Abstract
Autonomous agents should formulate and achieve goals with minimum support from humans. Although this might be feasible in a perfectly static world, it is not as easy in the real world where uncertainty is bound to occur. One approach to solving such a problem is to formulate goals based on cases that explain discrepancies observed in the environment. However, in an uncertain world, multiple such cases often apply (i.e., as alternative explanations). Moreover, agents in the real world often have limited resources to achieve their missions. So, it is risky to generate and achieve goals for every applicable explanatory case. Our solution to these problems is to down-select the retrieved cases based on probabilities derived using Bayesian inference, then to monitor the selected cases’ validity based on observed evidence. We evaluate the performance of an agent in an underwater mine clearance domain and compare it to another agent that selects a random case from the candidate set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gogineni, V., Kondrakunta, S., Molineaux, M., Cox, M.T.: Application of case-based explanations to formulate goals in an unpredictable mine clearance domain. In: Proceedings of the ICCBR-2018 Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems, Stockholm, Sweden, pp. 42–51 (2018)
Floyd, M.W., Drinkwater, M., Aha, D.W.: Trust-Guided Behavior Adaptation Using Case-Based Reasoning. Naval Research Laboratory, Washington, United States (2015)
Paisner, M., Cox, M., Maynord, M., Perlis, D.: Goal-driven autonomy for cognitive systems. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 36, no. 36 (2014)
Molineaux, M., Klenk, M., Aha, D.W.: Goal-driven autonomy in a navy strategy simulation. In: AAAI, pp. 1548–1554 (2010)
Dannenhauer, D., Munoz, A.H.: Raising expectations in GDA agents acting in dynamic environments. In: IJCAI, pp. 2241–2247 (2015)
Cox, M.T.: Goal-driven autonomy and question-based problem recognition. In: Proceedings of the 2nd Annual Conference on Advances in Cognitive Systems, Maryland, USA, pp. 29–45 (2013)
Munoz-Avila, H., Aha, D.W., Jaidee, U., Klenk, M., Molineaux, M.: Applying goal driven autonomy to a team shooter game. In: FLAIRS Conference (2010)
Schank, R.C., Kass, A., Riesbeck, C.K.: Inside Case-Based Explanation. Psychology Press, London (2014)
Cox, M.T., Burstein, M.H.: Case-based explanations and the integrated learning of demonstrations. Künstliche Intelligenz (Artif. Intell.) 22(2), 35–38 (2008)
Ram, A.: Indexing, elaboration and refinement: incremental learning of explanatory cases. Mach. Learn. 10, 201–248 (1993)
Schank, R.C.: Explanation Patterns: Understanding Mechanically and Creatively. Psychology Press, London (2013)
Ram, A.: A theory of questions and question asking. J. Learn. Sci. 1(3 and 4), 273–318 (1991)
Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Elsevier, Amsterdam (2004)
de Mántaras, R.L., et al.: Re-trieval, reuse and retention in case-based reasoning. Knowl. Eng. Rev. 20(3), 215–240 (2006)
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–52 (1994)
Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)
Box, G.E., Tiao, G.C.: Bayesian Inference in Statistical Analysis. Wiley, Hoboken (2011)
Maynord, M., Cox, M.T., Paisner, M., Perlis, D.: Data-driven goal generation for integrated cognitive systems. In: 2013 AAAI Fall Symposium Series (2013)
Hanheide, M., et al.: A framework for goal generation and management. In: Proceedings of the AAAI Workshop on Goal-Directed Autonomy (2010)
Kondrakunta, S., Gogineni, V., Molineaux, M., Munoz-Avila, H., Oxenham, M., Cox, M.T.: Toward problem recognition, explanation and goal formulation. In: Proceedings of the 6th Goal Reasoning Workshop at IJCAI/FAIM-2018, Stockholm, Sweden (2018)
Benjamin, M.R., Schmidt, H., Newman, P.M., Leonard, J.J.: Nested autonomy for unmanned marine vehicles with MOOS-IvP. J. Field Robot. 27(6), 834–875 (2010)
Cox, M.T., Ram, A.: Introspective multistrategy learning: on the construction of learning strategies. Artif. Intell. 112(1–2), 1–55 (1999)
Cox, M.T., Alavi, Z., Dannenhauer, D., Eyorokon, V., Munoz-Avila, H., Perlis, D.: MIDCA: a metacognitive, integrated dual-cycle architecture for self-regulated autonomy. In: AAAI (2016)
Schank, R.C., Leake, D.B.: Creativity and learning in a case-based explainer. Artif. Intell. 40(1–3), 353–385 (1989)
Leake, D.B.: Evaluating Explanations: A Content Theory. Psychology Press, London (2014)
Ram, A.: AQUA: Questions that drive the explanation process. Georgia Institute of Technology (1993)
Gentner, D., Forbus, K.: MAC/FAC: A model of similarity-based retrieval. In: Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society, Chicago, IL, pp. 504–550 (1991)
Kendall-Morwick, J., Leake, D.: A study of two-phase retrieval for process-oriented case-based reasoning. In: Montani, S., Jain, L.C. (eds.) Successful Case-Based Reasoning Applications-2, vol. 494, pp. 7–27. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-38736-4_2
Roth-Berghofer, T.R., Cassens, J.: Mapping goals and kinds of explanations to the knowledge containers of case-based reasoning systems. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 451–464. Springer, Heidelberg (2005). https://doi.org/10.1007/11536406_35
Aamodt, A.: Explanation-driven case-based reasoning. In: Wess, S., Althoff, K., Richter, M. (eds.) Topics in Case-Based Reasoning, vol. 837, pp. 274–288. Springer, Berlin (1994). https://doi.org/10.1007/3-540-58330-0_93
Floyd, M.W., Aha, D.W.: Incorporating transparency during trust-guided behavior adaptation. In: Goel, A., Díaz-Agudo, M.Belén, Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS (LNAI), vol. 9969, pp. 124–138. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47096-2_9
Cox, M.T.: A model of planning, action, and interpretation with goal reasoning. Adv. Cogn. Syst. 5, 57–76 (2017)
Acknowledgements
This research was supported by AFOSR under grant FA2386-17-1-4063 and by ONR under grant number N00014-18-1-2009. We thank the anonymous reviews for the comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gogineni, V.R., Kondrakunta, S., Brown, D., Molineaux, M., Cox, M.T. (2019). Probabilistic Selection of Case-Based Explanations in an Underwater Mine Clearance Domain. In: Bach, K., Marling, C. (eds) Case-Based Reasoning Research and Development. ICCBR 2019. Lecture Notes in Computer Science(), vol 11680. Springer, Cham. https://doi.org/10.1007/978-3-030-29249-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-29249-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29248-5
Online ISBN: 978-3-030-29249-2
eBook Packages: Computer ScienceComputer Science (R0)