David W. Aha: Publications

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Articles | Papers | Volumes | Chapters | Invites | Reports

Journal Articles

  1. Ladner, R., Petry, F., Gupta, K.M., Warner, E., Moore, P., & Aha, D.W. (2008). Soft computing techniques for web services brokering. Soft Computing, 12, 1089-1098.

  2. Ponsen, M.S.V., Spronck, P., Muñoz-Avila, H., & Aha, D.W. (2006). Automatically generating game tactics through evolutionary learning. AI Magazine, 27(3), 75-84.

  3. Ponsen, M., Spronck, P., Muñoz-Avila, H., & Aha, D.W. (2007). Knowledge acquisition for adaptive game AI. Science of Computer Programming.

  4. Aha, D.W., Molineaux, M., & Ponsen, M. (2006). Learning to win: Case-based plan selection in a real-time strategy game. Künstliche Intelligenz, v1/06, 39-44.

  5. Aha, D.W., Marling, C., & Watson, I. (2005). Case-based reasoning commentaries: Introduction. Knowledge Engineering Review, 20(3), 201-202.

  6. Aha, D.W., McSherry, D., & Yang, Q. (2005). Advances in conversational case-based reasoning. Knowledge Engineering Review, 20(3), 247-254.

  7. Ilghami, O., Nau, D., Muñoz-Avila, H., & Aha, D.W. (2005). Learning preconditions for planning from plan traces and HTN structure. Computational Intelligence, 21(4), 388-413.

  8. Weber, R.O., & Aha, D.W. (2003). Intelligent delivery of military lessons learned, Decision Support Systems, 34, 287-304.

  9. Marling, C., Sqalli, M., Rissland, E., Muñoz-Avila, H., & Aha, D.W. (2002). Case-based reasoning integrations. AI Magazine, 23(1), 69-86.

  10. Aha, D.W., Breslow, L.A., & Muñoz-Avila, H. (2001). Conversational case-based reasoning. Applied Intelligence, 14, 9-32.

  11. Aha, D.W., & Muñoz-Avila, H. (2001). Editorial for the Special Issue on Interactive Case-Based Reasoning. Applied Intelligence, 14, 7-8.

  12. Bazell, D., & Aha, D.W. (2001). Ensembles of classifiers for morphological galaxy classification. Astrophysical Journal, 548, 219-223.

  13. Weber, R., Aha, D.W., & Becerra-Fernandez, I. (2001). Intelligent lessons learned systems. Expert Systems with Applications, 20, 17-34.

  14. Aha, D.W., Becerra-Fernandez, I., Maurer, F., & Muñoz-Avila, H. (2000). Reports on the AAAI 1999 workshop program: Exploring synergies of knowledge management and case-based reasoning. AI Magazine, 21(1), 98.

  15. Muñoz-Avila, H., Hendler, J., & Aha, D.W. (1999). Conversational case-based planning. Review of Applied Expert Systems, 5, 163-174.

  16. Kasif, S., Salzberg, S., Waltz, D., Rachlin, J., & Aha, D.W. (1998). A probabilistic framework for memory-based reasoning. Artificial Intelligence, 104(1-2), 297-312. (NCARAI TR AIC-99-007)

  17. Aha, D.W. (1998). The Omnipresence of Case-Based Reasoning in Science and Application. Knowledge-Based Systems, 11(5-6), 261-273.

  18. Aha, D.W., & Wettschereck, D. (1997). MLnet ECML'97 workshop: Case-based learning: Beyond classification of feature vectors. MLnet News, 5:1, 8-11.

  19. Aha, D. W., & Bankert, R. (1997). Cloud classification using error-correcting output codes. Artificial Intelligence Applications: Natural Resources, Agriculture, and Environmental Science, 11:1, 13-28. (Technical Report AIC-96-024).

  20. Peak, J.E., & Aha, D.W. (1997). AIRIES '96: Workshop on artificial intelligence research in environmental science. AI Applications, 11(1), 103-111.

  21. Aha, D.W. (1997). Editorial on Lazy Learning. Artificial Intelligence Review, 11, 7-10.

  22. Wettschereck, D., Aha, D.W., & Mohri, T. (1997). A review and comparative evaluation of feature weighting methods for lazy learning algorithms. Artificial Intelligence Review, 11, 273-314. Also available as Technical Report AIC-96-006: Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  23. Aha, D. W., & Ram, A. (1996). Summary of the 1995 AAAI Fall Symposium on Adaptation of Knowledge for Reuse. AI Magazine, 17(1), 83-84.

  24. Breslow, L. A., & Aha, D.W. (1997). Simplifying decision trees: A survey. Knowledge Engineering Review, 12, 1-40. (TR: AIC-96-014)

  25. Aha, D.W. (1996). AAAI-94 Workshop Report: Case-Based Reasoning. AI Magazine, 17, 92.

  26. Bankert, R. L., & Aha, D.W. (1996). Improvement to a neural network cloud classifier. Journal of Applied Meteorology, 35, 2036-2039.

  27. Aha, D.W. (1992). Tolerating Noisy, Irrelevant, and Novel Attributes in Instance-Based Learning Algorithms. International Journal of Man-Machine Studies, 36, 267-287.

  28. Aha, D.W., Kibler, D., & Albert, M.K. (1991). Instance-based learning algorithms. Machine Learning, 6, 37-66.

  29. Kibler, D., Aha, D.W., & Albert, M.K. (1989). Instance-based prediction of real-valued attributes. Computational Intelligence, 5, 51-57.

