We present a framework for a large-scale distributed eScience Articial Intelligence search. Our approach is generic and can be used for many dierent problems. Unlike many other approaches, we do not require dedicated machines, homogeneous infrastructure or the ability to communicate between nodes. We give special consideration to the robustness of the framework, minimising the loss of eort even after total loss of infrastructure, and allowing easy verication of every step of the distribution process. In contrast to most eScience applications, the input data and specication of the problem is very small, being easily given in a paragraph of text. The unique challenges our framework tackles are related to the combinatorial explosion of the space that contains the possible solutions and the robustness of long-running computations. Not only is the time required to nish the computations unknown, but also the resource requirements may change during the course of the computation. We demonstrate the applicability of our framework by using it to solve a challenging and hitherto open problem in computational mathematics. The results demonstrate that our approach easily scales to computations of a size that would have been impossible to tackle in practice just a decade ago.
Many algorithms in operations research and artificial intelligence are based on the backtracking principle, i.e., depth first search in implicitly defined trees. For parallelizing these algorithms, an efficient load balancing scheme is of central importance. 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. Viewing cooperative distributed problem solving (CDPS) as distributed constraint satisfaction provides a useful formalism for characterizing CDPS techniques. This formalism and algorithms for solving distributed constraint satisfaction problems (DCSPs) are compared. A technique called asynchronous backtracking that allows agents to act asynchronously and concurrently, in contrast to the traditional sequential backtracking techniques used in constraint satisfaction problems, is presented. Experimental results show that solving DCSPs in a distributed fashion is worthwhile when the problems solved by individual agents are loosely coupled. > Analytical models and experimental results concerning the average case behavior of parallel backtracking are presented. Two types of backtrack search algorithms are considered: simple backtracking, which does not use heuristics to order and prune search, and heuristic backtracking, which does. Analytical models are used to compare the average number of nodes visited in sequential and parallel search for each case. For simple backtracking, it is shown that the average speedup obtained is linear when the distribution of solutions is uniform and superlinear when the distribution of solutions is nonuniform. For heuristic backtracking, the average speedup obtained is at least linear, and the speedup obtained on a subset of instances is superlinear. Experimental results for many synthetic and practical problems run on various parallel machines that validate the theoretical analysis are presented. >