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Documents authored by Inoue, Katsumi

Document
DOI: 10.4230/OASIcs.ICLP.2018.12
Learning Commonsense Knowledge Through Interactive Dialogue

Authors: Benjamin Wu, Alessandra Russo, Mark Law, and Katsumi Inoue

Published in: OASIcs, Volume 64, Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018)

Abstract
One of the most difficult problems in Artificial Intelligence is related to acquiring commonsense knowledge - to create a collection of facts and information that an ordinary person should know. In this work, we present a system that, from a limited background knowledge, is able to learn to form simple concepts through interactive dialogue with a user. We approach the problem using a syntactic parser, along with a mechanism to check for synonymy, to translate sentences into logical formulas represented in Event Calculus using Answer Set Programming (ASP). Reasoning and learning tasks are then automatically generated for the translated text, with learning being initiated through question and answering. The system is capable of learning with no contextual knowledge prior to the dialogue. The system has been evaluated on stories inspired by the Facebook's bAbI's question-answering tasks, and through appropriate question and answering is able to respond accurately to these dialogues.
Cite as

Benjamin Wu, Alessandra Russo, Mark Law, and Katsumi Inoue. Learning Commonsense Knowledge Through Interactive Dialogue. In Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018). Open Access Series in Informatics (OASIcs), Volume 64, pp. 12:1-12:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{wu_et_al:OASIcs.ICLP.2018.12,
  author =	{Wu, Benjamin and Russo, Alessandra and Law, Mark and Inoue, Katsumi},
  title =	{{Learning Commonsense Knowledge Through Interactive Dialogue}},
  booktitle =	{Technical Communications of the 34th International Conference on Logic Programming (ICLP 2018)},
  pages =	{12:1--12:19},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-090-3},
  ISSN =	{2190-6807},
  year =	{2018},
  volume =	{64},
  editor =	{Dal Palu', Alessandro and Tarau, Paul and Saeedloei, Neda and Fodor, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICLP.2018.12},
  URN =		{urn:nbn:de:0030-drops-98780},
  doi =		{10.4230/OASIcs.ICLP.2018.12},
  annote =	{Keywords: Commonsense Reasoning, Answer Set Programming, Event Calculus, Inductive Logic Programming}
}
Document
DOI: 10.4230/LIPIcs.ICLP.2012.86
Generating Event-Sequence Test Cases by Answer Set Programming with the Incidence Matrix

Authors: Mutsunori Banbara, Naoyuki Tamura, and Katsumi Inoue

Published in: LIPIcs, Volume 17, Technical Communications of the 28th International Conference on Logic Programming (ICLP'12) (2012)

Abstract
The effective use of ASP solvers is essential for enhancing efficiency and scalability. The incidence matrix is a simple representation used in Constraint Programming (CP) and Integer Linear Programming for modeling combinatorial problems. Generating test cases for event-sequence testing is to find a sequence covering array (SCA). In this paper, we consider the problem of finding optimal sequence covering arrays by ASP and CP. Our approach is based on an effective combination of ASP solvers and the incidence matrix. We first present three CP models from different viewpoints of sequence covering arrays: the naïve matrix model, the event-position matrix model, and the incidence matrix model. Particularly, in the incidence matrix model, an SCA can be represented by a (0,1)-matrix called the incidence matrix of the array in which the coverage constraints of the given SCA can be concisely expressed. We then present an ASP program of the incidence matrix model. It is compact and faithfully reflects the original constraints of the incidence matrix model. In our experiments, we were able to significantly improve the previously known bounds for many arrays of strength three. Moreover, we succeeded either in finding optimal solutions or in improving known bounds for some arrays of strength four.
Cite as

Mutsunori Banbara, Naoyuki Tamura, and Katsumi Inoue. Generating Event-Sequence Test Cases by Answer Set Programming with the Incidence Matrix. In Technical Communications of the 28th International Conference on Logic Programming (ICLP'12). Leibniz International Proceedings in Informatics (LIPIcs), Volume 17, pp. 86-97, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{banbara_et_al:LIPIcs.ICLP.2012.86,
  author =	{Banbara, Mutsunori and Tamura, Naoyuki and Inoue, Katsumi},
  title =	{{Generating Event-Sequence Test Cases by Answer Set Programming with the Incidence Matrix}},
  booktitle =	{Technical Communications of the 28th International Conference on Logic Programming (ICLP'12)},
  pages =	{86--97},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-43-9},
  ISSN =	{1868-8969},
  year =	{2012},
  volume =	{17},
  editor =	{Dovier, Agostino and Santos Costa, V{\'\i}tor},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2012.86},
  URN =		{urn:nbn:de:0030-drops-36127},
  doi =		{10.4230/LIPIcs.ICLP.2012.86},
  annote =	{Keywords: Event-Sequence Testing, Answer Set Programming, Matrix Model, Constraint Programming, Propositional Satisfiability (SAT)}
}
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