Conferences and Workshops

  1. Gupta, K.M., Zang, M., Gray, A., Aha, D.W., & Kriege, J. (2008). Enabling the interoperability of large-scale legacy systems. Proceedings of the Twentieth Innovative Applications of Artificial Intelligence Conference (pp. 1679-1684). Chicago, IL: AAAI Press.

  2. Zang, M., Gray, A., Kriege, J., Pohl, J., Gupta, K.M., & Aha, D.W. (2008). IMT: A mixed-initiative data mapping and search toolkit. Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (pp. 1890-1891). Chicago, IL: AAAI Press.

  3. Molineaux, M., Aha, D.W., & Moore, P. (2008). Learning continuous action models in a real-time strategy environment. Proceedings of the Twenty-First Florida Artificial Intelligence Research Conference (pp. 257-262). Coconut Grove, FL: AAAI Press. (Winner: Best Paper Award)

  4. Ladner, R., Warner, E., Petry, F., Gupta, K.M., & Aha, D.W. (2007). An evaluation of case-based classification to support automated web service discovery and brokering. In Proceedings of the Defense and Net-Centric Systems Conference. Orlando, FL.

  5. Gupta K.M., & Aha D.W. (2007). Conversation for textual case-based reasoning. In D. Wilson, & D. Khemani (Eds.) Textual Case-Based Reasoning: Beyond Retrieval: Papers from the ICCBR-07 Workshop (Technical Report). Belfast, Northern Ireland.

  6. McSherry, D., & Aha, D.W. (2007). Mixed-initiative relaxation of constraints in critiquing dialogues. Proceedings of the Seventh International Conference on Case-Based Reasoning (pp. 107-121). Belfast, Northern Ireland: Springer.

  7. McDowell, L.K., Gupta, K.M., & Aha, D.W. (2007). Cautious inference in collective classification. In Proceedings of the Twenty-Second National Conference on Artificial Intelligence. Vancouver (BC), Canada: AAAI Press.

  8. McDowell, L.K., Gupta, K.M., & Aha, D.W. (2007). Case-based collective classification. In Proceedings of the Twentieth International FLAIRS Conference. Key West, FL: AAAI Press.

  9. McSherry, D., & Aha, D.W. (2007). The ins and outs of critiquing. Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (pp. 962-967). Hyderabad, India: Professional Book Center.

  10. McSherry, D., & Aha, D.W. (2007). Avoiding long and fruitless dialogues in critiquing. Proceedings of the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 173-186). Cambridge, England: Springer.

  11. Gupta K.M., Aha D.W., & Moore P.G. (2006). Rough set feature selection algorithms for textual case-based classification. Proceedings of the Eighth European Conference on Case-Based Reasoning (pp. 166-181). Ölündeniz, Turkey: Springer. (Nominee: Best Paper Award)

  12. Aha, D.W., Molineaux, M., & Moore, P. (2006). A testbed for evaluating AI research systems in commercial games. Proceedings of the Second Conference on Artificial Intelligence and Interactive Digital Entertainment (pp. 137-138). Marina del Rey, CA: AAAI Press.

  13. Ladner R., Warner, E., Petry, F., Gupta K.M., Moore P.G., Aha D.W., & Shaw, K. (2006). Case-based classification alternatives to ontologies for automated web service discovery and integration. Proceedings of the Defense & Security Symposium. Orlando, FL: International Society for Optical Engineering.

  14. Aha, D.W., & Christman, J. (2006). Enabling and testing autonomous learning behaviors in models of computer-generated forces. Proceedings of the Spring Simulation Interoperability Workshop. Huntsville, AL: Simulation Interoperability Standards Organization.

  15. Gupta, K.M., Aha, D.W., & Moore, P. (2005). Rough set feature selection methods for case-based categorization of text documents. First International Conference on Pattern Recognition and Machine Intelligence (pp. 792-798). Kolkata, India: Springer.

  16. Aha, D.W., Molineaux, M., & Ponsen, M. (2005). Learning to win: Case-based plan selection in a real-time strategy game. Proceedings of the Sixth International Conference on Case-Based Reasoning (pp. 5-20). Chicago, IL: Springer. (Winner: ICCBR'05 Best Paper Award, 2005 NRL Alan Berman Publication Award)

  17. Needels, K., Molineaux, M., & Aha, D.W. (2005). Evaluating case-based systems in virtual games. In D.W. Aha & D.C. Wilson (Eds.) Computer Gaming and Simulation Environments: Proceedings of the ICCBR'05 Workshop (Technical Report). Chicago, IL.

  18. Molineaux, M., Aha, D.W., & Ponsen, M.J.V. (2005). Defeating novel opponents in a real-time strategy game. In D.W. Aha, H. Muñoz-Avila, & M. van Lent (Eds.) Reasoning, Representation, and Learning in Computer Games: Papers from the IJCAI Workshop (Technical Report AIC-05-127). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  19. Ponsen, M.J.V., Lee-Urban, S., Muñoz-Avila, H., Aha, D.W., & Molineaux, M. (2005). Stratagus: An open-source game engine for research in real-time strategy games. In D.W. Aha, H. Muñoz-Avila, & M. van Lent (Eds.) Reasoning Representation, and Learning in Computer Games: Papers from the IJCAI Workshop (Technical Report AIC-05-127). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  20. Ilghami, O., Nau, D., Muñoz-Avila, H., & Aha, D.W. (2005). Learning approximate preconditions for methods in hierarchical plans. Proceedings of the Twenty Second International Conference on Machine Learning. Bonn, Germany: Morgan Kaufmann.

  21. Molineaux, M., & Aha, D.W. (2005). TIELT: A testbed for gaming environments. Proceedings of the Sixteenth National Conference on Artificial Intelligence (pp. 1690-1691). Pittsburgh, PA: AAAI Press.

