Although powerful and robust, constraint programming often reduces to a backtracking constrained search. Consequently, the search time for many complex problems can be great. One solution is multithreaded par-allelization. Multithreaded parallelization can greatly improve search performance , not only increasing the size of solvable problems but also improving heuristic solutions that were prev...

This paper illustrates how a Prolog program, using chronological backtracking to find a solution in some search space, can be enhanced to perform intelligent backtracking. The enhancement crucially relies on the impurity of Prolog that allows a program to store information when a dead end is reached. To illustrate the technique, a simple search program is enhanced. To appear in Theory and Pract...

Extending functional Lisp with McCarthy’s nondeterministic operator AHFJ yields a language which can concisely express search problems. Dependencydirected backtracking is a powerful search strategy. We describe a non-deterministic Lisp dialect called SCHEMER and show that it can provide automatic dependency-directed backtracking. The resulting language provides a convenient interface to this ef...

Finding the sparse solution to under-determined or ill-condition equations is a fundamental problem encountered in most applications arising from linear inverse problem, compressive sensing, machine learning and statistical inference. In this paper, inspired by reformulation of ?1-norm regularized minimization into convex quadratic program Xiao et al. (Nonlinear Anal Theory Methods Appl, 74(11)...

An efficient backtracking algorithm makes with a large dictionary and the heuristic of selecting possible a very fast program to play the SCRABBLE” Brand the move with the highest score at each turn makes a Crossword Game. The efficiency is achieved by creating data very fast program that is rarely beaten by humans. The structures before the backtracking search begins that serve program makes n...

Considering the theoretical hardness of SAT, the astonishing adeptness of SAT solvers when attacking practical problems has changed the way we perceive the limits of algorithmic reasoning. Modern SAT solvers are based on the idea of conflict driven clause learning (CDCL). The CDCL algorithm is a combination of an explicit backtracking search for a satisfying assignment complemented with a deduc...

Pathological dependency cycles occur in state-space planners when control structures cannot efficiently determine a maximal matching for a bipartite operator/binding graph. Without proper search control, the planner will require many computationally expensive backtracks to arrive at a solution. We present a method for improving planning efficiency in the midst of pathological dependency cycles ...

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...