In COLT-92

"We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the Davis-Putnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, N-queens, and Boolean induction. General application strategies and limitations of the approach are also discussed. GSAT is best viewed as a model-finding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theorem-proving problems as model-finding tasks." Proc. AAAI-92.

McGraw-Hill

Machine Learning, 8, 229–256

Tech. rep. Papers from the AAAI Robot Competition, RC-92-01, American Association for Artificial Intelligence.

In ECAI-92, pp. 689–693.

Proceedings of the Fifth Annual Workshop on Computational Learning Theory 5: 144-152, 1992.

MIT Press.

de Finetti Bruno, (1992 [1931]), "On the Subjective Meaning of Probability," in Paola Monari & Daniela Cocchi (eds), Bruno de Finetti: Probabilità e induzione (Induction and Probability), Bologna, CLUEB, 298-329. Title Link: Maria Carla Galavotti. "Pragmatism and the Birth of Subjective Probability". European Journal of Pragmatism and American Philosophy [Online], XI-1 | 2019, Online since 19 July 2019, connection on 21 July 2019.

Chalasani et al. show that this problem is Korf (1985) presents a method for learning macrooperators in NP, but NPcompleteness is open. Tadepalli (1991a, and shows that the method is applicable 1991b) shows how macro tables are polynomially PAClearnable to serially decomposable problems.