Before a combinatorial problem can be solved by current SAT methods, it must usually be
encoded in conjunctive normal form, which facilitates algorithm implementation and allows a …

Reactive Search integrates sub-symbolic machine learning techniques into search
heuristics for solving complex optimization problems. By automatically adjusting the working …

When multiple agents are in a shared environment, there usually exist con straints among
the possible actions of these agents. A distributed constraint satisfaction problem (distributed …

Planning research in Artificial Intelligence (AI) has often focused on problems where there
are cascading levels of action choice and complex interactions between actions. In contrast …

Recently, inspired by quantum annealing, many solvers specialized for unconstrained
binary quadratic programming problems have been developed. For further improvement and …

We describe a novel incomplete approach for solving constraint satisfaction problems
(CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to use …

The function of many RNAs depends crucially on their structure. Therefore, the design of
RNA molecules with specific structural properties has many potential applications, eg in the …

A major difficulty in evaluating incomplete local search style algorithms for constraint
satisfaction problems is the need for a source of hard problem instances that are guaranteed …

A recent theoretical result by Achlioptas et al. shows that many models of random binary
constraint satisfaction problems become trivially insoluble as problem size increases. This …

The local search algorithm WSat is one of the most successful algorithms for solving the
satisfiability (SAT) problem. It is notably effective at solving hard Random 3-SAT instances …