A Computational Account of Everyday Abductive Inference

Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011

Volumn , Issue , 2011, Pages 2289-2294

  Bridewell, Will ^a   Langley, Pat ^b  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

Author keywords

abduction; cognitive architecture; conceptual inference; plausible inference

Indexed keywords

ABDUCTION; ABDUCTIVE INFERENCE; COGNITIVE ARCHITECTURES; CONCEPTUAL INFERENCE; HUMAN COGNITION; PLAUSIBLE INFERENCE; QUALITATIVE CHARACTERISTICS; RELATED WORKS;

EID: 84856483398     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111, 1036–1060.
2. Appelt, D. E., & Pollack, M. E. (1992). Weighted abduction for plan ascription. User Modeling and User-Adapted Interaction, 2, 1–25.
3. Blaylock, B., & Allen, J. (2005). Generating artificial corpora for plan recognition. Proceedings of the Tenth International Conference on User Modeling (pp. 179–188). Edinburgh: Springer.
4. Cassimatis, N. L., Bello, P., & Langley, P. (2008). Ability, breadth and parsimony in computational models of higher-order cognition. Cognitive Science, 32, 1304–1322.
5. Charniak, E., & Goldman, R. (1991). A probabilistic model of plan recognition. Proceedings of the Conference of the American Association for Artificial Intelligence (pp. 160–165). Anaheim, CA: AAAI Press.
6. Fischer, O., Goel, A., Svirbely, J. R., & Smith, J. W. (1991). The role of essential explanation in abduction. Artificial Intelligence in Medicine, 3, 181–191.
7. Hobbs, J. R., Stickel, M. E., Appelt, D. E., & Martin, P. (1993). Interpretation as abduction. Artificial Intelligence, 63, 69–142.
8. Kate, R. J., & Mooney, R. J. (2009). Probabilistic abduction using Markov logic networks. Proceedings of the Workshop on Plan, Activity, and Intent Recognition.
9. Laird, J. E., Newell, A., & Rosenbloom, P. S. (1987). Soar: An architecture for general intelligence. Artificial Intelligence, 33, 1–64.
10. Langley, P., Choi, D., & Rogers, S. (2009). Acquisition of hierarchical reactive skills in a unified cognitive architecture. Cognitive Systems Research, 10, 316–332.
11. Miller, R. A., Pople, H. E. J., Myers, J. D. (1982). Internist-1: An experimental computer-based diagnostic consultant for general internal medicine. New England Journal of Medicine, 307, 468–476.
12. Newell, A., & Simon, H. A. (1976). Computer science as empirical enquiry: Symbols and search. Communications of the ACM, 19, 113-126.
13. Ng, H. T., & Mooney, R. J. (1992). Abductive plan recognition and diagnosis: A comprehensive empirical evaluation. Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning (pp. 499–508). Cambridge, MA.
14. Peirce, C. S. (1878). Deduction, induction, and hypothesis. Popular Science Monthly, 13, 470–482.
15. Raghavan, S., & Mooney, R. J. (2010). Bayesian abductive logic programs. Proceedings of the AAAI-10 Workshop on Statistical Relational AI (pp. 82–87).
16. Thagard, P. (2007). Coherence, truth, and the development of scientific knowledge. Philosophy of Science, 74, 28–47.