  22. Ponsen, M.J.V., Muñoz-Avila, H., Spronck P., & Aha D.W. (2005). Automatically acquiring domain knowledge for adaptive game AI using evolutionary learning. Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence (pp. 1535-1540). Pittsburgh, PA: AAAI Press.

  23. Gupta, K.M., & Aha, D.W. (2005). Interpreting events using generative sublanguage ontologies. Proceedings of the Third Workshop on Generative Approaches to the Lexicon. Geneva, Switzerland: University of Geneva.

  24. Gupta, K.M., & Aha, D.W. (2005). Automatic planning using natural language. Proceedings of the Eighteenth International FLAIRS Conference. Miami Beach, FL: AAAI Press.

  25. Gupta, K.M., & Aha, D.W. (2004). Heuristic acronym extraction using linguistic features. In Proceedings of the International Conference on Natural Language Processing. Hyberadad, India: International Institute of Information Technology.

  26. Gupta, K.M., & Aha, D.W. (2004). RuMoP: A rule-based morphotactic parser. In Proceedings of the International Conference on Natural Language Processing. Hyberadad, India: International Institute of Information Technology.

  27. Muñoz-Avila, H., & Aha, D.W. (2004). On the role of explanation for hierarchical case-based planning in real-time strategy games. In P. Gervás & K.M. Gupta (Eds.) Proceedings of the ECCBR 2004 Workshops (Technical Report 142-04). Madrid, Spain: Universidad Complutense Madrid, Departamento di Sistemos Informáticos y Programación.

  28. Gupta, K.M., Aha, D.W., & Moore, P. (2004). Learning feature taxonomies for case indexing. Proceedings of the Seventh European Conference on Case-Based Reasoning (pp. 211-226). Madrid, Spain: Springer.

  29. Aha, D.W., & Molineaux, M. (2004). Integrating learning in interactive gaming simulators. In D. Fu & J. Orkin (Eds.) Challenges of Game AI: Proceedings of the AAAI'04 Workshop (Technical Report WS-04-04). San Jose, CA: AAAI Press.

  30. Molineaux, M., & Aha, D.W. (2004). Evaluating learning techniques in gaming simulators. In Proceedings of the Behavior Representation in Modeling and Simulation Conference (pp. 395-396). Arlington, VA: SISO.

  31. Summers, J.D., McLaren, B.M., & Aha, D.W.et al. (2004). Towards applying case-based reasoning to composable behavior modeling. Proceedings of the Behavior Representation in Modeling and Simulation Conference (pp. 277-284). Arlington, VA: SISO.

  32. Gupta, K.M., & Aha, D.W. (2004). Towards acquiring case indexing taxonomies from text. In Proceedings of the Sixteenth International Conference of the Florida Artificial Intelligence Research Society Miami Beach, FL: AAAI Press.

  33. Horst, J.A., Barbera, A., Schlenoff, C., & Aha, D.W. (2004). Identifying sensory processing requirements for an on-road driving application of 4D/RCS. Proceedings of Mobile Robocis XVII (pp. 73-84). Philadelphia, PA.
  34. Barbera, A., Horst, J., Schlenoff, C., & Aha, D.W. (2004). Task analysis for autonomous on-road driving. Proceedings of Mobile Robotics XVII (pp. 61-72). Philadelphia, PA.

  35. Barbera, A., Horst, J., Schlenoff, C., Wallace, E., & Aha, D.W. (2003). Developing world model data specifications as metrics for sensory processing for on-road driving tasks. Measuring the Performance and Intelligence of Systems: Proceedings of the PerMIS Workshop. Gaithersburg, MD: National Institute of Standards and Technology.

  36. Gupta, K.M., & Aha, D.W. (2003). A framework for incremental query formulation in mixed-initiative case-based reasoning. In D.W.Aha (Ed.) Mixed-Initiative Case-Based Reasoning: Proceedings of the ICCBR'03 Workshop. Trondheim, Norway: Norweigen University of Science and Technology, Department of Computer and Information Science.

  37. Murdock, J.W., Aha, D.W., & Breslow, L.A. (2003). Assessing elaborated hypotheses: An interpretive case-based reasoning approach. Proceedings of the Fifth International Conference on Case-Based Reasoning (pp. 332-346). Trondheim, Norway: Springer.

  38. Gupta, K.M., & Aha, D.W. (2003). Nominal concept representation in sublanguage ontologies. Proceedings of the Second International Workshop on Generative Approaches to the Lexicon. Geneva, Switzerland: University of Geneva

  39. Murdock, J.W., Aha, D.W., & Breslow, L.A. (2003). Case-based argumentation via process models. Proceedings of the Fifteenth International Conference of the Florida Artificial Intelligence Research Society (pp. 134-138). St. Augustine, FL: AAAI Press.

  40. Gupta, K.M., Aha, D.W., & Sandhu, N. (2002). Exploiting taxonomic and causal relations in conversational case retrieval. Proceedings of the Sixth European Conference on Case-Based Reasoning (pp. 133-147). Aberdeen, Scotland: Springer. (Best Research Paper nominee)

  41. Ilghami, O., Nau, D., Muñoz-Avila, H., & Aha, D.W. (2002). CaMeL: Learning methods for HTN planning. Proceedings of the Sixth International Conference on AI Planning and Scheduling. Toulouse, France: AAAI Press.

  42. Gupta, K.M., Aha, D.W., Marsh, E., & Maney, T. (2002). An architecture for engineering sublanguage WordNets. Proceedings of the First International Conference On Global WordNet (pp. 207-215). Mysore, India: Central Institute of Indian Languages.

