Increasing the runtime speed of case-based plan recognition

Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2015

Volumn , Issue , 2015, Pages 385-390

  Maynord, Michael ^a   Vattam, Swaroop ^b,c   Aha, David W ^c  

^a UNIVERSITY OF MARYLAND   (United States)

^b NRC Postdoctoral Research Fellow   (United States)

^c NAVAL RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; ITERATIVE METHODS;

ABSTRACTION HIERARCHY; DOMAIN KNOWLEDGE; EMPIRICAL PERFORMANCE; EUCLIDEAN SPACES; PLAN RECOGNITION; RANDOM DISTRIBUTION; RETRIEVAL ACCURACY; ROBOTS WORKING;

CASE BASED REASONING;

EID: 84958225274     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Aha, D. W., and Breslow, L. A. 1997. Refining conversational case libraries. In Proceedings of the Second International Conference on Case-Based Reasoning, 267-278. Springer.
2. Bergmann, R., and Wilke, W. 1996. On the role of abstraction in case-based reasoning. In Proceedings of the Third European Workshop on Case-Based Reasoning, 28-43. Springer.
3. Branting, L. K., and Aha, D. W. 1995. Stratified case-based reasoning: Reusing hierarchical problem solving episodes. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 384-390. Morgan Kaufmann.
4. Cox, M. T., and Kerkez, B. 2006. Case-based plan recognition with novel input. Control and Intelligent Systems 34:96-104.
5. Cox, M. T.; Munoz-Avila, H.; and Bergmann, R. 2005. Case-based planning. Knowledge Engineering Review 20:283-287.
6. Daelemans, W.; van den Bosch, A.; and Weijters, T. 1997. Igtree: Using trees for compression and classification in lazy learning algorithms. Artificial Intelligence Review 11:407-432.
7. Fagan, M., and Cunningham, P. 2003. Case-based plan recognition in computer games. In Proceedings of the Fifth International Conference on Case-Based Reasoning, 161-170. Springer.
8. Johnson, M. 1985. Relating metrics, lines and variables defined on graphs to problems in medicinal chemistry. In Alavi, Y.; Chartrand, G.; Lick, D.; Wall, C.; and Lesniak, L., eds., Graph theory with applications to algorithms and computer science. John Wiley & Sons.
9. Kambhamapti, S., and Hendler, J. A. 1992. A validationstructure-based theory of plan modifications. Artificial Intelligence 55:193-258.
10. Kautz, H., and Allen, J. F. 1986. Generalized plan recognition. In Proceedings of the Fifth National Conference on Artificial Intelligence, 32-38. Morgan Kaufmann.
11. Knoblock, C. 1994. Automatically generating abstractions for planning. Artificial Intelligence 68:243-302.
12. Kruskal, J. 1964. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1):1-27.
13. Lopez de Mantaras, R.; McSherry, D.; Bridge, D.; Leake, D.; Smyth, B.; Craw, S.; Faltings, B.; Maher, M. L.; Cox, M. T.; Forbus, K.; et al. 2005. Retrieval, reuse, revision and retention in case-based reasoning. Knowledge Engineering Review 20(03):215-240.
14. Molineaux, M.; Aha, D.W.; and Sukthankar, G. 2009. Beating the defense: Using plan recognition to inform learning agents. In Proceedings of the Twenty-Second International FLAIRS Conference, 337-343. AAAI Press.
15. Müller, G., and Bergmann, R. 2014. A cluster-based approach to improve similarity-based retrieval for processoriented case-based reasoning. In Proceedings of the Twentieth European Conference on Artificial Intelligence, 639-644. IOS Press.
16. Nau, D. S.; Au, T.-C.; Ilghami, O.; Kuter, U.; Murdock, J.W.;Wu, D.; and Yaman, F. 2003. Shop2: An htn planning system. Journal of Artificial Intelligence Research 20:379-404.
17. Nau, D. 2013. Game applications of HTN planning with state variables. In Buro, M.; Eric Jacopin; and Vassos, S., eds., Planning in Games: Papers from the ICAPS Workshop.
18. Sacerdoti, E. D. 1974. Planning in a hierarchy of abstraction spaces. Artificial intelligence 5:115-135.
19. Sanchez-Ruiz, A. A., and Ontanon, S. 2014. Least common subsumer trees for plan retrieval. In Proceedings of the Twenty-Second International Conference on Case-Based Reasoning, 405-419. Springer.
20. Smyth, B., and Cunningham, P. 1992. Deja vu: A hierarchical case-based reasoning system for software design. In Proceedings of the Tenth European Conference on Artificial Intelligence, 587-589. Wiley and Sons.
21. Tecuci, D., and Porter, B. W. 2009. Memory based goal schema recognition. In Proceedings of the Twenty-Second International FLAIRS Conference, 111-116. AAAI Press.
22. Vattam, S.; Aha, D. W.; and Floyd, M. 2014. Case-based plan recognition using action sequence graphs. In Proceedings of the Twenty-Second International Conference on Case-Based Reasoning, 495-510. Springer.
23. Wess, S.; Althoff, K.-D.; and Derwand, G. 1993. Using k-d trees to improve the retrieval step in case-based reasoning. In First European Workshop on Case-Based Reasoning, 167-181. Springer.