Unbiased statistics of a Constraint Satisfaction Problem - A controlled-bias generator

Denis Berthier (1)

1 IMT-BS - DSI - Département Systèmes d'Information

We show that estimating the complexity (mean and distribution) of the instances of a fixed size Constraint Satisfaction Problem (CSP) can be very hard. We deal with the main two aspects of the problem: defining a measure of complexity and generating random unbiased instances. For the first problem, we rely on a general framework and a measure of complexity we presented at CISSE08. For the generation problem, we restrict our analysis to the Sudoku example and we provide a solution that also explains why it is so difficult.

Mots clés: Constraint Satisfaction; Modelling and simulation; Unbiased statistics; AI

Domaines: Intelligence artificielle [cs.AI]

Denis Berthier. Unbiased statistics of a Constraint Satisfaction Problem - A controlled-bias generator. Tarek Sobh; Khaled Elleithy. Innovations in Computing Sciences and Software Engineering, Springer Netherlands, pp.91-97, 2010, 978-90-481-9112-3. ⟨10.1007/978-90-481-9112-3⟩. ⟨hal-00641955⟩