memory computer using message passing, which is a distributed version of AC-4. We extend here this result. We design DisAC-3 and DisAC-6. The communication scheme is also extended to allow communication during the propagation step of the consistency algorithms. All these algorithms were systematically experimented. An analysis of the different experiments shows that, as in the sequential case, DisAC-6 provides the best performance and that DisAC-3 outperforms DisAC-4 on most tests. All the distributed algorithms shows a linear speedup. This lead to the conclusion that DisAC-6 is a good candidate for distributed arc-consistency.

Distributed architectures and solutions are described for classes of constraint satisfaction problems, called network consistency problems. An inherent assumption of these architectures is that the communication network mimics the structure of the constraint problem. The solutions are required to be self-stabilizing and to treat arbitrary networks, which makes them suitable for dynamic or error-prone environments. We first show that even for relatively simple constraint networks, such as rings, there is no self-stabilizing solution that guarantees convergence from every initial state of the system using a completely uniform, asynchronous model (where all processors are identical). An almost-uniform, asynchronous, network consistency protocol with one specially designated node is shown and proven correct. We also show that some restricted topologies such as trees can accommodate the uniform, asynchronous model when neighboring nodes cannot take simultaneous steps. 1 Introduction Consid...

In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem as a distributed constraint optimization problem (DCOP), in which each agent can communicate with other agents that share an interest in one or more variables. Whereas existing DCOP algorithms can be easily manipulated by an agent, either by misreporting private information or deviating from the algorithm, we introduce M-DPOP, the first DCOP algorithm that provides a faithful distributed implementation for efficient social choice. This provides a concrete example of how the methods of mechanism design can be unified with those of distributed optimization. Faithfulness ensures that no agent can benefit by unilaterally deviating from any aspect of the protocol, neither informationrevelation, computation, nor communication, and whatever the private information of other agents. We allow for payments by agents to a central bank, which is the only central authority that we require. To achieve faithfulness, we carefully integrate the Vickrey-Clarke-Groves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report