  43. Weber, R., & Aha, D.W. (2002). Intelligent elicitation of military lessons. In Proceedings of the Sixth Intelligent User Interfaces Conference. San Francisco, CA: ACM.

  44. Aha, D.W., Weber, R., Muñoz-Avila, H., Breslow, L.A., & Gupta, K. (2001). Bridging the lesson distribution gap. Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (pp. 987-992). Seattle, WA: Morgan Kaufmann.

  45. Muñoz-Avila, H., Aha, D.W., Nau, D., Weber, R., Breslow, L.A., &.Yaman, F. (2001). SiN: Integrating case-based reasoning with task decomposition. Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (pp. 999-1004). Seattle, WA: Morgan Kaufmann.

  46. Muñoz-Avila, H., Gupta, K., Aha, D.W., & Nau, D. (2001). Knowledge-based project planning. In R. Dieng & N. Matta (Eds.) Knowledge Management and Organizational Memory: Papers from the IJCAI Workshop. Unpublished workshop proceedings.

  47. Weber, R., Aha, D.W., Sandhu, N., & Muñoz-Avila, H. (2001). A textual case-based reasoning framework for knowledge management applications. Proceedings of the Ninth German Workshop on Case-Based Reasoning. Baden-Baden, Germany: Springer.

  48. Weber, R., Aha, D. W., Muñoz-Avila, H., & Breslow, L.A. (2000). Active delivery for lessons learned systems. Proceedings of the Fifth European Workshop on Case-Based Reasoning (pp. 322-334). Trento, Italy: Springer-Verlag.

  49. Weber, R., Aha, D.W., Branting, L.K., Lucas, J.R., & Becerra-Fernandez, I. (2000). Active case-based reasoning for lessons delivery systems. Proceedings of the Thirteenth International Conference of the Florida Artificial Intelligence Research Society (pp. 170-174). Orlando, FL: AAAI Press.

  50. Weber, R., Aha, D.W., & Becerra-Fernandez, I. (2000). Categorizing intelligent lessons learned systems. In D.W. Aha & R. Weber (Eds.) (2000). Intelligent Lessons Learned Systems: Papers from the 2000 Workshop (Technical Report WS-00-008). Menlo Park, CA: AAAI Press.

  51. Nau, D., Aha, D.W., & Muñoz-Avila, H. (2000). Ordered task decomposition. In Y. Gil & K. Myers (Eds.) Representational Issues for Real-World Planning Systems: Papers from the AAAI Workshop (Technical Report WS-00-18). Menlo Park, CA: AAAI Press.

  52. Muñoz-Avila, H., Aha, D.W., Breslow, L.A., Nau, D., & Weber, R. (2000). Integrating conversational case retrieval with generative planning. European Workshop on Case-Based Reasoning (pp. 210-221). Trento, Italy: Springer-Verlag.

  53. Lucas, J.R., & Aha, D.W. (2000). Roadmap for the Joint Center for Lessons Learned. In D.W. Aha & R. Weber (Eds.) (2000). Intelligent Lessons Learned Systems: Papers from the 2000 Workshop (Technical Report WS-00-008). Menlo Park, CA: AAAI Press.

  54. Knight, C., & Aha, D.W. (2000). A common knowledge framework and lessons learned module. In D.W. Aha & R. Weber (Eds.) (2000). Intelligent Lessons Learned Systems: Papers from the 2000 Workshop (Technical Report WS-00-008). Menlo Park, CA: AAAI Press.

  55. Muñoz-Avila, H., McFarlane, D., Aha, D.W., Ballas, J., Breslow, L.A., & Nau, D. (1999). Using guidelines to constrain interactive case-based HTN planning. Proceedings of the Third International Conference on Case-Based Reasoning (pp. 288--02). Munich, Germany: Springer. (Nominee: Best Research Paper)

  56. Muñoz-Avila, H., Aha, D.W., Breslow, L.A., & Nau, D. (1999). HICAP: An interactive case-based planning architecture and its application to noncombatant evacuation operations. Proceedings of the Ninth Conference on Innovative Applications of Artificial Intelligence (pp. 879-885). Orlando, FL: AAAI Press.

  57. Becerra-Fernandez, I., & Aha, D.W. (1999). Case-based problem solving for knowledge management systems. Proceedings of the Twelvth International Conference of the Florida Artificial Intelligence Research Society (pp. 219-223). Orlando, FL: AAAI Press.

  58. Aha, D.W., Maney, T., & Breslow, L.A. (1998). Supporting dialogue inferencing in conversational case-based reasoning. Fourth European Workshop on Case-Based Reasoning (pp. 262-273). Dublin, Ireland: Springer. (NCARAI Technical Report AIC-98-008)

  59. Aha, D.W. (1998). Textual reasoning in the context of conversational case-based reasoning systems. In M. Lenz & K. Ashley (Eds.) Textual Case-Based Reasoning: Papers from the 1998 Workshop (Technical Report WS-98-12). Menlo Park, CA: AAAI Press.

  60. Ricci, F., & Aha, D.W. (1998). Error-correcting output codes for local learners. Proceedings of the Tenth European Conference on Machine Learning (280-291). Chemnitz, Germany: Springer.

  61. Aha, D. W., & Maney, T. (1997). A model-based approach for supporting dialogue inferencing in a conversational case-based reasoner. In E.~Freuder (Ed.), Multimodal Reasoning: Proceedings of the 1998 AAAI Spring Symposium (Technical Report SS-98-04). Menlo Park, CA: AAAI Press. (NCARAI Technical Report AIC-97-023.)

