Using Learning Automata to Enhance Local-Search Based SAT Solvers with Learning Capability

نویسندگان

  • Ole-Christoffer Granmo
  • Noureddine Bouhmala
چکیده

The conflict between exploration and exploitation is a well-known problem in machine learning and other areas of artificial intelligence. Learning Automata (LA) Thathachar & Sastry (2004); Tsetlin (1973) capture the essence of this conflict, and have thus occupied researchers for over forty years. Initially, LA were used to model biological systems, however, in the last decades they have also attracted considerable interest because they can learn the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational complexity. Recent applications of LA include allocation of polling resources in web monitoring Granmo et al. (2007), allocation of limited sampling resources in binomial estimation Granmo et al. (2007), and optimization of throughput in MPLS traffic engineering Oommen et al. (2007). LA solutions have furthermore found application within combinatorial optimization problems. In Gale et al. (1990); Oommen & Ma (1988) a so-called Object Migration Automaton is used for solving the classical equipartitioning problem. An order of magnitude faster convergence is reported compared to the best known algorithms at that time. A similar approach has also been discovered for the Graph Partitioning ProblemOommen & Croix (1996). Finally, the list organization problem has successfully been addressed by LA schemes. These schemes have been found to converge to the optimal arrangement with probability arbitrary close to unity Oommen & Hansen (1987). In this chapter we study a new application domain for LA, namely, the Satisfiability (SAT) problem. In brief, we demonstrate how the learning capability of LA can be incorporated into selected classical local-search based solvers for SAT-like problems, with the purpose of allowing improved performance by means of learning. Furthermore, we provide a detailed empirical evaluation of the resulting LA based algorithms, and we analyze the effect that the introduced learning has on the local-search based solvers.

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تاریخ انتشار 2010