Abstract
In many planning domains there may be multiple potential solutions to a given problem. Each solution may require different resources, involve more or less risk, and result in desirable or undesirable effects. Reuse of historical plans is a strategy that can be employed to solve planning problems. While the retrieval of similar historical plans can be facilitated with sophisticated annotation and search engines, evaluating the usefulness of historical plans tends to be subjective, is context sensitive, and difficult when no single historical plan can be used to develop a new plan. Course of action (COA) evaluation is a method that can be used to compare a set of alternative solutions. An agent-based tool called MICCA (Mixed-Initiative Course of Action Critic Advisors) can aid human operators or software agents in evaluating and adapting historical plans for use in achieving one or more objectives in some current or future hypothetical world state. In this paper we introduce MICCA and describe how case base reasoning (CBR) and generative planning techniques are utilized to support COA evaluation and adaptation.
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References
Long, D., Fox, M.: The 3rd International Planning Competition: Results and Analysis. Artificial Intelligence Journal (AIJ) 20, 1–59 (2003)
Mulvehill, A.M., Benyo, B., Cox, M., Bostwick, R.: Expectation Failure as a Basis for Agent-Based Model Diagnosis and Mixed Initiative Model Adaptation during Anomalous Plan Execution. In: Twentieth International Joint Conference on Artificial Intelligence, Hyderabad, India (2007)
Wagenhals, L.W., Levis, A.H.: Course of Action Development and Evaluation, Defense Technical Information Center (January 2000)
Veloso, M., Mulvehill, A.M., Cox, M.: Rationale-Supported Mixed-Initiative Case-Based Planning. In: IAAI Conference Proceedings (1997)
Ford, A., Carozzoni, J.: Creating and Capturing Expertise in Mixed-Initiative Planning. In: 12th International Command and Control Research and Technology Symposium (12th ICCRTS), Newport, RI, June 19-21 (2007)
Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Com – Artificial Intelligence Communications 7(1), 39–59 (1994)
Mulvehill, A.M., Krisler, B., Bostwick, R.: Deriving Reliable Model Revisions from Executed Plan Data Analysis. In: 14th International Command and Control Research and Technology Symposium, Washington, D.C. (2009)
Nau, D.S., Au, T.C., Ilghami, O., Kuter, U., Muñoz-Avila, H., Murdock, J.W., Wu, D., Yaman, F.: Applications of SHOP and SHOP2. IEEE Intelligent Systems 20(2), 34–41 (2005)
Yaman, F., des Jardins, M.: More-or-Less CP-Networks. In: Uncertainty in Artificial Intelligence, Vancouver, Canada, July 20-22 (2007)
Mitra, R., Basak, J.: Methods of Case Adaptation: A Survey. International Journal of Intelligent Systems 20, 627–645 (2005)
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Mulvehill, A.M., Benyo, B., Yaman, F. (2013). Leveraging Historical Experience to Evaluate and Adapt Courses of Action. In: Delany, S.J., Ontañón, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2013. Lecture Notes in Computer Science(), vol 7969. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39056-2_18
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DOI: https://doi.org/10.1007/978-3-642-39056-2_18
Publisher Name: Springer, Berlin, Heidelberg
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