HTN-style planning in relational POMDPs using first-order FSCs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7006 LNAI, Issue , 2011, Pages 216-227

  Müller, Felix ^a   Biundo, Susanne ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION HIERARCHY; BACKUP OPERATIONS; FINITE-STATE CONTROLLERS; FIRST-ORDER; HIERARCHICAL PLANNING; HIERARCHICAL TASK NETWORKS; PARTIAL OBSERVABILITY; PARTIALLY OBSERVABLE MARKOV DECISION PROCESS; PLANNING PROBLEM; POLICY GENERATION;

ALGORITHMS; DYNAMIC PROGRAMMING; INTELLIGENT AGENTS; MARKOV PROCESSES; MULTI AGENT SYSTEMS; OBSERVABILITY; POTASSIUM IODIDE;

SOLAR CONCENTRATORS;

EID: 80053982684     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24455-1_20     Document Type: Conference Paper

References (18)

1. Biundo, S., Bercher, P., Geier, T., Müller, F., Schattenberg, B.: Advanced user assistance based on AI planning. In: Cognitive Systems Research, Special Issue on "Complex Cognition", pp. 219-236 (2011)
2. Boger, J., Poupart, P., Hoey, J., Boutilier, C., Fernie, G., Mihailidis, A.: A decision-theoretic approach to task assistance for persons with dementia. In: IJCAI, pp. 1293-1299 (2005)
3. Bouguerra, A., Karlsson, L.: Hierarchical task planning under uncertainty. In: 3rd Italian Workshop on Planning and Scheduling (2004)
4. Erol, K., Hendler, J., Nau, D.S.: UMCP: A sound and complete procedure for hierarchical task-network planning. In: AIPS, pp. 249-254 (1994)
5. Hansen, E.A.: Solving POMDPs by searching in policy space. In: UAI, pp. 211-219 (1998)
6. Hansen, E.A.: Indefinite-horizon POMDPs with action-based termination. In: AAAI, pp. 1237-1242 (2007)
7. Hansen, E.A., Zhou, R.: Synthesis of hierarchical finite-state controllers for POMDPs. In: ICAPS, pp. 113-122 (2003)
8. Madani, O., Hanks, S., Condon, A.: On the undecidability of probabilistic planning and related stochastic optimization problems. Artificial Intelligence 147(1-2), 5-34 (2003)
9. Nau, D., Au, T.-C., Ilghami, O., Kuter, U., Muñoz-Avila, H., Murdock, J.W.,Wu, D., Yaman, F.: Applications of SHOP and SHOP2. In: IEEE Intelligent Systems (2004)
10. Nau, D., Cao, Y., Lotem, A., Munoz-Avila, H.: SHOP: simple hierarchical ordered planner. In: IJCAI, pp. 968-973 (1999)
11. Papadimitriou, C.H., Tsitsiklis, J.N.: The complexity of Markov decision processes. Mathematics of Operations Research 12(3), 441-450 (1987)
12. Pednault, E.P.D.: ADL: exploring the middle ground between STRIPS and the situation calculus. In: KR, pp. 324-332 (1989)
13. Pineau, J., Gordon, G., Thrun, S.: Anytime point-based approximations for large POMDPs. JAIR 27, 335-380 (2006)
14. Puterman,M.L.: Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons, Chichester (1994)
15. Russell, J.A., Mehrabian, A.: Evidence for a three-factor theory of emotions. Journal of Research in Personality 11(3), 273-294 (1977)
16. Sanner, S., Kersting, K.: Symbolic dynamic programming for first-order POMDPs. In: AAAI, pp. 1140-1146 (2010)
17. Wang, C., Khardon, R.: Relational partially observable MDPs. In: AAAI, pp. 1153-1158 (2010)
18. Younes, H.L.S., Littman, M.L.: PPDDL 1.0: an extension to PDDL for expressing planning domains with probabilistic effects. Technical Report CMU-CS-04-167, Carnegie Mellon University, Pittsburgh (2004)