Quantified Constraint Satisfaction Problems (QCSPs) are CSPs in which some variables are universally quantified. For each possible value of such variables, we have to find ways to ...
Ian P. Gent, Peter Nightingale, Andrew G. D. Rowle...
We consider the deterministic and the randomized decision tree complexities for Boolean functions, denoted DC(f) and RC(f), respectively. A major open problem is how small RC(f) ca...
Aim of this paper is to address the problem of learning Boolean functions from training data with missing values. We present an extension of the BRAIN algorithm, called U-BRAIN (U...
: We study the satisfiability of randomly generated formulas formed by M clauses of exactly K literals over N Boolean variables. For a given value of N the problem is known to be m...
Abstract. The input to a constraint satisfaction problem (CSP) consists of a set of variables, each with a domain, and constraints between these variables formulated by relations o...