Probabilistic Logical Inference on the Web
cplint on SWISH is a web application for probabilistic logic programming. It allows users to perform inference and learning using just a web browser, with the computation performed on the server. In this paper we report on recent advances in the system, namely the inclusion of algorithms for computing conditional probabilities with exact, rejection sampling and Metropolis-Hasting methods. Moreover, the system now allows hybrid programs, i.e., programs where some of the random variables are continuous. To perform inference on such programs likelihood weighting is used that makes it possible to also have evidence on continuous variables. cplint on SWISH offers also the possibility of sampling arguments of goals, a kind of inference rarely considered but useful especially when the arguments are continuous variables. Finally, cplint on SWISH offers the possibility of graphing the results, for example by drawing the distribution of the sampled continuous arguments of goals.
KeywordsProbabilistic logic programming Probabilistic logical inference Hybrid program
This work was supported by the “GNCS-INdAM”.
- 1.Pfeffer, A.: Practical Probabilistic Programming. Manning Publications, Cherry Hill (2016)Google Scholar
- 5.Sato, T.: A statistical learning method for logic programs with distribution semantics. In: 12th International Conference on Logic Programming, Tokyo Japan, pp. 715–729. MIT Press, Cambridge (1995)Google Scholar
- 11.De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: A probabilistic Prolog and its application in link discovery. In: 20th International Joint Conference on Artificial Intelligence, (IJCAI 2005), Hyderabad, India, vol. 7, pp. 2462–2467. AAAI Press, Palo Alto, California USA (2007)Google Scholar
- 16.Von Neumann, J.: Various techniques used in connection with random digits. Nat. Bureau Stand. Appl. Math. Ser. 12, 36–38 (1951)Google Scholar
- 17.Nampally, A., Ramakrishnan, C.: Adaptive MCMC-based inference in probabilistic logic programs. arXiv:1403.6036 (2014)
- 18.Wood, F., van de Meent, J.W., Mansinghka, V.: A new approach to probabilistic programming inference. In: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, pp. 1024–1032 (2014)Google Scholar
- 20.Riguzzi, F., Cota, G.: Probabilistic logic programming tutorial. Assoc. Logic Program. Newsl. 29(1), 1 (2016)Google Scholar