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 pres...

Backtracking search is frequently applied to solve a constraint-based search problem but it often suuers from exponential growth of computing time. We present an alternative to backtracking search: local search based on connict minimization. We have applied this general search framework to study a benchmark constraint-based search problem, the n-queens problem. An eecient local search algorithm...

This paper introduces a new variant of the Backtracking Search Optimization Algorithm (BSA) in order to improve the quality of the solutions found by the algorithm. The proposed algorithm is called Idol-Guided Backtracking Search Optimization Algorithm (IG-BSA): it uses the historical population defined in BSA to build and update a set of idols taken from several previous generations, and the c...

This paper addresses the following question: what is the essential difference between stochastic local search (LS) and systematic backtracking (BT) that gives LS superior scalability? One possibility is LS’s lack of firm commitment to any variable assignment. Three BT algorithms are modified to have this feature by introducing randomness into the choice of backtracking variable: a forward check...

Heuristics are crucial tools in decreasing search effort in varied fields of AI. In order to be effective, a heuristic must be efficient to compute, as well as provide useful information to the search algorithm. However, some well-known heuristics which do well in reducing backtracking are so heavy that the gain of deploying them in a search algorithm might be outweighed by their overhead. We p...

There are three main algorithmic techniques for solving constraint satisfaction problems: backtracking search, local search, and dynamic programming. In this chapter, I survey backtracking search algorithms. Algorithms based on dynamic programming [15]— sometimes referred to in the literature as variable elimination, synthesis, or inference algorithms—are the topic of Chapter 7. Local or stocha...