Skip to main content
SpringerLink
Log in

Planning with Different Forms of Domain-Dependent Control Knowledge — An Answer Set Programming Approach

  • Conference paper
  • First Online:
Logic Programming and Nonmotonic Reasoning (LPNMR 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2173))

Abstract

In this paper we present a declarative approach to adding domain-dependent control knowledge for Answer Set Planning (ASP). Our approach allows different types of domain-dependent control knowledge such as hierarchical, temporal, or procedural knowledge to be represented and exploited in parallel, thus combining the ideas of control knowledge in HTN-planning, GOLOG-programming, and planning with temporal knowledge into ASP. To do so, we view domain-dependent control knowledge as sets of independent constraints. An advantage of this approach is that domain-dependent control knowledge can be modularly formalized and added to the planning problem as desired. We define a set of constructs for constraint representation and provide a set of domainindependent logic programming rules for checking constraint satisfaction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baral, C., and Gelfond, M. Reasoning agents in dynamic domains. In Minker, J,. ed., Logic-Based Artificial Intelligence, Kluwer Academic Publishers, (2000), 257–279, 215, 217, 224

    Google Scholar 

  2. Baral, C., Gelfond, M., and Provetti, A. Reasoning about actions: laws, observations, and hypotheses. In Journal of Logic Programming, volume 31, 201–244, 1994. 216

    Article  MathSciNet  Google Scholar 

  3. Baral, C., McIlraith, S., and Son, T. Formulating diagnostic problem solving using an action language with narratives and sensing. In Proceedings of the 2000 KR Conference, 311–322, 2000. 216, 224

    Google Scholar 

  4. de Kleer, J., Mackworth, A., and Reiter, R. Characterizing diagnoses and systems. In Artificial Intelligence, volume 56(2-3), 197–222, 1992.

    Google Scholar 

  5. Gelfond, M., and Lifschitz, V. The stable model semantics for logic programming. In Logic Programming: Proc. of the Fifth Int’l Conf. and Symp., 1070–1080, 1988.

    Google Scholar 

  6. Gelfond, M., and Lifschitz, V. Classical negation in logic programs and disjunctive databases. In New Generation Computing, 365–387, 1991. 213

    Google Scholar 

  7. Gelfond, M., and Lifschitz, V. Representing actions in extended logic programs. In Proc. of Joint International Conference and Symposium on Logic Programming, 559–573, 1992.

    Google Scholar 

  8. Gelfond, M., and Lifschitz, V. Action languages. In Electronic Transactions on AI, volume 3(16), 1998.

    Google Scholar 

  9. Lifschitz, V. Action languages, Answer Sets, and Planning. In The Logic Programming Paradigm: a 25-Year Perspective. 357–373, Springer Verlag, 1999. 224

    Google Scholar 

  10. Marek, W., and Truszczynski, M. Stable models and an alternative logic paradigm. In The Logic Programming Paradigm: a 25-Year Perspective, 375–398, Springer Verlag, 1999. 217, 224

    Google Scholar 

  11. McCain, T., and Turner, H. A causal theory of ramifications and qualifications. In Artificial Intelligence, volume 32, 57–95, 1995. 219, 224

    Google Scholar 

  12. McIlraith, T. Explanatory diagnosis conjecturing actions to explain observations. In Proceedings of the 1998 KR Conference, 167–177, 1998. 224

    Google Scholar 

  13. Niemela, I. Logic programs with stable model semantics as a constraint programming paradigm. In Annals of Mathematics and Artificial Intelligence, 25(3-4), 241–273, 1999. 224

    Article  MathSciNet  Google Scholar 

  14. Niemela, I., and Simons, P. SMODELS-an implementation of the well-founded and stable model semantics for normal logic programs. In Proc. of LPNMR’97, volume 1265 of Lecture Notes in Computer Science, 420–429, 1997. 217

    Google Scholar 

  15. Reiter, R. A theory of diagnosis from first principles. In Artificial Intelligence, volume 32, 57–95, 1987. 215, 224

    Article  MATH  MathSciNet  Google Scholar 

  16. Simons, P. Extending the stable model semantics with more expressive rules. In 5th International Conference, LPNMR’99, 305–316, 1999. 219

    Google Scholar 

  17. Thielscher, M. A theory of dynamic diagnosis. In Linkoping Electronic Articles in Computer and Information Science, volume 2(11), 1997. 224

    Google Scholar 

  18. H. Turner. Representing actions in logic programs and default theories. In Journal of Logic Programming, 31(1-3):245–298, May 1997. 219

    Article  MATH  MathSciNet  Google Scholar 

  19. Eiter, T., Faber, Leone, N., Pfeifer, G. Declarative Problem Solving in DLV In Minker, J,. ed., Logic-Based Artificial Intelligence, Kluwer Academic Publishers, 257–279, 2000. 221

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, New Mexico State University, PO Box 30001 MSC CS, Las Cruces, NM, 88003, USA

    Tran Cao Son

  2. Department of Computer Science and Engineering, Arizona State University Tempe, AZ, 85287, USA

    Chitta Baral

  3. Knowledge Systems Laboratory, Computer Science, Stanford University Stanford, CA, 94305, USA

    Sheila McIlraith

Authors
  1. Tran Cao Son
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chitta Baral
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sheila McIlraith
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institut für Informationssysteme, Vienna University of Technology, 1040, Wien, Austria

    Thomas Eiter

  2. Institut für Informationssysteme, Vienna University of Technology, 1040, Wien, Austria

    Wolfgang Faber

  3. Department of Computer Science Lexington, University of Kentucky, KY, 40506-0046, USA

    Miros law Truszczyński

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Son, T.C., Baral, C., McIlraith, S. (2001). Planning with Different Forms of Domain-Dependent Control Knowledge — An Answer Set Programming Approach. In: Eiter, T., Faber, W., Truszczyński, M.l. (eds) Logic Programming and Nonmotonic Reasoning. LPNMR 2001. Lecture Notes in Computer Science(), vol 2173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45402-0_17

Download citation

  • DOI: https://doi.org/10.1007/3-540-45402-0_17

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42593-9

  • Online ISBN: 978-3-540-45402-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation