Distributed Models for solving CSPs (original) (raw)

Distributed CSPs by graph partitioning

Applied Mathematics and Computation, 2006

Nowadays, many real problems in artificial intelligence can be modelled as constraint satisfaction problems (CSPs). A general CSP is known to be NP-complete. Nevertheless, distributed models may reduce the exponential complexity by partitioning the problem into a set of subproblems. In this paper, we present a preprocess technique to break a single large problem into a set of smaller loosely connected ones. These semi-independent CSPs can be efficiently solved and, furthermore, they can be solved concurrently.

Nogood-FC for solving partitionable constraint satisfaction problems

Journal of Intelligent Manufacturing, 2010

Many real problems can be naturally modelled as constraint satisfaction problems (CSPs). However, some of these problems are of a distributed nature, which requires problems of this kind to be modelled as distributed constraint satisfaction problems (DCSPs). In this work, we present a distributed model for solving CSPs. Our technique carries out a partition over the constraint network using a graph partitioning software; after partitioning, each sub-CSP is arranged into a DFS-tree CSP structure that is used as a hierarchy of communication by our distributed algorithm. We show that our distributed algorithm outperforms well-known centralized algorithms solving partitionable CSPs.

Problem Partition and Solvers Coordination in Distributed Constraint Satisfaction

This paper presents a decompositionnbased distributed algorithm for solving constraint satisfaction problems. The main alternatives for distributing constraint satisfaction are reviewed. An algorithm using a partition of the constraint graph is then detailed, with its parallel version. Experiments on problems made of loosely connected random constraint satisfaction problems show its beneets for under-constrained problems and problems which complexity is in the phase transition zone.

Distributed Splitting of Constraint Satisfaction Problems

Lecture Notes in Computer Science, 2000

Constraint propagation aims to reduce a constraint satisfaction problem into an equivalent but simpler one. However, constraint propagation must be interleaved with a splitting mechanism in order to compose a complete solver. In 13 a framework for constraint propagation based on a control-driven coordination model was presented. In this paper we extend this framework in order to integrate a distributed splitting mechanism. This technique has three main advantages: 1 in a single distributed and generic framework, propagation and splitting can be interleaved in order to realize complete distributed solvers, 2 by changing only one agent, we can perform di erent kinds of search, and 3 splitting of variables can be dynamically triggered before the xed point of a propagation is reached.

A distributed algorithm solving CSPs with a low communication cost

Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence, 1996

We present a new distributed algorithm which nds all solutions of Constraint Satisfaction Problems. Based on the Backtrack algorithm, it spreads subtrees of the search tree over processes running in parallel. The work is optimally shared among the processes while the communication cost remains low. We show that the speedup of the resolution is asymp totically linear as the number of variables increases. Furthermore, we study the addition of Lookahead pruning techniques and Nogood Recording. Experimental results con rm the e ciency of the algorithm, even if the search tree is very unbalanced.

Distributed Partial Constraint Satisfaction Problem

1997

Many problems in multi-agent systems can be described as distributed Constraint Satisfaction Problems (distributed CSPs), where the goal is to find a set of assignments to variables that satisfies all constraints among agents. However, when real problems are formalized as distributed CSPs, they are often over-constrained and have no solution that satisfies all constraints. This paper provides the Distributed Partial Constraint Satisfaction Problem (DPCSP) as a new framework for dealing with over-constrained situations. We also present new algorithms for solving Distributed Maximal Constraint Satisfaction Problems (DMCSPs), which belong to an important class of DPCSP. The algorithms are called the Synchronous Branch and Bound (SBB) and the Iterative Distributed Breakout (IDB). Both algorithms were tested on hard classes of over-constrained random binary distributed CSPs. The results can be summarized as SBB is preferable when we are mainly concerned with the optimality of a solution, while IDB is preferable when we want to get a nearly optimal solution quickly.

Feasible distributed CSP models for scheduling problems

Engineering Applications of Artificial Intelligence, 2008

Nowadays, many real problems can be formalized as Distributed CSPs. A distributed constraint satisfaction problem (DisCSP) is a CSP in which variables and constraints are distributed among multiple automated agents. Many researchers assume for simplicity that each agent has exactly one variable. For real planning and scheduling problems, these distributed techniques require a large amount of messages passed among agents, so these problems are very difficult to solve. In this paper, we present a general distributed model for solving real-life scheduling problems and propose some guidelines for distributing large-scale problems. Furthermore, we present two case studies in which two scheduling problems are distributed by using our model.

Distributed Breakout Algorithm for Solving Distributed Constraint Satisfaction Problems

1996

This paper presents a new algorithm for solving distributed constraint satisfaction problems (distributed CSPs) called the distributed breakout algorithm, which is inspired by the breakout algorithm for solving centraiized 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.

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.

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