Michael T. Cox

1. Cox, M. T. (2007). Metareasoning, monitoring, and self-explanation. In A. Raja & M. T. Cox (Eds.), Proceedings of the First International Workshop on Metareasoning in Agent-based Systems (pp. 46-60). AAMAS-07.
2. Cox, M. T. (2007). Perpetual self-aware cognitive agents. AI Magazine 28(1): 32-45.
   
3. Cox, M. T. (2005a). Metacognition in computation: A selected research review. Artificial Intelligence. 169 (2), 104-141.  
4. Cox, M. T. (2005b). Perpetual self-aware cognitive agents. In M. Anderson & T. Oates (Eds.), Proceedings of the AAAI Spring Symposium on Metacognition in Computation (pp. 42-48). Tech. Rep. No. SS-05-04. Menlo Park, CA: AAAI Press.
5. Cox, M. T. (2004a). Mixed-initiative case replay. In the Proceedings of the 17th International FLAIRS Conference (pp. 166-171), Menlo Park, CA: AAAI Press.
6. Cox, M. T. (2004b). Multiagent team formation and collaboration: Final report for the Secure Knowledge Management program. (Tech. Rep. No. WSU-CS-04-01). Dayton, OH: Wright State University, Department of Computer Science and Engineering.
7. Cox, M. T. (2003). Planning as mixed-initiative goal manipulation . In G. Tecuci, D. Aha, M. Boicu, M. T. Cox, G. Ferguson, & A. Tate (Eds.), Proceedings of the Workshop on Mixed-Initiative Intelligent Systems at the 18th International Joint Conference on Artificial Intelligence (pp. 36-41). Menlo Park, CA: International Joint Conference on Artificial Intelligence, Inc.
8. Cox, M. T. (2001). Toward tailored information presentation in support of collaborative planning . In B. Bell  & E. Santos (Eds.), Intent Inference for Collaborative Tasks: Papers from the 2001 fall symposium (pp. 44-50). AAAI Technical Report FS-01-05. Menlo Park, CA: AAAI Press.
9. Cox, M. T. (2000). A conflict of metaphors: Modeling the planning process. In Proceedings of 2000 Summer Computer Simulation Conference (pp. 666-671). San Diego: The Society for Computer Simulation.
10. Cox, M. T. (Ed.) (1999). Proceedings of the AAAI-99 Workshop on Mixed-Initiative Intelligence.Menlo Park, CA: AAAI Press.
11. Cox, M. T. (1998, July). Mixed-initiative planning: Supporting combined human and machine decisions. Slides from a talk presented to the Air Force Research Lab, Information Technology Division, Planning Seminar Series, Rome, NY.
12. Cox, M. T. (1997a). An explicit representation of reasoning failures. In D. B. Leake & E. Plaza (Eds.), Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning (pp. 211-222). Berlin: Springer-Verlag.
13. Cox, M. T. (1997b). Loose coupling of failure explanation and repair: Using learning goals to sequence learning methods. In D. B. Leake & E. Plaza (Eds.), Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning (pp. 425-434). Berlin: Springer-Verlag.
14. Cox, M. T. (1996a). An empirical study of computational introspection: Evaluating introspective multistrategy learning in the Meta-AQUA system. In R. S. Michalski & J. Wnek, (Eds.), Proceedings of the Third International Workshop on Multistrategy Learning (pp. 135-146). Menlo Park, CA: AAAI Press / The MIT Press.
15. Cox, M. T. (1996b). Introspective multistrategy learning: Constructing a learning strategy under reasoning failure. (Tech. Rep. No. GIT-CC-96-06). Doctoral dissertation, Georgia Institute of Technology, College of Computing, Atlanta.
16. Cox, M. T. (1995). Representing mental events (or the lack thereof). In M. T. Cox & M. Freed (Eds.), Proceedings of the 1995 AAAI Spring Symposium on Representing Mental States and Mechanisms (pp. 22-30). Menlo Park, CA: AAAI Press / The MIT Press.
17. Cox, M. T. (1994a). Case-based introspection [Research summary]. In the Proceedings of the Twelfth National Conference on Artificial Intelligence(p. 1435). Cambridge, MA: The MIT Press.
18. Cox, M. T. (1994b). Machines that forget: Learning from retrieval failure of mis-indexed explanations. In Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society (pp. 225-230). Hillsdale, NJ: Lawrence Erlbaum Associates.
19. Cox, M. T. (1994c). Metacognition, problem solving and aging (Cognitive Science Tech. Rep. No. 15). Atlanta: Georgia Institute of Technology, College of Computing.
