T. L. McCluskey, S. N. Cresswell, N. E. Richardson, M. M. West


AI planning engines require detailed specifications of dynamic knowledge of the domain in which they are to operate, before they can function. Further, they require domain-specific heuristics before they can function efficiently. The problem of formulating domain models containing dynamic knowledge regarding actions is a barrier to the widespread uptake of AI planning, because of the difficulty in acquiring and maintaining them. Here we postulate a method which inputs a partial domain model (one without knowledge of domain actions) and training solution sequences to planning tasks, and outputs the full domain model, including heuristics that can be used to make plan generation more efficient. To do this we extend GIPO’s Opmaker system (Simpson et al., 2007) so that it can induce representations of actions from training sequences without intermediate state information and without requiring large numbers of examples. This method shows the potential for considerably reducing the burden of knowledge engineering, in that it would be possible to embed the method into an autonomous program (agent) which is required to do planning. We illustrate the algorithm as part of an overall method to acquire a planning domain model, and detail results that show the efficacy of the induced model.


  1. AIPS-98 Planning Competition Committee (1998). PDDL - The Planning Domain Definition Language. Technical Report CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control.
  2. Grant, T. (2007). Assimilating planning domain knowledge from other agents. In Proceedings of the 26th Workshop of the UK Planning and Scheduling Special Interest Group, Prague, Czech Republic, December 2007.
  3. Grant, T. J. (1996). Inductive Learning of Knowledge-Based Planning Operators. PhD thesis, de Rijksuniversiteit Limburg te Maastricht, Netherlands.
  4. Hoffmann, J. (2000). A Heuristic for Domain Independent Planning and its Use in an Enforced Hill-climbing Algorithm. In Proceedings of the 14th Workshop on Planning and Configuration - New Results in Planning, Scheduling and Design.
  5. Liu, D. and McCluskey, T. L. (2000). The OCL Language Manual, Version 1.2. Technical report, Department of Computing and Mathematical Sciences, University of Huddersfield .
  6. McCluskey, T. L., Liu, D., and Simpson, R. M. (2003). Gipo ii: Htn planning in a tool-supported knowledge engineering environment. In Proceedings of the Thirteenth International Conference on Automated Planning and Scheduling.
  7. McCluskey, T. L. and Porteous, J. M. (1996). Engineering and Compiling Planning Domain Models to Promote Validity and Efficiency. Technical Report RR9606, School of Computing and Maths, University of Huddersfield.
  8. McCluskey, T. L., Richardson, N. E., and Simpson, R. M. (2002). An Interactive Method for Inducing Operator Descriptions. In The Sixth International Conference on Artificial Intelligence Planning Systems.
  9. Richardson, N. E. (2008). An Operator Induction Tool Supporting Knowledge Engineering in Planning. PhD thesis, School of Computing and Engineering, University of Huddersfield, UK.
  10. Russell, S. J. (1989). Execution architectures and compilation. In Proc. IJCAI.
  11. S. A. Chien (editor) (1997). 1st NASA Workshop on Planning and Scheduling in Space Applications. NASA, Oxnard, CA.
  12. Simpson, R. M., Kitchin, D. E., and McCluskey, T. L. (2007). Planning domain definition using gipo. Journal of Knowledge Engineering, 1.
  13. S.S.Benson (1996). Learning Action Models for Reactive Autonomous Agents. PhD thesis, Dept of Computer Science, Stanford University.
  14. Wu, K., Yang, Q., and Jiang, Y. (2005). Arms: Actionrelation modelling system for learning acquisition models. In Proceedings of the First International Competition on Knowledge Engineering for AI Planning, Monterey, California, USA.
  15. Yang, Q., Pan, R., and Pan, S. J. (2007). Learning recursive htn-method structures for planning. In Proceedings of the ICAPS'07 Workshop on Artificial Intelligence Planning and Learning.

Paper Citation

in Harvard Style

L. McCluskey T., N. Cresswell S., E. Richardson N. and M. West M. (2009). AUTOMATED ACQUISITION OF ACTION KNOWLEDGE . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 93-100. DOI: 10.5220/0001662500930100

in Bibtex Style

author={T. L. McCluskey and S. N. Cresswell and N. E. Richardson and M. M. West},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},

in EndNote Style

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
SN - 978-989-8111-66-1
AU - L. McCluskey T.
AU - N. Cresswell S.
AU - E. Richardson N.
AU - M. West M.
PY - 2009
SP - 93
EP - 100
DO - 10.5220/0001662500930100