Asynchronous branch & bound and A* for disWCSPs with heuristic function based on consistency-maintenance (original) (raw)

Distributed constraint satisfaction with partially known constraints

Constraints - An International Journal, 2009

Distributed constraint satisfaction problems (DisCSPs) are composed of agents connected by constraints. The standard model for DisCSP search algorithms uses messages containing assignments of agents. It assumes that constraints are checked by one of the two agents involved in a binary constraint, hence the constraint is fully known to both agents. This paper presents a new DisCSP model in which constraints are kept private and are only partially known to agents. In addition, value assignments can also be kept private to agents and not be circulated in messages. Two versions of a new asynchronous backtracking algorithm that work with partially known constraints (PKC) are presented. One is a two-phase asynchronous backtracking algorithm and the other uses only a single phase. Another new algorithm preserves the privacy of assignments by performing distributed forward-checking (DisFC). We propose to use entropy as quantitative measure for privacy. An extensive experimental evaluation demonstrates a trade-off between preserving privacy and the efficiency of search, among the different algorithms.

Distributed Private Constraint Optimization

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008

We merge two popular optimization criteria of Distributed Constraint Optimization Problems (DCOPs)reward-based utility and privacy-into a single criterion. Privacy requirements on constraints has classically motivated an optimization criterion of minimizing the number of disclosed tuples, or maximizing the entropy about constraints. Common complete DCOP search techniques seek solutions minimizing the cost and maintaining some privacy. We start from the observation that for some problems we could provide as input a quantification of loss of privacy in terms of cost. We provide a formal way to integrate this new input parameter into the DCOP framework, discuss its implications and advantages.

Secure Distributed Constraint Satisfaction: Reaching Agreement without Revealing Private Information

2002

This paper develops a secure distributed Constraint Satisfaction algorithm. A Distributed Constraint Satisfaction Problem (DisCSP) is a CSP in which variables and constraints are distributed among multiple agents. A major motivation for solving a DisCSP without gathering all information in one server is the concern about privacy/security. However, existing DisCSP algorithms leak some information during the search process and privacy/security issues are not dealt with formally. Our newly developed algorithm utilizes a public key encryption scheme. In this algorithm, multiple servers, which receive encrypted information from agents, cooperatively perform a search process that is equivalent to a standard chronological backtracking. This algorithm does not leak any private information, i.e., neither agents nor servers can obtain any additional information on the value assignment of variables that belong to other agents.

The Distributed Constraint Satisfaction Problem: Formalization and Algorithms

IEEE Transactions on Knowledge and Data Engineering, 1998

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 is, by far more, efficient than the asynchronous backtracking algorithm and can solve fairly large-scale problems

Algorithms for Distributed Constraint Satisfaction: A Review

Autonomous Agents and Multi-agent Systems, 2000

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 nd a consistent combination of actions that satises 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 briey 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, w e show t wo extensions of the basic problem formalization of distributed CSPs, i.e., handling multiple local variables, and dealing with over-constrained problems.

Utilitarian Distributed Constraint Optimization Problems

ArXiv, 2016

Privacy has been a major motivation for distributed problem optimization. However, even though several methods have been proposed to evaluate it, none of them is widely used. The Distributed Constraint Optimization Problem (DCOP) is a fundamental model used to approach various families of distributed problems. As privacy loss does not occur when a solution is accepted, but when it is proposed, privacy requirements cannot be interpreted as a criteria of the objective function of the DCOP. Here we approach the problem by letting both the optimized costs found in DCOPs and the privacy requirements guide the agents' exploration of the search space. We introduce Utilitarian Distributed Constraint Optimization Problem (UDCOP) where the costs and the privacy requirements are used as parameters to a heuristic modifying the search process. Common stochastic algorithms for decentralized constraint optimization problems are evaluated here according to how well they preserve privacy. Furthe...

Distributed Constraint Reasoning

Encyclopedia of Artificial Intelligence

Distributed constraint reasoning is concerned with modeling and solving naturally distributed problems. It has application to the coordination and negotiation between semi-cooperative agents, namely agents that want to achieve a common goal but would not give up private information over secret constraints. When compared to centralized constraint satisfaction (CSP) and constraint optimization (COP), one of the most expensive operations is communication. Other differences stem from new coherence and privacy needs. We review approaches based on asynchronous backtracking and depth-first search spanning trees. Distributed constraint reasoning started as an outgrowth of research in constraints and multi-agent systems. Take the sensors network problem in Figure 1, defined by a set of geographically distributed sensors that have to track a set of mobile nodes. Each sensor can watch only a subset of its neighborhood at a given time. Three sensors need to simultaneously focus on the same mobi...

Global Constraints in Distributed Constraint Satisfaction and Optimization

The Computer Journal, 2013

Global constraints are an essential component in the efficiency of centralized constraint programming. We propose to include global constraints in distributed constraint satisfaction and optimization problems (DisCSPs and DCOPs). We detail how this inclusion can be done, considering different representations for global constraints (direct, nested, binary). We explore the relation of global constraints with local consistency (both in the hard and soft cases), in particular for generalized arc consistency (GAC). We provide experimental evidence of the benefits of global constraints on several benchmarks, both for distributed constraint satisfaction and for distributed constraint optimization.

Utilitarian Approach to Privacy in Distributed Constraint Optimization Problems

2017

Privacy has been a major motivation for distributed problem optimization. However, even though several methods have been proposed to evaluate it, none of them is widely used. The Distributed Constraint Optimization Problem (DCOP) is a fundamental model used to approach various families of distributed problems. Here we approach the problem by letting both the optimized costs found in DCOPs and the privacy requirements guide the agents’ exploration of the search space. We introduce Utilitarian Distributed Constraint Optimization Problem (UDCOP) where the costs and the privacy requirements are used as parameters to a heuristic modifying the search process. Common stochastic algorithms for decentralized constraint optimization problems are evaluated here according to how well they preserve privacy.

Constraint relaxation in distributed constraint satisfaction problems

Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93)

The distributed constraint satisfaction problem (DCSP) formulation has recently been identied as a general framework for formalizing various Distributed Articial Intelligence problems. In this paper, we extend the DCSP formalization by introducing the notion of importance values of constraints. With these values, we dene a solution criterion for DCSPs that are over-constrained (where no solution satises all constraints completely). We show that agents can nd an optimal solution with this criterion by using the asynchronous incremental relaxation algorithm, i n which the agents iteratively apply the asynchronous backtracking algorithm [10] to solve a DCSP, while incrementally relaxing less important constraints. In this algorithm, agents act asynchronously and concurrently, in contrast to traditional sequential backtracking techniques, while guaranteeing the completeness of the algorithm and the optimality of the optimality. Furthermore, we show that, in this algorithm, agents can avoid redundant computation and achieve a vefold speed-up in example problems by maintaining the dependencies between constraint violations (nogoods) and constraints. 1 These dependencies are totally dierent from the dependencies used in dependency-directed backtracking [2], in which dependencies between variable values and constraint violations are maintained. In our algorithm, dependencies between constraint violations (nogoods) and constraints are maintained, since constraints are changed dynamically by constraint relaxation.