I am a Senior Computer Scientist with BBN Technologies, Cambridge, MA, in the Intelligent Computing Business Unit, Human Centered Systems Group. I am a technical contributor to DARPA/IPTO's Integrated Learning Program. I was previously involved with the deviation analysis portion of DARPA/IXO’s JAGUAR Program. Prior to coming to BBN, I was an Assistant Professor in the Department of Computer Science and Engineering, College of Engineering & Computer Science at Wright State University. I was also director of COLAB², the Collaboration and Cognition Laboratory at Wright State. I finished a Postdoctoral Fellowship in the Computer Science Department at Carnegie Mellon University. I examined mixed-initiative planning in large scale domains using the PRODIGY framework. Before that position, I worked on my PhD at Georgia Tech and developed the Meta-AQUA introspective multistrategy learning system.

I have research interests in multistrategy learning, case-based reasoning , mixed-initiative planning, and computational introspection . In particular, I am interested in how goals affect intelligent behavior across all these processes. 

Research	Teaching
Freeware Tools	Curriculum Vitae
Additional Background	Schedule
1995 AAAI Symposium, Co-Chair     Mental States & Mechanisms	AAAI-99 Workshop, Chair  Mixed-Initiative Intelligence

1. 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.
2. Cox, M. T. (2007a). 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.
3. Cox, M. T. (2007b). Perpetual self-aware cognitive agents. AI Magazine 28(1): 32-45. 
4. Cox, M. T. (2005). Metacognition in computation: A selected research review. Artificial Intelligence. 169(2), 104-141.
   
5. Cox, M. T. (2004). Mixed-initiative case replay. In the Proceedings of the 17th International FLAIRS Conference (pp. 166-171), Menlo Park, CA: AAAI Press.
6. Cox, M., & Kerkez, B. (2006). Case-based plan recognition with novel input. International Journal of Control and Intelligent Systems. 34(2): 96-104.
   
7. Cox, M. T., Munoz-Avila, H., & Bergmann, R. (2006). Case-based planning. Engineering Review. 20(3), 283-287.
   
8. Cox, M. T., & Raja, A. (2007). Metareasoning: A manifesto. BBN Technical Memo TM-2028. Cambridge, MA: BBN Technologies.
9. Cox, M. T., & Ram, A. (1999). Introspective multistrategy learning: On the construction of learning strategies. Artificial Intelligence, 112, 1-55.
10. Cox, M. T., & Zhang, C. (2007). Mixed-initiative goal manipulation. AI Magazine 28(2): 62-73. 
11. Gordon, A. S., Hobbs, J. R., & Cox, M. T. (in press). Anthropomorphic self-models for metareasoning agents. To appear in M. T. Cox & A. Raja (Eds.), Metareasoning: Papers from the 2008 AAAI Workshop. Menlo Park, CA: AAAI Press.
12. Munoz-Avila, H., & Cox, M. T. (in press). Case-based plan adaptation: An analysis and review. IEEE Intelligent Systems.
13. Kerkez, B., & Cox, M. T. (2003). Incremental case-based plan recognition with local predictions . International Journal on Artificial Intelligence Tools: Architectures, languages, algorithms, 12(4), 413-464.
    
14. 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.
15. 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.
    
16. 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. 
17. Santos, E., Deloach, S., & Cox, M. T. (2006). Achieving dynamic multi-commander, multi-mission planning and execution. Applied Intelligence 25(3): 335-357. 
18. 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. 
19. 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.

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