DynABT: Dynamic Asynchronous Backtracking for Dynamic DisCSPs

Abstract

Abstract. Constraint Satisfaction has been widely used to model static combinatorial problems. However, many AI problems are dynamic and take place in a distributed environment, i.e. the problems are distributed over a number of agents and change over time. Dynamic Distributed Constraint Satisfaction Problems (DDisCSP) [1] are an emerging field for the resolution problems that are dynamic and distributed in nature. In this paper, we propose DynABT, a new Asynchronous algorithm for DDisCSPs which combines solution and reasoning reuse i.e. it handles problem changes by modifying the existing solution while re-using knowledge gained from solving the original(unchanged) problem. The benefits obtained from this approach are two-fold: (i) new solutions are obtained at a lesser cost and; (ii) resulting solutions are stable i.e. close to previous solutions. DynABT has been empirically evaluated on problems of varying difficulty and several degrees of changes has been found to be competitive for the problem classes tested.

Distributed Problem Solving

“…For example, power lines might fail or power requirements of a district might change. Researchers have thus extended DCR models to dynamic DCR models like dynamic DCSPs (Mailler 2005; Omomowo, Arana, and Ahriz 2008) and dynamic DCOPs (Petcu and Faltings 2005c; Sultanik, Lass, and Regli 2009; Yeoh et al 2011). A typical model of a dynamic DCR problem is a sequence of (static) DCR problems with changes from one DCR problem to the next one in the sequence.…”