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.
Backtracking search in a distributed setting has also been investigated by several authors @cite_11 @cite_30 . A special variant for distributed scenarios, asynchronous backtracking, was proposed in @cite_23 . formalise the distributed constraint satisfaction problem and present algorithms for solving it @cite_33 .
@cite_30 @cite_33 @cite_23 @cite_11
2160458341 2148639734 2157740753 1999038767
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. A distributed concurrent search algorithm for distributed constraint satisfaction problems (DisCSPs) is presented. Concurrent search algorithms are composed of multiple search processes (SPs) that operate concurrently and scan non-intersecting parts of the global search space. Each SP is represented by a unique data structure, containing a current partial assignment (CPA), that is circulated among the different agents. Search processes are generated dynamically, started by the initializing agent, and by any number of agents during search.In the proposed, ConcDB, algorithm, all search processes perform dynamic backtracking. As a consequence of backjumping, a search space can be found unsolvable by a different search process. This enhances the efficiency of the ConcDB algorithm. Concurrent Dynamic Backtracking is an asynchronous distributed algorithm and is shown to be faster than former algorithms for solving DisCSPs. Experimental evaluation of ConcDB, on randomly generated DisCSPs demonstrates that the network load of ConcDB is similar to the network load of synchronous backtracking and is much lower than that of asynchronous backtracking. The advantage of Concurrent Search is more pronounced in the presence of imperfect communication, when messages are randomly delayed. 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. > Abstract The paper offers a systematic way of regrouping constraints into hierarchical structures capable of supporting search without backtracking. The method involves the formation and preprocessing of an acyclic database that permits a large variety of queries and local perturbations to be processed swiftly, either by sequential backtrack-free procedures, or by distributed constraint propagation processes.