Workshop Learning and Automata(LICS 2017 )

Jun 19- 19, 2017,Reykjavik (Iceland)

Grammatical Inference (GI) studies machine learning algorithms for classical recursive models of computations like automata and grammars. The expressive power of these models and the complexity of associated computational problems are a major research topic within theoretical computer science (TCS). This workshop aims at offering a favorable place for dialogue and at generating discussions between researchers from these two communities. 

Topics of interest include, but are not limited to:

• computational complexity of learning problems involving automata and formal languages.
• Algorithms and frameworks for learning models representing language classes inside and outside the Chomsky hierarchy, including tree and graph grammars.
• Learning problems involving models with additional structure, including numeric weights, inputs/outputs such as transducers, register automata, timed automata, Markov reward and decision processes, and semi-hidden Markov models.
• Relations between automata and recurrent neural networks.
• Active learning of finite state machines and formal languages.
• Methods for estimating probability distributions over strings, trees, graphs, or any data used as input for symbolic models.
• Applications of learning to formal verification and (statistical) model checking.
• Logical aspects of learning and grammatical inference.
• Theoretical studies of learnable classes of languages/representations.
• Metrics and other error measures between automata or formal languages.