20. Cox, M. T. (1993). Introspective multistrategy learning. Ph.D. thesis proposal, (Cognitive Science Tech. Rep. No. 2). Atlanta: Georgia Institute of Technology, College of Computing.
21. Cox, M. T., Cabrera, A., Edmonds, A., Moorman, K. & Sawyer, J. (Eds.) (1994). Proceedings of the 1994 Cognitive Science Graduate Student Conference (Cognitive-Science Tech.Rep. No. 4), Atlanta: Georgia Institute of Technology, College of Computing.
22. Cox, M. T., Edwin, G., Balasubramanian, K., & Elahi, M. (2001). Multiagent goal transformation and mixed-initiative planning using Prodigy/Agent. In N. Callaos, B. Sanchez, L. H. Encinas, & J. G. Busse (Eds.), Proceedings of the 5th World Multiconference on Systemics, Cybernetics and Informatics, Vol. VII (pp. 1-6). Orlando, FL: International Institute of Informatics and Systemics.
23. Cox, M. T., Elahi, M., & Cleereman, K. (2003). A distributed planning approach using multiagent goal transformations. In A. Ralescu (Ed.),Proceedings of the 14th Midwest Artificial Intelligence and Cognitive Science Conference (pp. 18-23). Cincinnati: Omnipress.
24. Cox, M. T., & Freed, M. (Eds.). (1995). Proceedings of the 1995 AAAI Spring Symposium on Representing Mental States and Mechanisms (Tech. Rep. No. SS-95-05). Menlo Park, CA: AAAI Press / The MIT Press.
25. Cox, M. T., & Freed, M. (1994). Using knowledge of cognitive behavior to learn from failure. In J. W. Brahan and G. E. Lasker (Eds.), In J. W. Brahan and G. E. Lasker (Eds.), Proceedings of the Seventh International Conference on Systems Research, Informatics and Cybernetics: Vol. 2. Advances in Artificial Intelligence - Theory and Application (pp. 142-147). Windsor, Ontario, Canada: The International Institute for Advanced Studies in Systems Research and Cybernetics.
26. Cox, M. T., Hartrum, T., DeLoach, S., & Narayanan, S. (2002). Agent-based mixed-initiative collaboration: The ABMIC project final report (Tech. Rep. No. WSU-CS-02-01). Dayton, OH: Wright State University, Department of Computer Science and Engineering.
27. Cox, M., & Kerkez, B. (in press). Case-based plan recognition with novel input. To appear in the International Journal of Control and Intelligent Systems.
28. Cox, M., Kerkez, B., Srinivas, C., Edwin, G., Archer, W. (2000). Toward Agent-Based Mixed-Initiative Interfaces.In H. R. Arabnia (Ed.), Proceedings of the 2000 International Conference on Artificial Intelligence , Vol. 1
29. Cox, M. T., Munoz-Avila, H., & Bergmann, R. (2006). Case-based planning. Engineering Review. 20(3), 283-287.
30. Cox, M. T., & Raja, A. (2007). Metareasoning: A manifesto. BBN Technical Memo TM-2028. Cambridge, MA: BBN Technologies.
31. Cox, M. T., & Ram, A. (1999a).  Introspective Multistrategy Learning: On the construction of learning strategies. Artificial Intelligence, 112, 1-55.
32. Cox, M. T., & Ram, A. (1999b).  On the intersection of story understanding and learning. In A. Ram & K. Moorman (Eds.), Understanding language understanding: Computational models of reading (pp. 397-434). Cambridge, MA: MIT Press/Bradford Books.
33. Cox, M. T., & Ram, A. (1995). Interacting learning-goals: Treating learning as a planning task. In J.-P. Haton, M. Keane & M. Manago (Eds.), Advances in case-based reasoning (pp. 60-74). Berlin: Springer-Verlag.
34. Cox, M. T., & Ram, A. (1994a). Choosing learning strategies to achieve learning goals. In M. desJardins & A. Ram (Eds.), Proceedings of the 1994 AAAI Spring Symposium on Goal-Driven Learning (pp. 12-21). Menlo Park, CA: AAAI Press / The MIT Press.
35. Cox, M. T., & Ram, A. (1994b). Failure-driven learning as input bias. In Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society (pp. 231-236). Hillsdale, NJ: Lawrence Erlbaum Associates.
