A new representation and associated algorithms for generalized planning

Artificial Intelligence

Volumn 175, Issue 2, 2011, Pages 615-647

  Srivastava, Siddharth ^a   Immerman, Neil ^a   Zilberstein, Shlomo ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

Automated planning; Plan verification; Plans with loops

Indexed keywords

AUTOMATED PLANNING; OPEN PROBLEMS; PLAN VERIFICATION; PLANNING PROBLEM; PLANS WITH LOOPS; PROBLEM INSTANCES;

ALGORITHMS; STATIC ANALYSIS; STRUCTURED PROGRAMMING;

PROBLEM SOLVING;

EID: 78650945131     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2010.10.006     Document Type: Article

References (36)

1. J.F. Allen, N. Chambers, G. Ferguson, L. Galescu, H. Jung, M.D. Swift, W. Taysom, Plow: A collaborative task learning agent, in: Proc. of the 22nd National Conference on Artificial Intelligence, 2007, pp. 22-26.
2. J.A. Baier, C. Fritz, S.A. McIlraith, Exploiting procedural domain control knowledge in state-of-the-art planners, in: Proc. of the 17th International Conference on Automated Planning and Scheduling, 2007, pp. 26-33.
3. B. Bonet, H. Geffner, Planning with incomplete information as heuristic search in belief space, in: Proc. of the 6th International Conference on Artificial Intelligence Planning and Scheduling, 2000, pp. 52-61.
4. B. Bonet, H. Palacios, H. Geffner, Automatic derivation of memoryless policies and finite-state controllers using classical planners, in: Proc. of the 19th International Conference on Artificial Intelligence Planning and Scheduling, 2009, pp. 34-41.
5. A. Cimatti, M. Pistore, M. Roveri, and P. Traverso Weak, strong, and strong cyclic planning via symbolic model checking Artificial Intelligence 147 1-2 2003 35 84
6. A. Cimatti, M. Roveri, P. Traverso, Automatic OBDD-based generation of universal plans in non-deterministic domains, in: Proc. of the Fifteenth National Conference on Artificial Intelligence, 1998, pp. 875-881.
7. B. Cook, A. Podelski, A. Rybalchenko, Termination proofs for systems code, in: Proc. of the 2006 ACM SIGPLAN Conference on Programming Language Design and Implementation, 2006, pp. 415-426.
8. G. Dejong, and R. Mooney Explanation-based learning: An alternative view Machine Learning 1 2 1986 145 176
9. S. Eker, T.J. Lee, M. Gervasio, Iteration learning by demonstration, in: Papers from AAAI 2009 Spring Symposium on Agents that Learn from Human Teachers, 2009.
10. K. Erol, J. Hendler, D.S. Nau, HTN planning: Complexity and expressivity, in: Proc. of the 12th National Conference on Artificial Intelligence, 1994, pp. 1123-1128.
11. Z. Feng, E.A. Hansen, Symbolic heuristic search for factored Markov decision processes, in: Proc. of the 18th National Conference on Artificial Intelligence, 2002, pp. 455-480.
12. A. Fern, S. Yoon, and R. Givan Approximate policy iteration with a policy language bias: Solving relational Markov decision processes Journal of Artificial Intelligence Research 25 2006 85 118
13. R. Fikes, P. Hart, N. Nilsson, Learning and executing generalized robot plans, Tech. rep., AI Center, SRI International, 1972.
14. T.A. Henzinger, R. Jhala, R. Majumdar, G. Sutre, Lazy abstraction, in: Proc. of the 29th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, 2002, pp. 58-70.
15. J. Hoey, R. St-Aubin, A. Hu, C. Boutilier, SPUDD: Stochastic planning using decision diagrams, in: Proc. of the 15th Conference on Uncertainty in Artificial Intelligence, 1999, pp. 279-288.