  62. Aha, D. W., & Breslow, L.A. (1997). Refining conversational case libraries. Proceedings of the Second International Conference on Case-Based Reasoning (pp. 267-278). Providence, RI: Springer-Verlag. (Technical Report AIC-97-004).

  63. Aha, D. W., & Breslow, L.A. (1997). Learning to refine case libraries: Initial results. In D. Wettschereck & D. W. Aha (Eds.) Working Notes for Case-Based Learning: Beyond Classification of Feature Vectors (Technical Report AIC-97-005). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence. (AIC-97-003)

  64. Aha, D.W. (1997). A proposal for refining case libraries. In R. Bergmann & W. Wilke (Eds.) Fifth German Workshop on Case-Based Reasoning: Foundations, Systems, and Applications (Technical Report LSA-97-01E). University of Kaiserslautern, Department of Computer Science.

  65. Breslow, L.A., & Aha, D.W. (1997). Comparing tree-simplification procedures. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics (pp. 67-74). Fort Lauderdale, FL: Unpublished. (TR: AIC-96-015)

  66. Wettschereck, D., & Aha, D.W. (1995). Weighting features. Proceedings of the First International Conference on Case-Based Reasoning (pp. 347-358). Lisbon, Portugal: Springer-Verlag. (Also available as NCARAI TR: AIC-95-026.)

  67. Branting, L.K., & Aha, D.W. (1995). Stratified case-based reasoning: Reusing hierarchical problem solving episodes. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (pp. 384-390). Montreal, Canada: Morgan Kaufmann.

  68. Aha, D. W., & Bankert, R. (1995). A comparative evaluation of sequential feature selection algorithms. In Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics (pp. 1-7). Ft. Lauderdale, FL: Unpublished. (NCARAI TR: AIC-94-026)

  69. Bankert, R.L., & Aha, D.W. (1995). Automated identification of cloud patterns in satellite imagery. Fourteenth Conference on Weather Analysis and Forecasting. Dallas, TX: American Meteorological Society. (NCARAI TR: AIC-94-027)

  70. Aha, D.W., & Aha, D.W. (1994). Feature selection for case-based classification of cloud types: An empirical comparison. In D. W. Aha (Ed.) Case-Based Reasoning: Papers from the 1994 Workshop (Technical Report WS-94-01). Menlo Park, CA: AAAI Press. (NCARAI TR: AIC-94-011)

  71. Aha, D.W., Lapointe, S., Ling, C.X., & Matwin, S. (1994). Learning recursive relations with randomly selected small training sets. Proceedings of the Eleventh International Machine Learning Conference (pp. 12-18). New Brunswick, NJ: Morgan Kaufmann. (NCARAI TR: AIC-94-024)

  72. Rachlin, J., Kasif, S., Salzberg, S., & Aha, D.W. (1994) Towards a better understanding of memory-based and bayesian classifiers. Proceedings of the Eleventh International Machine Learning Conference (pp. 242-250). New Brunswick, NJ: Morgan Kaufmann. (NCARAI TR: AIC-94-012)

  73. Aha, D.W., Lapointe, S., Ling, C.X., & Matwin, S. (1994). Inverting implication with small training sets. Proceedings of the European Conference on Machine Learning (pp. 31-48). Catania, Italy: Springer Verlag. (NCARAI TR: AIC-93-031)

  74. Aha, D.W., Lapointe, S., Ling, C.X., & Matwin, S. (1993). Learning singly-recursive relations from small datasets. Proceedings of the IJCAI-93 Workshop on Inductive Logic Programming. Chambery, France: Unpublished.

  75. Aha, D.W., & Salzberg, S.L. (1993). Learning to catch: Applying nearest neighbor algorithms to dynamic control tasks. Proceedings of the Fourth International Workshop on Artificial Intelligence and Statistics (pp. 363-368). Ft. Lauderdale, FL: Unpublished.

  76. Aha, D.W. (1992). Generalizing from case studies: A case study. Proceedings of the Ninth International Conference on Machine Learning (pp. 1-10). Aberdeen, Scotland: Morgan Kaufmann.

  77. Aha, D.W., & Goldstone, R. L. (1992). Concept learning and flexible weighting. Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society (pp. 534-539). Bloomington, IN: Lawrence Earlbaum.

  78. Bankman, I. N., & Aha, D.W. (1992). Fast learning in feedforward neural networks by migrating hidden unit outputs. In C. H. Dagli (Ed.), Intelligent Engineering Systems Through Artificial Neural Networks, Volume II. St. Louis, MI: ASME Press.

  79. Aha, D.W. (1991). Incremental constructive induction: An instance-based approach. Proceedings of the Eighth International Workshop on Machine Learning (pp. 117-121). Evanston, ILL: Morgan Kaufmann.

  80. Aha, D.W. (1991). Case-based learning algorithms. Proceedings of the DARPA Case-Based Reasoning Workshop (pp. 147-158). Washington, D.C.: Morgan Kaufmann.

  81. Albert, M. K., & Aha, D.W. (1991). Analyses of instance-based learning algorithms. Proceedings of the Ninth National Conference on Artificial Intelligence (pp. 553-558). Anaheim, CA: AAAI Press.

  82. Aha, D.W., & Kibler, D. (1989). Noise-tolerant instance-based learning algorithms. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 794-799). Detroit, MI: Morgan Kaufmann.

  83. Aha, D.W. (1989). Incremental, instance-based learning of independent and graded concept descriptions. Proceedings of the Sixth International Workshop on Machine Learning (pp. 387-391). Ithaca, NY: Morgan Kaufmann.