36. Cox, M. T., & Ram, A. (1994c). Managing learning goals in strategy selection problems. In M. Keane, J.-P. Haton, & M. Manago (Eds.), Proceedings of the Second European Workshop on Case-Based Reasoning (pp. 85-93). Paris: AcknoSoft Press.
37. Cox, M. T., & Ram, A. (1992a). Multistrategy learning with introspective meta-explanations. In D. Sleeman & P. Edwards (Eds.), Machine Learning: Ninth International Conference (ML92) (pp. 123-128). San Mateo, CA: Morgan Kaufmann.
38. Cox, M. T., & Ram, A. (1992b). An explicit representation of forgetting. In J. W. Brahan and G. E. Lasker (Eds.), Proceedings of the Sixth International Conference on Systems Research, Infor matics and Cybernetics: Vol. 2. Advances in Artificial Intelligence - Theory and Application (pp. 115-120). Windsor, Ontario, Canada: The International Institute for Advanced Studies in Systems Research and Cybernetics.
39. Cox, M. T., & Ram, A. (1991). Using introspective reasoning to select learning strategies. In R. S. Michalski and G. Tecuci (Eds.), Proceedings of the First International Workshop on Multistrategy Learning (pp. 217-230). Washington, DC: George Mason University, Center for Artificial Intelligence.
40. Cox, M. T., & Veloso, M. M. (1998). Goal transformations in continuous planning . In M. desJardins (Ed.), Proceedings of the 1998 AAAI Fall Symposium on Distributed Continual Planning (pp. 23-30). Menlo Park, CA: AAAI Press / The MIT Press.
41. Cox, M. T., & Veloso, M. M. (1997a). Controlling for unexpected goals when planning in a mixed-initiative setting. In E. Costa & A. Cardoso (Eds.), Progress in Artificial Intelligence: Eighth Portuguese Conference on Artificial Intelligence (pp. 309-318). Berlin: Springer.
42. Cox, M. T., & Veloso, M. M. (1997b). Supporting combined human and machine planning: An interface for planning by analogical reasoning. In D. B. Leake & E. Plaza (Eds.), Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning (pp. 531-540). Berlin: Springer-Verlag.
43. Cox, M. T., & Veloso, M. M. (1997c). Supporting combined human and machine planning: The Prodigy 4.0 User Interface Version 2.0 (Tech, Rep. No. CMU-CS-97-174). Pittsburgh: Carnegie Mellon University, Computer Science Department.
44. Cox, M. T., & Zhang, C. (2007). Mixed-initiative goal manipulation. AI Magazine 28(2): 62-73.  
45. Cox, M. T., & Zhang, C. (2005). Planning as mixed-initiative goal manipulation. In S. Biundo, K. Myers, & K. Rajan (Eds.), Proceedings of the Fifteenth International Conference on Automated Planning and Scheduling (pp. 282-291). Menlo Park, CA: AAAI Press.
46. Cox, M. T., & Zhang, C. (2003). Planning as a mixed-initiative goal manipulation process. Submitted.
47. Brown, S., & Cox, M. (1999). Planning for Information Visualization in Mixed-Initiative Systems. In M. T. Cox (Ed.), Proceedings of the 1999 AAAI-99 Workshop on Mixed-Initiative Intelligence, (pp. 2-10). Menlo Park, CA: AAAI Press.
48. Burstein, M., Brinn, M., Cox, M. T., Hussain, T., Laddaga, R., McDermott, D., McDonald, D., & Tomlinson, R. (2007). An architecture and language for the integrated learning of demonstrations. In M. Burstein & J. Hendler (Eds.), Acquiring Planning Knowledge via Demonstration: Papers from the 2007 AAAI Workshop (pp. 6-11). Technical Report WS-07-02. Menlo Park, CA: AAAI Press.
49. Cheatham, M., & Cox, M. T. (2005a). AI planning in portal-based workflow management systems. In C. Thompson & H. Hexmoor (Eds.), Proceedings of the 2005 International Conference on Integration of Knowledge Intensive Multi-Agent Systems (pp. 47-52). Piscataway, NJ: IEEE Press.
    
50. Cheatham, M., & Cox, M. T. (2005b). AI workflow management in a collaborative environment. In W. McQuay & W. W. Smari (Eds.), Proceedings of the 2005 International Symposium on Collaborative Technologies and Systems (pp. 160-166). Piscataway, NJ: IEEE Press.
51. Cleereman, K., & Cox, M. T. (2004a). Linear inequality control rules in state-space planning. In E. G. Berkowitz (Ed.), Proceedings of the 15th Midwest Artificial Intelligence and Cognitive Science Conference (pp. 148-153). Roosevelt University. Schaumburg, IL.