16. J. Hoffmann, R.I. Brafman, Contingent planning via heuristic forward search with implicit belief states, in: Proc. of the 15th International Conference on Automated Planning and Scheduling, 2005, pp. 71-80.
17. J. Hoffmann, A. Sabharwal, C. Domshlak, Friends or foes? An AI planning perspective on abstraction and search, in: Proc. of the 16th International Conference on Automated Planning and Scheduling, 2006, pp. 415-469.
18. C. Hogg, H. Munoz-Avila, U. Kuter, HTN-MAKER: Learning HTNs with minimal additional knowledge engineering required, in: Proc. of the 23rd National Conference on Artificial Intelligence, 2008, pp. 950-956.
19. C.A. Knoblock, Search reduction in hierarchical problem solving, in: Proc. of the 9th National Conference on Artificial Intelligence, 1991, pp. 686-691.
20. T. Lau, S.A. Wolfman, P. Domingos, and D.S. Weld Programming by demonstration using version space algebra Machine Learning 53 1-2 2003 111 156
21. T.A. Lau, L.D. Bergman, V. Castelli, D. Oblinger, Sheepdog: Learning procedures for technical support, in: Proc. of the 9th International Conference on Intelligent User Interfaces, 2004, pp. 109-116.
22. H.J. Levesque, Planning with loops, in: Proc. of the 19th International Joint Conference on Artificial Intelligence, 2005, pp. 509-515.
23. H.J. Levesque, F. Pirri, and R. Reiter Foundations for the situation calculus Electronic Transactions on Artificial Intelligence 2 1998 159 178
24. H.J. Levesque, R. Reiter, Y. Lesperance, F. Lin, and R.B. Scherl GOLOG: A logic programming language for dynamic domains Journal of Logic Programming 31 1997 59 83
25. M.A. Peot, D.E. Smith, Conditional nonlinear planning, in: Proceedings of the 1st International Conference on Artificial Intelligence Planning Systems, 1992, pp. 189-197.
26. A. Podelski, A. Rybalchenko, A complete method for the synthesis of linear ranking functions, in: Proc. of VMCAI 2004: Verification, Model Checking, and Abstract Interpretation, 2004, pp. 239-251.
27. T. Reps, M. Sagiv, A. Loginov, Finite differencing of logical formulas for static analysis, in: Proc. of European Symposium on Programming, 2003, pp. 380-398.
28. M. Sagiv, T. Reps, and R. Wilhelm Parametric shape analysis via 3-valued logic ACM Transactions on Programming Languages and Systems 24 3 2002 217 298
29. J.W. Shavlik Acquiring recursive and iterative concepts with explanation-based learning Machine Learning 5 1990 39 70
30. L. Spalazzi A survey on case-based planning Artificial Intelligence Review 16 1 2001 3 36
31. S. Srivastava, N. Immerman, S. Zilberstein, Learning generalized plans using abstract counting, in: Proc. of the 23rd National Conference on AI, 2008, pp. 991-997.
32. S. Srivastava, N. Immerman, S. Zilberstein, Computing applicability conditions for plans with loops, in: Proc. of the 20th International Conference on Automated Planning and Scheduling, 2010, pp. 161-168.
33. S. Srivastava, N. Immerman, S. Zilberstein, Merging example plans into generalized plans for non-deterministic environments, in: Proc. of the 9th International Conference on Autonomous Agents and Multiagent Systems, 2010, pp. 1341-1348.
34. E. Winner, M. Veloso, DISTILL: Learning domain-specific planners by example, in: Proc. of the 20th International Conference on Machine Learning, 2003, pp. 800-807.
35. E. Winner, M. Veloso, LoopDISTILL: Learning domain-specific planners from example plans, in: Workshop on AI Planning and Learning, in conjunction with ICAPS-07, 2007.
36. F. Yaman, T. Oates, Workflow inference: What to do with one example and no semantics, in: Proc. of the AAAI Workshop on Acquiring Planning Knowledge via Demonstration, 2007.