Bibliography | Falk, Matthias: Symbolic Learning of State Transitions of Deterministic Finite Automata. University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Diploma Thesis No. 10 (2017). 163 pages, english.
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CR-Schema | I.2.2 (Automatic Programming) I.2.4 (Knowledge Representation Formalisms and Methods) I.2.6 (Artificial Intelligence Learning) I.2.10 (Vision and Scene Understanding) I.5.1 (Pattern Recognition Models) F.1.1 (Models of Computation) E.4 (Data Coding and Information Theory)
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Abstract | A framework for a new supervised learning algorithm, called Insight that represents a situated agent, is sketched. The envisioned agent should be able to predict the behaviour of its finite, deterministic, fully observable, singleagent environment represented by a Finite State Machine. By generalising the observed state transitions, which are only a fraction of all possible ones, the agent should become capable to predict the full state transition table. States are not – as it is common in automata theory – simply enumerated but are represented as structured entities, as vectors of primitive values, called state aspects. State vectors are mapped one-to-one to the agent’s perception apparatus, each state aspect corresponding to a single sensor. The envisioned agent should describe observed transitions in terms of a self-invented (concept) vocabulary of objects (nouns), their properties and interrelations (adjectives) and changes (verbs).
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Department(s) | University of Stuttgart, Institute for Natural Language Processing
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Entry date | July 3, 2018 |
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