  84. Kibler, D., & Aha, D.W. (1988). Instance-based prediction of real-valued attributes. Proceedings of the Seventh Biennial Canadian Conference on Artificial Intelligence (pp. 110-116). Edmonton, Alberta: Morgan Kaufmann.

  85. Kibler, D., & Aha, D.W. (1988). Comparing instance-averaging with instance-filtering learning algorithms. Proceedings of the Third European Working Session on Learning (pp. 63-80). Glasgow, Scotland: Pitman.

Edited Volumes

  1. Aha, D.W., & Marling, C. (Eds.) (2005). Special issue on Case-Based Reasoning. Knowledge Engineering Review, 20(3).

  2. Aha, D.W., & Wilson, D.C. (Eds.) (2005). Computer gaming and simulation environments: Proceedings of the ICCBR Workshop (Unpublished Technical Report). In S. Bruninghaus (Ed.) Workshop Proceedings of the Sixth International Conference on Case-Based Reasoning. Chicago, IL: Depaul University.

  3. Aha, D.W., Muñoz-Avila, H., & van Lent, M. (Eds.) (2005). Reasoning, Representation, and Learning in Computer Games: Proceedings of the IJCAI Workshop (Technical Report AIC-05-127). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  4. Tecuci, G., Aha, D., Boicu, M., Cox, M.T., Ferguson, G., & Tate, A. (Eds.) (2003). Mixed-Initiative Intelligent Systems: Papers from the IJCAI Workshop (Technical Report Cl-1). Acapulco, Mexico: AAAI Press. [http://lalab.gmu.edu/miis/proceedings.html]

  5. Aha, D.W. (Ed.) (2003). Mixed-Initiative Case-Based Reasoning: Papers from the ICCBR'03 Workshop (Technical Report). Trondheim, Norway: Norweigen University of Science and Technology, Department of Computer and Information Science.

  6. Aha, D.W. (Ed.) (2002). Mixed-Initiative Case-Based Reasoning: Papers from the ECCBR'02 Workshop (Technical Report). Aberdeen, Scotland: Robert Gordon University, Department of Computing Science.

  7. Aha, D.W., & Watson, I. (Eds.) (2001). Proceedings of the Fourth International Conference on Case-Based Reasoning. Vancouver, Canada: Springer.

  8. Aha, D.W., & Weber, R. (Eds.) (2000). Intelligent Lessons Learned Systems: Papers from the AAAI Workshop (Technical Report WS-W8). Menlo Park, CA: AAAI Press. (Also available as Technical Report AIC-00-005, Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.)

  9. Anand, S.S., Aamodt, A., & Aha, D.W. (1999). Automating the Construction of Case-Based Reasoners: Papers from the IJCAI Workshop (Technical Report ML-5). Unpublished workshop proceedings.

  10. Aha, D.W., Becerra-Fernandez, I., Maurer, F., & Muñoz-Avila, H. (Eds.) (1999). Exploring Synergies of Knowledge Management and Case-Based Reasoning: Papers from the AAAI Workshop (Technical Report WS-99-10). Menlo Park, CA: AAAI Press. (Also available as: Technical Report AIC-99-008: Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.)

  11. Aha, D.W., Daniels, J.J. (Eds.) (1998). Case-Based Reasoning Integrations: Papers from the 1998 Workshop (Technical Report WS-98-15). Menlo Park, CA: AAAI Press. (NCARAI Technical Report AIC-98-011).

  12. Aha, D.W. (Ed.) Lazy Learning. Norwell, MA: Kluwer Academic Publishers.

  13. Wettschereck, D., & Aha, D.W. (Eds.) (1997). ECML-97 MLNet Workshop Notes: Case-Based Learning: Beyond Classification of Feature Vectors (Technical Report AIC-97-005). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  14. Aha, D.W. (Ed.) (1997). Special issue on Lazy Learning. Artificial Intelligence Review, 11:1-5.

  15. Aha, D.W., & Ram, A. (Eds.) (1995). Adaptation of Knowledge for Reuse: Proceedings of the 1995 AAAI Fall Symposium (Technical Report FS-95-02). Menlo Park, CA: AAAI Press.

  16. Aha, D.W., & Riddle, P. (Eds.), (1995). Working Notes for Applying Machine Learning in Practice: A Workshop at the Twelvth International Machine Learning Conference (Technical Report AIC-95-023). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  17. Aha, D.W. (Ed.) (1994). Case-Based Reasoning: Papers from the 1994 Workshop (Technical Report WS-94-01). Menlo Park, CA: AAAI Press.

Book Chapters

  1. Aha, D.W. (2004). Lessons-enabled decision processes. In A. Guillen and T. Iura (Eds.) Using Lessons Learned/Best Practices to Achieve Mission Success (Technical Report TOR-2004(8506)-1). El Segundo, CA: The Aerospace Corporation, Systems Planning and Engineering Group.

  2. Muñoz-Avila, H., Gupta, K., Aha, D.W., & Nau, D. (2002). Knowledge-based project planning. In R. Dieng-Kuntz & N. Matta (Eds.) Knowledge Management and Organizational Memories. Norwell, MA: Kluwer.

  3. Aha, D.W. (1998). Feature weighting for lazy learning algorithms. In H. Liu & H. Motoda (Eds.) Feature Extraction, Construction and Selection: A Data Mining Perspective. Norwell, MA: Kluwer.

  4. Aha, D.W., & Salzberg, S. (1994). Learning to catch: Applying nearest neighbor algorithms to dynamic control tasks. In P. Cheeseman & R. W. Oldford (Eds.), Selecting Models from Data: Artificial Intelligence and Statistics IV. New York, NY: Springer-Verlag. (NCARAI TR: AIC-93-032)

  5. Aha, D.W. (1992). Relating relational learning algorithms. In S. Muggleton (Ed.), Inductive Logic Programming. Academic Press: London.

  6. Kibler, D., & Aha, D.W. (1989). Comparing instance-saving with instance-averaging learning algorithms. In D. P. Benjamin (Ed.) Change of Representation and Inductive Bias. Norwell, MA: Kluwer Academic Publishers.

Invited Papers (Not Refereed)

  1. Aha, D.W. (2008). Object classification in a relational world: A modest review and initial contributions. Proceedings of the Nineteenth Irish Conference on Artificial Intelligence and Cognitive Science (pp. 1). Cork, Ireland: Unpublished.

  2. Aha, D.W. (2005). Conversational case-based reasoning. Proceedings of the First International Conference on Pattern Recognition and Machine Intelligence (p. 30). Kolkata, India: Springer.

  3. Aha, D.W., & Gupta, K.M. (2004). Generative lexicons for extracting concepts from text documents. Proceedings of the Sixth Annual ONR Workshop on Collaborative Decision Support Systems. San Luis Obispo, CA: California Polytechnic Institute, Collaborative Agent Design Research Center.

  4. Aha, D.W., Gupta, K.M., & Murdock, W. (2002). Exploiting taxonomic reasoning in support of real-time mission reachback processes. Proceedings of the Fourth Annual ONR Workshop on Collaborative Decision-Support Systems (pp. 223-240). San Luis Obispo, CA: California Polytechnic Institute, Collaborative Agent Design Research Center.

  5. Aha, D.W., & Gupta, K.M. (2002). Causal query elaboration in conversational case-based reasoning. Proceedings of the Fourteenth International Conference of the Florida Artificial Intelligence Research Society (pp. 95-100). Pensacola, FL: AAAI Press.

  6. Murdock, J.W., & Aha, D.W. (2002). Computational modeling of cognitive processes in plan authoring. Proceedings of the ONR/SPAWAR Workshop on Cognitive Elements of Effective Collaboration. Unpublished proceedings.

  7. Aha, D.W. (1999). The AAAI-99 KM/CBR Workshop: Summary of contributions. In C. Gresse von Wangenheim & C. Tautz (Eds.) Proceedings of the ICCBR-99 Workshop on Practical Case-Based Reasoning Strategies for Building and Maintaining Corporate Memories. Munich, Germany: Unpublished. (NCARAI TR AIC-99-009)

  8. Aha, D.W., & Wettschereck, D. (1997). Case-based learning: Beyond classification of feature vectors. Proceedings of the Ninth European Conference on Machine Learning (pp. 329-336). Prague: Springer.

  9. Aha, D.W. (1993). Integrating machine learning with knowledge-based systems. Proceedings of the First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems (pp. 150-151). Dunedin, NZ: IEEE Press. (NCARAI TR: AIC-93-047)

  10. Kibler, D., & Aha, D.W. (1988). Case-based classification. Proceedings of the Case-Based Reasoning Workshop at AAAI 1988 (pp. 62-67). Unpublished manuscript.

  11. Kibler, D., & Aha, D.W. (1988). Comparing instance-averaging with instance-saving learning algorithms. Proceedings of the First International Workshop on Change of Representation and Inductive Bias (pp. 199-211). Unpublished manuscript.

  12. Kibler, D., & Aha, D.W. (1987). Learning representative exemplars of concepts: An initial case study. Proceedings of the Fourth International Workshop on Machine Learning (pp. 24-30). Irvine, CA: Morgan Kaufmann.

  13. Weiss, V., & Aha, D.W. (1984). Materials selection with logic programming. Proceedings for the 29th Symposium of the Society for Advancement of Material and Process Engineering, Reno, NA.

Technical Reports

  1. Gupta, K.M., & Aha, D.W. (2008). Textual case-based classification with rough set feature selection (Technical Report AIC-08-222). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  2. Gupta, K.M., Aha, D.W., & Petry, F. (2007). Adaptive web services brokering (Technical Note AIC-07-166). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  3. Gupta, K.M., Zang, M., Gray, A., Aha, D.W., & Kriege, J. (2007). Enabling the interoperability of large-scale legacy systems (Technical Note AIC-07-127). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  4. Summers, J.D., McLaren, B.M., & Aha, D.W. (2004). A theoretical analysis on the challenges of applying case-based reasoning for composable behavior modeling (Technical Report AIC-04-001). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  5. Aha, D.W., Breslow, L.A., & Murdock, W. (2003). Managing terrorist activity hypotheses (Technical Report AIC-03-188). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence, Intelligent Decision Aids Group.

  6. Pratt, D.R., McCormack, J., & Aha, D.W. (2003). An application of case-based reasoning for computer generated forces (Technical Note AIC-03-106). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  7. Murdock, J.W., & Aha, D.W. (2002) AHEAD: Case-Based Process Model Explanation of Asymmetric Threats (Technical Report AIC-02-203). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  8. Luxenberg, A., & Aha, D.W. (2002) A brief survey of planning approaches that learn (Technical Report AIC-02-150). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  9. Sandhu, N., & Aha, D.W.(2002). Lesson Elicitation Tool (LET): Technical user's guide (v2.0) (Unpublished Technical Report). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  10. Aha, D.W. (2001). Local lessons learned processes: A radical proposal for sharing lessons within the DoD (Unpublished Technical Report). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.
    - Comments welcome; this is my thesis after a few years studying LLPs

