hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Inductive logic programming algorithm for estimating quality of partial plans
Department of Computer Science, Lund University, Sweden.ORCID iD: 0000-0002-7796-5201
Department of Computer Science, Lund University, Sweden.
2007 (English)In: MICAI 2007: Advances in Artificial Intelligence: 6th Mexican International Conference on Artificial Intelligence, Aguascalientes, Mexico, November 4-10, 2007. Proceedings / [ed] Alexander Gelbukh, Ángel Fernando Kuri Morales, Berlin: Springer Berlin/Heidelberg, 2007, 359-369 p.Conference paper, Oral presentation only (Refereed)
Abstract [en]

We study agents situated in partially observable environments, who do not have the resources to create conformant plans. Instead, they create conditional plans which are partial, and learn from experience to choose the best of them for execution. Our agent employs an incomplete symbolic deduction system based on Active Logic and Situation Calculus for reasoning about actions and their consequences. An Inductive Logic Programming algorithm generalises observations and deduced knowledge in order to choose the best plan for execution. We show results of using PROGOL learning algorithm to distinguish "bad" plans, and we present three modifications which make the algorithm fit this class of problems better. Specifically, we limit the search space by fixing semantics of conditional branches within plans, we guide the search by specifying relative relevance of portions of knowledge base, and we integrate learning algorithm into the agent architecture by allowing it to directly access the agent's knowledge encoded in Active Logic. We report on experiments which show that those extensions lead to significantly better learning results.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2007. 359-369 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 4827
Keyword [en]
Algorithms, Intelligent agents, Specifications
National Category
Computer Science
Identifiers
URN: urn:nbn:se:hh:diva-21024DOI: 10.1007/978-3-540-76631-5_34ISI: 000251037900034Scopus ID: 2-s2.0-38149099905ISBN: 978-3-540-76631-5 ISBN: 978-3-540-76630-8 OAI: oai:DiVA.org:hh-21024DiVA: diva2:587659
Conference
6th Mexican International Conference on Artificial Intelligence, Aguascalientes, Mexico, November 4-10, 2007
Available from: 2013-01-14 Created: 2013-01-14 Last updated: 2013-10-28Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Nowaczyk, Sławomir
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 84 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf