Computer Science – Artificial Intelligence
Scientific paper
2011-06-26
Journal Of Artificial Intelligence Research, Volume 18, pages 351-389, 2003
Computer Science
Artificial Intelligence
Scientific paper
10.1613/jair.1166
The recent emergence of heavily-optimized modal decision procedures has highlighted the key role of empirical testing in this domain. Unfortunately, the introduction of extensive empirical tests for modal logics is recent, and so far none of the proposed test generators is very satisfactory. To cope with this fact, we present a new random generation method that provides benefits over previous methods for generating empirical tests. It fixes and much generalizes one of the best-known methods, the random CNF_[]m test, allowing for generating a much wider variety of problems, covering in principle the whole input space. Our new method produces much more suitable test sets for the current generation of modal decision procedures. We analyze the features of the new method by means of an extensive collection of empirical tests.
Patel-Schneider P. F.
Sebastiani Roberto
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