A polynomial algorithm for deciding bisimilarity of normed context-free processes
Theoretical Computer Science
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
Inference of Reversible Languages
Journal of the ACM (JACM)
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Iterated Transductions and Efficient Learning from Positive Data: A Unifying View
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Polynomial-time identification of very simple grammars from positive data
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Polynomial-time identification of an extension of very simple grammars from positive data
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Polynomial-time identification of an extension of very simple grammars from positive data
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
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Recently, some non-regular subclasses of context-free grammars have been found to be efficiently learnable from positive data. In order to use these efficient algorithms to infer probabilistic languages, one must take into account not only equivalences between languages but also probabilistic generalities of grammars. The probabilistic generality of a grammar G is the class of the probabilistic languages generated by probabilistic grammars constructed on G. We introduce a subclass of simple grammars (SGs), referred as to unifiable simple grammars (USGs), which is a superclass of an efficiently learnable class, right-unique simple grammars (RSGs). We show that the class of RSGs is unifiable within the class of USGs, whereas SGs and RSGs are not unifiable within the class of SGs and RSGs, respectively. We also introduce simple context-free decision processes, which are a natural extension of finite Markov decision processes and intuitively may be thought of a Markov decision process with stacks. We propose a reinforcement learning method on simple context-free decision processes, as an application of the learning and unification algorithm for RSGs from positive data.