    
52. Cleereman, K., & Cox, M. T. (2004b). Pathological dependency cycles in state-space planning: When control rules fail. In the Proceedings of the 17th International FLAIRS Conference (pp. 757-762), Menlo Park, CA: AAAI Press.
53. Deckard, M., Narayanan, S., & Cox, M. T. (2000). Natural system metaphors for supporting collaboration in Air Force applications. In Proceedings of the 2000 Human Factors & Ergonomics Society/Industrial Ergonomics Meeting.
54. Edwin, G., & Cox, M. T. (2001b). Resource coordination in single agent and multiagent systems . In Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence (pp. 18-24). Los Alamitos, CA: IEEE Computer Society.
55. Edwin, G., & Cox, M. T. (2000a). COMAS: CoOrdination in MultiAgent Systems.In N. Callaos, B. Sanchez, L. H. Encinas, & J. G. Busse (Eds.), Proceedings of the 5th World Multiconference on Systemics, Cybernetics and Informatics, Vol. VII (pp. 7-12). Orlando, FL: International Institute of Informatics and Systemics.
56. Elahi, M. M., & Cox. M. T. (2005). A multiagent approach to AI planning. In Proceedings of the 8th International Conference on Computer and Information Technology. Islamic University of Technology (IUT), Dhaka, Bangladesh.
    
57. Elahi, M. M., & Cox. M. T. (2003). User's manual for Prodigy/Agent, Ver. 1.0 (Tech. Rep. No. WSU-CS-03-02). Dayton, OH: Wright State University, Department of Computer Scienceand Engineering.
58. Gwinnup, J., & Cox. M. T. (2003). Perceiving and acting upon the user desktop. In Proceedings of the 2nd IASTED International Conference on Information and KnowledgeSharing, (pp.206-211). Calgary, Canada: ACTA Press.
59. Immaneni, T., & Cox, M. T. (2004). GTrans: An application for mixed-initiative collaborative planning during emergency response situations.  In W. W. Smari & W. McQuay (Eds.), Proceedings of the 2004 International Symposium on Collaborative Technologies and Systems (CTS 04), (pp. 121-126). San Diego: Society of Modeling and Simulation International.
60. Kerkez, B., & Cox, M. T. (2003a). Alternate strategies for retrieval in state spaces. In Proceedings of the 16th International Florida Artificial Intelligence Research Society Conference (pp. 119-123). Menlo Park, CA: AAAI Press.
61. Kerkez, B., & Cox, M. T. (2003b). Incremental case-based plan recognition with local predictions .  International Journal on Artificial Intelligence Tools: Architectures, languages, algorithms, 12(4), 413-464..
62. Kerkez, B, & Cox. M. T. (2002a). Case-based plan recognition with incomplete plan libraries. In B. Bell & E. Santos (Eds.), Proceedings of the AAAI Fall 2002 Symposium on Intent Inference (pp. 52-54). Menlo Park, CA: AAAI Press / The MIT Press.
63. Kerkez, B., & Cox, M. T. (2002b). Local predictions for case-based plan recognition (pp. 189-203) In S. Craw & A. Preece (Eds.) Advances in case-based reasoning: 6th European Conference, ECCBR 2002 Proceedings. Berlin: Springer.
64. Kerkez, B., & Cox, M. T. (2001). Incremental Case-Based Plan Recognition Using State Indices. In D. W. Aha, I. Watson, & Q. Yang (Eds.), Case-Based Reasoning Research and Development: Proceedings of the 4th. International Conference on Case-Based Reasoning, ICCBR-2001 (pp. 291-305). Berlin: Springer
65. Kerkez, B, & Cox. M. T. (2000). Planning for the user interface: Window characteristics . In Proceedings of the 11th Midwest Artificial Intelligence and Cognitive Science Conference (pp. 79-84). Menlo Park, CA: AAAI Press.
66. Kerkez, B, Cox. M. T., & Srinivas, C. (2000). Planning for the user interface: Window content . In H. R. Arabnia (Ed.), Proceedings of the 2000 International Conference on Artificial Intelligence , Vol. 1 (pp 345-351). CSREA Press.
67. Kern, S., & Cox. M. T. (2000). A problem representation approach for decision support systems. In Proceedings of the 11th Midwest Artificial Intelligence and Cognitive Science(pp. 68-73). Menlo Park, CA: AAAI Press.
68. Kolodner, J. L., Cox, M. T., & Gonzalez-Calero, P. A. (2006). Case-based reasoning-inspired approaches to education. Knowledge Engineering Review, 20(3), 299-303.
