Intelligible AI Planning — Generating Plans Represented as a Set of Constraints

  • Austin Tate


1 Realistic planning systems must allow users and computer systems to cooperate and work together using a “mixed initiative” style. Black box or fully automated solutions are not acceptable in many situations. Studies of expert human problem solvers in stressful or critical situations show that they share many of the problem solving methods employed by hirearchical planning methods studied in Artificial Intelligence. But powerful solvers and constraint reasoners can also be of great help in parts of the planning process. A new more intelligible approach to using AI planning is needed which can use the best “open” styles of planning based on shared plan representations and hierarchical task networks (HTN) and which still allow the use of powerful constraint representations and solvers.


Planning System Constraint Satisfaction Problem Constraint Model Defense Advance Research Project Agency Defense Advance Research Project Agency 
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  1. Beck, H. (1993) TOSCA: A Novel Approach to the Management of Job-shop Scheduling Constraints, Realising CIM’s Industrial Potential: Proceedings of the Ninth CIM-Europe Annual Conference, pages 138–149, (eds. Kooij, C., MacConaill, P.A., and Bastos, J.).Google Scholar
  2. Currie, K.W. and Tate, A. (1991) O-Plan: the Open Planning Architecture, Artificial Intelligence 52(1), Autumn 1991, North-Holland.Google Scholar
  3. Khambhampati, S. and Srivastava, B. (1996) Unifying Classical Planning Approaches, Arizona State University ASU CSE TR 96-006, July 1996.Google Scholar
  4. Klein, G. (1998) Sources of Power - How People Make Decisions, MIT Press, 1998.Google Scholar
  5. Pollack, M. (1994) DIPART Architecture, Technical Report, Department of Computer Science, University of Pittsburgh, PA 15213, USA.Google Scholar
  6. Polyak, S. and Tate, A. (2000) A Common Process Ontology for Process-Centred Organisations, Knowledge Based Systems. Earlier version published as University of Edinburgh Department of Artificial Intelligence Research paper 930, 1998.Google Scholar
  7. Sacerdoti, E. (1977) A structure for plans and behaviours. Artificial Intelligence series, publ. North Holland.Google Scholar
  8. Smith, S. (1994) OPIS: A Methodology and Architecture for Reactive Scheduling, in Intelligent Scheduling, (eds; Zweben, M. and Fox, M.S.), Morgan Kaufmann, Palo Alto, CA., USAGoogle Scholar
  9. Tate, A. (1994) Mixed Initiative Planning in O-Plan2, Proceedings of the ARPA/Rome Laboratory Planning Initiative Workshop, (ed. Burstein, M.), Tucson, Arizona, USA, Morgan Kaufmann, Palo Alto.Google Scholar
  10. Tate, A. (ed.) (1996a) Advanced Planning Technology - Technological Achievements of the ARPA/Rome Laboratory Planning Initiative (ARPI), AAAI Press.Google Scholar
  11. Tate, A. (1996b) Representing Plans as a Set of Constraints - the <I-N-OVA> Model, Proceedings of the Third International Conference on Artificial Intelligence Planning Systems (AIPS-96), pp. 221–228, (Drabble, B., ed.) Edinburgh, Scotland, AAAI Press.Google Scholar
  12. Tate, A. (1998) Roots of SPAR - Shared Planning and Activity Representation, Knowledge Engineering Review, Vol. 13, No. 1, March 1998. See also
  13. Tate, A. (2000) <I-N-OVA> and <I-N-CA> - Representing Plans and other Synthesized Artifacts as a Set of Constraints, AAAI-2000 Workshop on Representational Issues for Real-World Planning Systems, at the National Conference of the American Association of Artificial Intelligence (AAAI-2000), Austin, Texas, USA, August 2000.Google Scholar
  14. Tate, A., Drabble, B. and Kirby, R. (1994) O-Plan2: an Open Architecture for Command, Planning and Control, in Intelligent Scheduling, (eds, Zweben, M. and Fox, M.S.), Morgan Kaufmann, Palo Alto, CA., USA.Google Scholar
  15. Tate, A., Drabble, B. and Dalton, J. (1994) Reasoning with Constraints within O-Plan2, Proceedings of the ARPA/Rome Laboratory Planning Initiative Workshop, (ed. Burstein, M.), Tucson, Arizona, USA, Morgan Kaufmann, Palo Alto, CA, USA.Google Scholar
  16. Tate, A., Levine, J., Jarvis, P. and Dalton, J. (2000) Using AI Planning techniques for Army Small Unit Operations, Poster Paper in the Proceedings of the Fifth International Conference on AI Planning and Scheduling Systems (AIPS-2000), Breckenridge, CO, USA, April 2000.Google Scholar
  17. Wilkins, D. (1988) Practical Planning, Morgan Kaufmann, Palo Alto, 1988.Google Scholar

Copyright information

© Springer-Verlag London 2001

Authors and Affiliations

  • Austin Tate
    • 1
  1. 1.Artificial Intelligence Applications InstituteUniversity of EdinburghUK

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