M.T. Cox and B. Kerkez


  1. [1] M. Bauer, Machine learning for user modeling and plan recognition, in V. Moustakis & J. Herrmann (Eds.), Proc. ICML’96Workshop on Machine Learning Meets Human Computer In-teraction, Morgan Kaufman, San Francisco, 1996, 5–16.
  2. [2] E. Charniak & R. Goldman, A Bayesian model of plan recog-nition, Artificial Intelligence, 64, 1993, 53–79. doi:10.1016/0004-3702(93)90060-O
  3. [3] H. Kautz, A formal theory of plan recognition, doctoral diss.,University of Rochester, Rochester, NY, 1987.
  4. [4] N. Lesh & O. Etzioni, Scaling up goal recognition, Proc. 5thInt. Conf. on Principles of Knowledge Representation andReasoning, Cambridge, MA, 1996, 178–189.
  5. [5] B. Kerkez, Incremental case-based plan recognition with in-complete plan libraries, doctoral diss., Wright State University,Dayton, OH, 2003.103
  6. [6] B. Kerkez & M.T. Cox, Incremental case-based plan recogni-tion with local predictions, International Journal on Artifi-cial Intelligence Tools: Architectures, Languages, Algorithms,12 (4), 2003, 413–463. doi:10.1142/S0218213003001307
  7. [7] J.L. Kolodner, Case-based reasoning (San Mateo, CA: MorganKaufmann, 1993).
  8. [8] C.K. Riesbeck & R.C. Schank (Eds.), Inside case-based rea-soning (Hillsdale, NJ: Lawrence Erlbaum Associates, 1989).
  9. [9] D. Leake (Ed.), Case-based reasoning: Experiences, lessons, &future directions (Menlo Park, CA: AAAI Press/MIT Press,1996).
  10. [10] B. Kerkez & M.T. Cox, Case-based plan recognition usingstate indices, in D.W. Aha & I. Watson (Eds.), Case-basedreasoning research and development: Proc. of 4th Int. Conf.on Case-Based Reasoning (Berlin: Springer, 2001), 227–242.
  11. [11] B. Grosz, L. Hunsberger, & S. Kraus, Planning and actingtogether, AI Magazine, 20 (4), 1999, 23–34.
  12. [12] M. Veloso, Planning and learning by analogical reasoning(Berlin: Springer, 1994).
  13. [13] J.G. Carbonell et al., PRODIGY 4.0: The manual and tuto-rial, Technical Report no. CMU-CS-92-150, Carnegie MellonUniversity, Department of Computer Science, Pittsburgh, PA,1992.
  14. [14] R.E. Fikes & N.J. Nilsson, STRIPS: A new approach totheorem proving in problem solving, Artificial Intelligence, 2,1971, 189–208. doi:10.1016/0004-3702(71)90010-5
  15. [15] M. Veloso & J.G. Carbonell, Case-based reasoning inPRODIGY, in R.S. Michalski & G. Tecuci (Eds.), Machinelearning IV: A multistrategy approach (San Francisco: MorganKaufmann, 1994), 523–548.
  16. [16] M. Veloso, J.G. Carbonell, A. Perez, D. Borrajo, E. Fink, &J. Blythe, Integrating planning and learning: The PRODIGYarchitecture, Journal of Theoretical and Experimental ArtificialIntelligence, 7 (1), 1995, 81–120. doi:10.1080/09528139508953801
  17. [17] M. Fagan & P. Cunningham, Case-based plan recognition incomputer games, Proc. 5th Int. Conf. on Case-Based Reasoning(Berlin: Springer, 2003).
  18. [18] M.T. Cox (Ed.), Proc. of the 1999 AAAI-99 Workshop onMixed-Initiative Intelligence (Menlo Park, CA: AAAI Press,1999).

Important Links:

Go Back