Learning regular sets from queries and counterexamples
Information and Computation
Crytographic limitations on learning Boolean formulae and finite automata
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Negative Results for Equivalence Queries
Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
What Is the Search Space of the Regular Inference?
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning Programs in the Event Calculus
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
An analysis of first-order logics of probability
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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This paper is concerned with algorithms for the logical generalisation of probabilistic temporal models from examples. The algorithms combine logic and probabilistic models through inductive generalisation. The inductive generalisation algorithms consist of three parts. The first part describes the graphical generalisation of state transition models. State transition models are generalised by applying state mergers. The second part involves symbolic generalisation of logic programs which are embedded in each states. Plotkin's LGG is used for symbolic generalisation of logic programs. The third part covers learning of parameters using statistics derived from the input sequences. The state transitions are unobservable in our settings. The probability distributions over the state transitions and actions are estimated using the EM algorithm. As an application of these algorithms, we learn chemical reaction rules from StochSim, the stochastic software simulator of biochemical reactions.