. 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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We present a new polynomial-space algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multi-agent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while operating both efficiently and asynchronously. Adopt is guaranteed to find an optimal solution, or a solution within a user-specified distance from the optimal, while allowing agents to execute asynchronously and in parallel. Adopt obtains these properties via a distributed search algorithm with several novel characteristics including the ability for each agent to make local decisions based on currently available information and without necessarily having global certainty. Theoretical analysis shows that Adopt provides provable quality guarantees, while experimental results show that Adopt is significanfly more efficient than synchronous methods. The speedups are shown to be partly due to the novel search strategy employed and partly due to the asynchrony of the algorithm.

In this paper we present a cooperative negotiation 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 mediated negotiations 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 where the problem is over-constrained and to identify the appropriate fix to the over-constrained problem. From the global perspective, the protocol has a hill climbing behavior and because it was designed to work in dynamic environments, is an approximate one. We describe the domain which inspired the creation of this protocol, as well as discuss experimental results.

It is critical that agents deployed in real-world settings, such as businesses, offices, universities and research laboratories, protect their individual users ’ privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning (including both algorithms for distributed constraint optimization (DCOP) and distributed constraint satisfaction (DisCSPs)), and researchers have begun to propose metrics for analysis of privacy loss in such multiagent algorithms. Unfortunately, a general quantitative framework to compare these existing metrics for privacy loss or to identify dimensions along which to construct new metrics is currently lacking. This paper presents three key contributions to address this shortcoming. First, the paper presents VPS (Valuations of Possible States), a general quantitative framework to express, analyze and compare existing metrics of privacy loss. Based on a state-space model, VPS is shown to capture various existing measures of privacy created for specific domains of DisCSPs. The utility of VPS is further illustrated through analysis of privacy loss in DCOP algorithms, when such algorithms are used by personal assistant agents to schedule meetings

Many problems in multi-agent systems can be described as a distributed CSP. However, some real-life problem can be over-constrained and without a set of consistent variable values when described as a distributed CSP. We have presented a distributed partial CSP for handling such an over-constrained situation and a distributed maximal CSP as a subclass of distributed partial CSP. In this paper, we first show another subclass of distributed partial CSP, a distributed hierarchical CSP. Next, we present a series of new algorithms for solving a distributed hierarchical CSP, each of which is designed based on our previous distributed constraint satisfaction algorithms. Finally, we evaluate the performance of the new algorithms on distributed 3-coloring problems in terms of optimality and anytime characteristics. The results show that our new algorithms perform much better than the previous algorithm for finding an optimal solution and produce good results for anytime characteristics. 1.

Abstract.Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking despite the fact that agent privacy is a key motivation for applying DCOPs in many applications. Recently, Maheswaran et al. [1, 2] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do state-of-the art DCOP algorithms suffer from a similar shortcoming? This paper answers that question by investigating several of the most efficient DCOP algorithms, including both DPOP and ADOPT. Furthermore, while previous work investigated the impact on efficiency of distributed contraint reasoning design decisions, e.g. constraint-graph topology, asynchrony, message-contents, this paper examines the privacy aspect of such decisions, providing an improved understanding of privacy-efficiency tradeoffs. Finally, this paper augments previous work on system-wide privacy loss, by investigating inequities in individual agents ’ privacy loss. 1

Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. However, despite agent privacy being a key motivation for applying DCOPs in many applications, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking. Recently, [Maheswaran et al.2005] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that some DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. This paper addresses the question of whether state-of-the art DCOP algorithms suffer from a similar shortcoming by investigating several of the most efficient DCOP algorithms, including both DPOP and ADOPT. Furthermore, while previous work investigated the impact on efficiency of distributed contraint reasoning design decisions (e.g. constraint-graph topology, asynchrony, message-contents), this paper examines the privacy aspect of such decisions, providing an improved understanding of privacy-efficiency tradeoffs.

We investigate how algorithms for Distributed Constraint Reasoning (DCR) can be modified to operate effectively over unreliable communication infrastructure. While DCR algorithms typically assume that communication is perfect, this assumption is problematic because unreliable communication is a common feature of many real-world multiagent domains.

In this paper the Constraint Satisfaction Ant Algorithm (CSAA) framework is presented. The underlying infrastructure and the ants behavior are described in detail. The CSAA framework is an ant-based system for solving discrete Constraint Satisfaction Problems (CSP) and Partial Constraint Satisfaction Problems (PCSP). CSPs and PCSPs are used among others to design facility layouts and schedule workflow and repairs. Ant-based systems use stochastic decision making and positive feedback processes to reach their goal. Ant algorithms have already proven their value in solving various optimization problems. In this paper we show that they are also useful for more general constraint reasoning. We combined the strengths of ant-based systems -- flexibility, the ability to adapt to changes -- with heuristics from traditional constraint reasoning in order to obtain a flexible, yet efficient algorithm. The flexibility is used to continuously improve on the solution. This aspect of the framework gives the algorithm a great advantage over traditional solving methods when constraints and/or variables are added or removed at run-time. This becomes important when for example workflow should change dynamically according to user demands.