Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning probabilistic logic models from probabilistic examples
Machine Learning
Model evolution by run-time parameter adaptation
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Learning operational requirements from goal models
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Counterexamples in probabilistic model checking
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
Learning structure and parameters of stochastic logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Run-time efficient probabilistic model checking
Proceedings of the 33rd International Conference on Software Engineering
Automated learning of probabilistic assumptions for compositional reasoning
FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Theory and Practice of Logic Programming - Prolog Systems
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We propose a novel framework for Probabilistic Inductive Logic Programming (PILP), called ProbPoly, in the context of Stochastic Logic Programs (SLP). The approach aims at learning probabilities of new learned clauses by minimizing the mean squared error (MSE) between the observed example probabilities and the predicted probabilities. We illustrate the approach using a very simple Probabilistic Context-Fee Grammar (PCFG). We then introduce a basic method for revising the probabilities of a simple discrete time Markov chain (DTMC) using an integration of ProbPoly and a probabilistic model checker, so that the properties, which were initially violated, are satisfied in the new DTMC. This work provides the foundations for a general approach to requirements elaboration in probabilistic settings.