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Probabilistic Rule Learning in Nonmonotonic Domains

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Computational Logic in Multi-Agent Systems (CLIMA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6814))

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Abstract

We propose here a novel approach to rule learning in probabilistic nonmonotonic domains in the context of answer set programming. We used the approach to update the knowledge base of an agent based on observations. To handle the probabilistic nature of our observation data, we employ parameter estimation to find the probabilities associated with each of these atoms and consequently with rules. The outcome is the set of rules which have the greatest probability of entailing the observations. This ultimately improves tolerance of noisy data compared to traditional inductive logic programming techniques. We illustrate the benefits of the approach by applying it to a planning problem in which the involved agent requires both nonmonotonicity and tolerance of noisy input.

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Author information

Authors and Affiliations

  1. Department of Computing, Imperial College London, 180 Queen’s Gate, SW7 2AZ, London, UK

    Domenico Corapi, Daniel Sykes & Alessandra Russo

  2. National Institute of Informatics, Chiyoda-ku, 2-1-2, Hitotsubashi, Tokyo, 101-8430, Japan

    Katsumi Inoue

Authors
  1. Domenico Corapi
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  2. Daniel Sykes
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  3. Katsumi Inoue
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  4. Alessandra Russo
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Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia, Departamento de Informática, CENTRIA, Quinta da Torre, 2829-516, Caparica, Portugal

    João Leite

  2. Università di Bologna, Laboratorio di Informatica Avanzata, Dipartimento di Informatica, Elettronica e Sistemistica, Viale Risorgimento 2, 40136, Bologna, Italy

    Paolo Torroni

  3. Department of Information Science and Media Studies, University of Bergen, P.O. Box 7802, 5020, Bergen, Norway

    Thomas Ågotnes

  4. University of Torino, Italy

    Guido Boella

  5. Faculté des Sciences, de la Technologie et de la Communication, Université du Luxembourg, 6 Rue Richard Coudenhove-Kalergi, 1359, Luxembourg, Luxembourg

    Leon van der Torre

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Corapi, D., Sykes, D., Inoue, K., Russo, A. (2011). Probabilistic Rule Learning in Nonmonotonic Domains. In: Leite, J., Torroni, P., Ågotnes, T., Boella, G., van der Torre, L. (eds) Computational Logic in Multi-Agent Systems. CLIMA 2011. Lecture Notes in Computer Science(), vol 6814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22359-4_17

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  • DOI: https://doi.org/10.1007/978-3-642-22359-4_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22358-7

  • Online ISBN: 978-3-642-22359-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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