Book ChapterDOI
Learning Simple Deterministic Finite-Memory Automata
Hiroshi Sakamoto
- pp 416-431
TLDR
The class of simple deterministic finite-memory automata is exactly learnable via membership and equivalence queries and the running time is estimated.Abstract:
This paper establishes the learnability of simple deterministic finite-memory automata via membership and equivalence queries Simple deterministic finite-memory automata are a subclass of finite-memory automata introduced by Kaminski and Francez [9] as a model generalizing finite automata to infinite alphabets For arriving at a meaningful learning model, we first prove the equivalence problem for simple deterministic finite-memory automata to be decidable by reducing it to the equivalence problem for finite state automata In particular, there exists a decision algorithm taking as input any two simple deterministic finite-memory automata A and B which computes a number k from A and B as well as two finite-state automata MA and MB over a finite alphabet Σ of cardinality k such that A and B are equivalent over all alphabets iff MA and MB are equivalent over Σ Next, we provide the announced learning algorithm, show its correctness, and analyze its running time The algorithm is partially based on Angluin's [1] observation table In particular, for every target and each finite alphabet Σ, the algorithm outputs a hypothesis that is consistent with the target over Σ Together with the first result mentioned above, we obtain the main result of this paper, ie, the class of simple deterministic finite-memory automata is exactly learnable via membership and equivalence queries Finally, the running time is estimatedread more
Citations
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Book ChapterDOI
Inferring canonical register automata
TL;DR: This paper presents an extension of active automata learning to register automata, an automaton model which is capable of expressing the influence of data on control flow and drastically outperforms the classic L * algorithm, even when exploiting optimal data abstraction and symmetry reduction.
Proceedings ArticleDOI
Learning nominal automata
TL;DR: An Angluin-style algorithm is presented to learn nominal automata, which are acceptors of languages over infinite (structured) alphabets, and can learn a subclass of nominal non-deterministic automata.
Dissertation
Nominal Techniques and Black Box Testing for Automata Learning
TL;DR: Using an adaptation of state-of-the-art algorithms for black-box automata learning, as implemented in the LearnLib tool, it is succeeded to learn a model of the Engine Status Manager, a software component that is used in printers and copiers of Océ.
Active learning of interface programs
TL;DR: This thesis addresses the problem of inferring interface programs of systems at runtime using active automata learning techniques and proposes an efficient active learning algorithm for DFAs and Mealy machines that combines the ideas of several known active learning algorithms in a non-trivial way.
Dissertation
Active Model Learning for the Analysis of Network Protocols
TL;DR: In order to learn an over-approximation of a “large” Mealy machine M, a transducer is placed in between the teacher and the learner, which translates concrete inputs in I to abstract inputs in X, concrete outputs in O to abstract outputs in Y, and vice versa.
References
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Book
Introduction to Automata Theory, Languages, and Computation
TL;DR: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity, appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
Book
Machine Learning: An Artificial Intelligence Approach
TL;DR: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective, including learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.
Journal ArticleDOI
Learning regular sets from queries and counterexamples
TL;DR: In this article, the problem of identifying an unknown regular set from examples of its members and nonmembers is addressed, where the regular set is presented by a minimaMy adequate teacher, which can answer membership queries about the set and can also test a conjecture and indicate whether it is equal to the unknown set and provide a counterexample if not.
Journal ArticleDOI
Queries and Concept Learning
TL;DR: This work considers the problem of using queries to learn an unknown concept, and several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries.
Book
Algorithmic Program Debugging
TL;DR: An algorithm that can fix a bug that has been identified, and integrate it with the diagnosis algorithms to form an interactive debugging system that can debug programs that are too complex for the Model Inference System to synthesize.