Integrating Function Application in State-Based Planning
- 443 Downloads
We present an extension of state-based planning from traditional Strips to function application, allowing to express operator effects as updates. As proposed in PDDL, fluent variables are introduced and, consequently, predicates are defined over general terms. Preconditions of operators are characterized as variable binding constraints with standard preconditions as a special case of equality constraints. Operator effects can be expressed by ADD/DEL effects and additionally by updates of fluent variables. Mixing ADD/DEL effects and updates in an operator is allowed. Updating can involve the application of user-defined and built-in functions of the language in which the planner is realized. We present an operational semantics of the extended language. We will give a variety of example domains which can be dealt with in an uniform way: planning with resource variables, numerical problems such as water jug, functional variants of Tower of Hanoi and blocks-world, list sorting, and constraint-logic programming.
KeywordsFree Variable Function Symbol Function Application Resource Variable Relational Symbol
Unable to display preview. Download preview PDF.
- Bacchus, F., Kautz, H., Smith, D. E., Long, D., Geffner, H., & Koehler, J. (2000). AIPS-00 Planning Competition, Breckenridge, CO.Google Scholar
- Bonet, B., & Geffner, H. (1998). Learning sorting and decision trees with POMDPs. In Proc. 15th international conf. on machine learning (pp. 73–81). Morgan Kaufmann.Google Scholar
- Bonet, B., & Geffner, H. (1999). Planning as heuristic search: New results. In Proc. European Conference on Planning (ECP-99), Durham, UK. Springer.Google Scholar
- Ehrig, H., & Mahr, B. (1985). Fundamentals of algebraic specification 1. Springer.Google Scholar
- Field, A. J., & Harrison, P. G. (1988). Functional progamming. Reading, MA: Addison-Wesley.Google Scholar
- Fox, M., & Long, D. (2001). PDDL2.1: An extension to PDDLfor expressing temporal planning domains. http://www.dur.ac.uk/d.p.long/competition.html.
- Frühwirth, T., & Abdennadher, S. (1997). Constraint-programming. Berlin: Springer.Google Scholar
- Geffner, H. (2000). Functional Strips: A more flexible language for planning and problem solving. In J. Minker (Ed.), Logic-based artificial intelligence. Dordrecht: Kluwer.Google Scholar
- Kautz, H., & Selman, B. (1996). Pushing the envelope: Planning, propositional logic and stochastic search. In Proc. 13th national conference on artificial intelligence and 8th innovative applications of artificial intelligence conference (pp. 1194–1201).Google Scholar
- Koehler, J. (1998). Planning under resource constraints. In H. Prade (Ed.), Proc. 13th European Conference on Artificial Intelligence (ECAI-98 (p. 489–493). Wiley.Google Scholar
- Koehler, J., Nebel, B., & Hoffmann, J. (1997). Extending planning graphs to an ADL subset. In Proc. European Conference on Planning (ECP-97) (p. 273–285). Springer. (extended version as Technical Report No. 88/1997, University Freiburg)Google Scholar
- Laborie, P., & Ghallab, M. (1995). Planning with sharable resource constraints. In Proc. of the 14th IJCAI (p. 1643–1649). Morgan Kaufmann.Google Scholar
- McDermott, D. (1998). PDDL-the planning domain definition language. http://ftp.cs.yale.edu/pub/mcdermott.
- McDermott, D. (2000). The 1998 AI planning systems competition. AI Magazine, 21(2).Google Scholar
- Müller, M. (2000). Integration von Funktionsanwendungen beim zustandsbasierten Planen. diploma thesis, Dep. of Computer Science, TU Berlin.Google Scholar
- Pednault, E. P. D. (1987). Formulating multiagent, dynamic-world problems in the classical planning framework. In M. P. Georgeff & A. L. Lansky (Eds.), Proc. Workshop on Reasoning About Actions and Plans (pp. 47–82). Morgan Kaufmann.Google Scholar
- Schmid, U., & Wysotzki, F. (1998). Induction of recursive program schemes. In Proc. 10th European Conference on Machine Learning (ECML-98) (Vol. 1398, p. 214–225). Springer.Google Scholar
- Schmid, U., & Wysotzki, F. (2000). Applying inductive programm synthesis to macro learning. In Proc. 5th Int.. Conf. on Artificial Intelligence Planning and Scheduling (p. 371–378).Google Scholar
- Sterling, L., & Shapiro, E. (1986). The art of Prolog: Advanced programming techniques. MIT Press.Google Scholar
- Weld, D. (1994). An introduction to least commitment planning. AI Magazine, 15(4), 27–61.Google Scholar