A Toolset for Propositional Probabilistic Logic
Resumo
A pesquisa em representação de conhecimento têm explorado diversos formalismos envolvendo aspectos lógicos e probabilísticos. Neste artigo apresentamos três ferramentas que dão suporte ao desenvolvimento de bases de conhecimento lógico-probabilísticas. A primeira ferramenta é um gerador de problemas de satisfatibilidade probabilística proposicional (PSAT), que é útil para testar algoritmos. A segunda ferramenta é um verificador de consistência em PSAT que lida com probabilidades sobre CNFs. A terceira ferramenta é um editor de problemas PSAT que simplifica a construção de bases de conhecimento.Referências
Andersen, K. A. and Hooker, J. N. (1994). Bayesian logic. Decision Support Systems, 11:191–210.
Dechter, R. and Smyth, P. (2000). Processing Boolean queries over belief networks. Technical Report [link], ICS.
Georgakapoulos, G., Kavvadias, D., and Papadimitriou, C. (1988). Probabilistic satisfiability. Journal of Complexity, 4:1–11.
Goodrich, M. T. and Tamassia, R. (2002). Algorithm Design: Foundations, Analysis and Internet Examples. John Willey and Sons.
Hailperin, T. (1986). Boole’s Logic and Probability. North-Holland, 2nd edition.
Hansen, P. and Jaumard, B. (2000). Probabilistic Satisfiability, Les Cahiers du GERAD, G-96-31, Montreal.
Hansen, P., Jaumard, B., and de Aragão, M. (1991). Column generation methods for probabilistic logic. ORSA Journal on Computing 3:135–148.
Pearl, J. (1988). Intelligent Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1st edition.
Ide, J. S. and Cozman, F. G. (2002). Random generation of Bayesian networks. In Proc. of the XVI Braziliam Symposium on Artifical Intelligence, pages 99–99. Springer-Verlag.
Nilson, N. (1986a). Probabilistic logic. Artificial Intelligence, 28:71–87.
Nilson, N. J. (1986b). Probabilistic logic. Artificial Intelligence, 28:71–87.
Papadimitriou, C. H. and Steiglitz, K. (1982). Combinatorial Optimization: Algorithms and Complexity. Prentice Hall.
Dechter, R. (1996). Bucket elimination: A unifying approach for probabilistic inference algorithms. In Proc. of 10th Annual Conference on Uncertainty in Artificial Intelligence, pages 211–219. Morgan Kaufmann.
Neapolitan, R. E. (1990). Probabilistic Reasoning in Expert Systems. Prentice Hall, 1st edition.
Richardson, M. and Domingos, P. (2006). Markov logic networks. Machine Learning, 62(1-2):107–136.
Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice Hall, 1st edition.
Zhang, N. L. and Poole, D. (1996). Exploiting causal independence in Bayesian network inference. Journal of Artificial Intelligence Research, 5:301–328.
Dechter, R. and Smyth, P. (2000). Processing Boolean queries over belief networks. Technical Report [link], ICS.
Georgakapoulos, G., Kavvadias, D., and Papadimitriou, C. (1988). Probabilistic satisfiability. Journal of Complexity, 4:1–11.
Goodrich, M. T. and Tamassia, R. (2002). Algorithm Design: Foundations, Analysis and Internet Examples. John Willey and Sons.
Hailperin, T. (1986). Boole’s Logic and Probability. North-Holland, 2nd edition.
Hansen, P. and Jaumard, B. (2000). Probabilistic Satisfiability, Les Cahiers du GERAD, G-96-31, Montreal.
Hansen, P., Jaumard, B., and de Aragão, M. (1991). Column generation methods for probabilistic logic. ORSA Journal on Computing 3:135–148.
Pearl, J. (1988). Intelligent Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1st edition.
Ide, J. S. and Cozman, F. G. (2002). Random generation of Bayesian networks. In Proc. of the XVI Braziliam Symposium on Artifical Intelligence, pages 99–99. Springer-Verlag.
Nilson, N. (1986a). Probabilistic logic. Artificial Intelligence, 28:71–87.
Nilson, N. J. (1986b). Probabilistic logic. Artificial Intelligence, 28:71–87.
Papadimitriou, C. H. and Steiglitz, K. (1982). Combinatorial Optimization: Algorithms and Complexity. Prentice Hall.
Dechter, R. (1996). Bucket elimination: A unifying approach for probabilistic inference algorithms. In Proc. of 10th Annual Conference on Uncertainty in Artificial Intelligence, pages 211–219. Morgan Kaufmann.
Neapolitan, R. E. (1990). Probabilistic Reasoning in Expert Systems. Prentice Hall, 1st edition.
Richardson, M. and Domingos, P. (2006). Markov logic networks. Machine Learning, 62(1-2):107–136.
Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Prentice Hall, 1st edition.
Zhang, N. L. and Poole, D. (1996). Exploiting causal independence in Bayesian network inference. Journal of Artificial Intelligence Research, 5:301–328.
Publicado
30/06/2007
Como Citar
ANDRADE, Paulo S. de S.; ROCHA, José C. F. da; COUTO, Danillo P.; TEVES, André da Costa; COZMAN, Fabio G..
A Toolset for Propositional Probabilistic Logic. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 6. , 2007, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2007
.
p. 1371-1380.
ISSN 2763-9061.