69. Langdon, A., & Cox, M. T. (2004). The effects of team topologies on multiagent planning. In E. G. Berkowitz (Ed.), Proceedings of the 15th Midwest Artificial Intelligence and Cognitive Science Conference (pp. 125-130). Roosevelt University. Schaumburg, IL.
    
70. Lee, P., & Cox, M. T. (2002). Dimensional indexing for targeted case-base retrieval: The SMIRKS system (pp 62-66). In S. Haller & G. Simmons (Eds.) Proceedings of the 15th International Florida Artificial Intelligence Research Society Conference. Menlo Park, CA: AAAI Press.
71. Lopez de Mántaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M. L., Cox, M. T., Forbus, K., Keane, M., Aamodt, A., & Watson, I. (2006). Retrieval, reuse and retention in case-based reasoning. Knowledge Engineering Review, 20(3), 215-240.
72. Mulvehill, A., Benyo, B., Cox, M. T., & Bostwick, R. (2007). Expectation failure as a basis for agent-based model diagnosis and mixed-initiative model adaptation during anomalous plan execution. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (pp. 289-294). Menlo Park, CA: AAAI Press.
73. Mulvehill, A., & Cox, M. (1999). Using Mixed Initiative to Support Force Deployment and Execution. In M. T. Cox (Ed.), Proceedings of the 1999 AAAI-99 Workshop on Mixed-Initiative Intelligence (pp. 119-123). Menlo Park, CA: AAAI Press.
74. Munoz-Avila, H., & Cox, M. T. (in press). Case-based plan adaptation: An analysis and review. IEEE Intelligent Systems.
75. Ram, A., & Cox, M. T. (1994). Introspective reasoning using meta-explanations for multistrategy learning. In R. S. Michalski & G. Tecuci (Eds.), Machine Learning: A multistrategy approach IV (pp. 349-377). San Mateo, CA: Morgan Kaufmann.
76. Ram, A., Cox, M. T., & Narayanan, S. (1995). Goal-driven learning in multistrategy reasoning and learning systems. In A. Ram & D. Leake (Eds.), Goal-driven learning (pp. 421-437). Cambridge, MA: MIT Press/Bradford Books.
77. Ram, A., Cox, M. T., & Narayanan, S. (1992, July). An architecture for integrated introspective learning. In Proceedings on Workshop on Computational Architectures for Supporting Knowledge Acquisition and Learning held at ML-92, Aberdeen, Scotland.
78. Ram, A., Narayanan, S., & Cox, M. T. (1995). Learning to trouble-shoot: Multistrategy learning of diagnostic knowledge for a real-world problem solving task. Cognitive Science, 19(3), 289-340.
79. Santos, E., Deloach, S., & Cox, M. T. (2006). Achieving dynamic multi-commander, multi-mission planning and execution. Applied Intelligence 25(3): 335-357.
    
80. Santos, Jr., E., DeLoach, S., & Cox, M. T. (2003). MADGS: An architecture for dynamic, multi-commander, multi-mission planning and execution (ISIS Laboratory Tech. Rep. No. 105). Storrs, CT: University of Connecticut, Department of Computer Science & Engineering.
81. Tecuci, G., Boicu, M., & Cox, M. T. (2007). Seven aspects of mixed-initiative reasoning: An introduction to the special issue on mixed-initiative assistants. AI Magazine 28(2): 11-18.
82. Tecuci, G., Aha, D., Boicu, M., Cox, M. T., Ferguson, G., & Tate, A. (Eds.) (2003). Proceedings of the Workshop on Mixed-Initiative Intelligent Systems at the 18th InternationalJoint Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press.
83. Veloso, M. M., Mulvehill, A. M., & Cox, M. T. (1997). Rationale-supported mixed-initiative case-based planning. In Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference (pp. 1072-1077). Menlo Park, CA: AAAI Press / The MIT Press.
84. Veloso, M. M., Pollack, M. E., & Cox, M. T. (1998). Rationale-based monitoring for continuous planning in dynamic environments. In R. Simmons, M. Veloso, & S. Smith (Eds.), Proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems (pp. 171-179). Menlo Park, CA: AAAI Press / The MIT Press.
85. Zhang, C., Cox, M. T., & Immaneni, T. (2002). GTrans version 2.1 User manual and reference (Tech. Rep. No. WSU-CS-02-02). Dayton, OH: Wright State University, Departmentof Computer Science and Engineering.