  11. Aha, D.W., & Abramson, M. (2001). On the contribution of availability in modeling affordability as fitness (Technical Report). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  12. Muñoz-Avila, H., Breslow, L.A., & Aha, D.W. (1998). Description and functionality of HTE (Version 2.90) (Technical Report AIC-98-022). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  13. Aha, D.W., & Breslow, L.A. (1998). Comparing simplification procedures for decision trees on an economics classification task (Technical Report NRL/FR/5510--98-9881 and crosslisted at NCARAI as AIC-98-009). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  14. Aha, D.W., & Breslow, L.A. (1998). Correcting for length biasing in conversational case scoring (Technical Report AIC-98-007). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  15. Aha, D.W., Maney, T., & Breslow, L.A. (1998). Supporting conversational case-based reasoning in an integrated reasoning framework (Technical Report AIC-98-006). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  16. Ricci, F., & Aha, D.W. (1997). Bias, variance, and error correcting output codes for local learners. (Technical Report AIC-97-025). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  17. Breslow, L. A., & Aha, D.W. (1997). NaCoDAE: Navy Conversational Decision Aids Environment (Technical Report AIC-97-018). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  18. Ricci, F., & Aha, D.W. (1997). Extending Local Learners with Error-Correcting Output Codes (Technical Report AIC-97-001). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  19. Adams, J. W., & Aha, D.W. (1996). A WWW demonstration of stratified case-based reasoning (Technical Report AIC-96-016). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  20. Aha, D.W., & Chang, L. (1996). Cooperative Bayesian and case-based reasoning for solving multiagent planning tasks (Technical Report AIC-96-005). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  21. Aha, D.W. (1995). An implementation and experiment with the nested generalized exemplars algorithm (Technical Report AIC-95-003). Washington, DC: Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence.

  22. Aha, D.W., & Harrison, P. (1994). Case-based sonogram classification (Technical Report NRL/FR/5510-94-9707). Washington, DC, Naval Research Center, Navy Center for Applied Research in Artificial Intelligence. (Also listed as NCARAI TR: AIC-93-041)

  23. Aha, D.W. (1990). A framework for instance-based learning algorithms: Mathematical, empirical, and psychological evaluations (Technical Report 90-42). Irvine, CA: University of California, Department of Information and Computer Science.

    Abstract: This dissertation introduces a framework for specifying instance-based algorithms that can solve supervised learning tasks. These algorithms input a sequence of instances and yield a partial concept description, which is represented by a set of stored instances and associated information. This description can be used to predict values for subsequently presented instances. The thesis of this framework is that extensional concept descriptions and lazy generalization strategies can support efficient supervised learning behavior.

    The instance-based learning framework consists of three components. The pre-processor component transforms an instance into a more palatable form for the performance component, which computes the instance's similarity with a set of stored instances and yields a prediction for its target value(s). Therefore, the similarity and prediction functions impose generalizations on the stored instances to inductively derive predictions. The learning component assesses the accuracy of these prediction(s) and updates partial concept descriptions to improve their predictive accuracy.

    This framework is evaluated in four ways. First, its generality is evaluated by mathematically determining the classes of symbolic concepts and numeric functions that can be closely approximated by IB1, a simple algorithm specified by this framework. Second, this framework is empirically evaluated for its ability to specify algorithms that improve IB1's learning efficiency. Significant efficiency improvements are obtained by instance-based algorithms that reduce storage requirements, tolerate noisy data, and learn domain-specific similarity functions respectively. Alternative component definitions for these algorithms are empirically analyzed in a set of five high-level parameter studies. Third, this framework is evaluated for its ability to specify psychologically plausible process models for categorization tasks. Results from subject experiments indicate a positive correlation between a models' ability to utilize attribute correlation information and its ability to explain psychological phenomena. Finally, this framework is evaluated for its ability to explain and relate a dozen prominent instance-based learning systems. The survey shows that this framework requires only slight modifications to fit these highly diverse systems. Relationships with edited nearest neighbor algorithms, case-based reasoners, and artificial neural networks are also described.

    Keyphrases: Machine learning, supervised learning tasks, instance-based concept representations, PAC-learning analysis, algorithm development, empirical study, evaluation of algorithm design choices, subject study, evaluation of cognitive models

    This is my dissertation, which can be ordered from the University of California, Irvine (Go Anteaters!). Unfortunately, I no longer know who is the point of contact for this at UCI.

  24. Aha, D.W. (1989). Incremental learning of independent, overlapping, and graded concepts with an instance-based process framework (Technical Report 89-10). Irvine, CA: University of California, Irvine, Department of Information and Computer Science.

    Abstract: Supervised learning algorithms make several simplifying assumptions concerning the characteristics of the concept descriptions to be learned. For example, concepts are often assumed to be (1) defined with respect to the same set of relevant attributes, (2) disjoint in instance space, and (3) have uniform instance distributions. While these assumptions constrain the learning task, they unfortunately limit an algorithm's applicability. We believe that supervised learning algorithms should learn attribute relevancies independently for each concept, allow instances to be members of any subset of concepts, and represent graded concept descriptions. This paper introduces a process framework for instance-based learning algorithms that exploit only specific instance and performance feedback information to guide their concept learning processes. We also introduce Bloom, a specific instantiation of this framework. Bloom is a supervised, incremental, instance-based learning algorithm that learns relative attribute relevancies independently for each concept, allows instances to be members of any subset of concepts, and represents graded concept memberships. We describe empirical evidence to support our claims that Bloom can learn independent, overlapping, and graded concept descriptions.

    Keyphrases: supervised concept learning, knowledge-poor, instance-based concept descriptions, independent concepts, overlapping concepts, graded concepts

    This is not available on-line at this time. Contact UCI for a copy, or me for a hardcopy.