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Adaptive Goal Driven Autonomy

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Case-Based Reasoning Research and Development (ICCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11156))

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Abstract

Goal-driven autonomy (GDA) is a reflective model of goal reasoning combining deliberative planning and plan execution monitoring. GDA’s is the focus of increasing interest due in part to the need to ensure that autonomous agents behave as intended. However, to perform well, comprehensive GDA agents require substantial domain knowledge. In this paper I focus on our work to automatically learn knowledge used by GDA agents. I also discuss future research directions.

This work is supported in part under ONR N00014-18-1-2009 and NSF 1217888.

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References

  1. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)

    Google Scholar 

  2. Aha, D.W.: Goal reasoning: foundations emerging applications and prospects. AI Mag. Under review (2018)

    Google Scholar 

  3. Choi, D., Langley, P.: Learning teleoreactive logic programs from problem solving. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 51–68. Springer, Heidelberg (2005). https://doi.org/10.1007/11536314_4

    Chapter  Google Scholar 

  4. Coddington, A.M., Luck, M.: A motivation-based planning and execution framework. Int. J. Artif. Intell. Tools 13(01), 5–25 (2004)

    Article  Google Scholar 

  5. Cox, M.T.: Perpetual self-aware cognitive agents. AI Mag. 28(1), 32 (2007)

    Google Scholar 

  6. Dannenhauer, D., Munoz-Avila, H.: LUIGi: a goal-driven autonomy agent reasoning with ontologies. In: Advances in Cognitive Systems (ACS 2013) (2013)

    Google Scholar 

  7. Dannenhauer, D.: Self monitoring goal driven autonomy agents. Ph.D. thesis, Lehigh University (2017)

    Google Scholar 

  8. Dannenhauer, D., Munoz-Avila, H.: Raising expectations in GDA agents acting in dynamic environments. In: IJCAI, pp. 2241–2247 (2015)

    Google Scholar 

  9. Dannenhauer, D., Munoz-Avila, H., Cox, M.T.: Informed expectations to guide GDA agents in partially observable environments. In: IJCAI, pp. 2493–2499 (2016)

    Google Scholar 

  10. Dvorak, D.D., Ingham, M.D., Morris, J.R., Gersh, J.: Goal-based operations: an overview. JACIC 6(3), 123–141 (2009)

    Article  Google Scholar 

  11. Dvorak, D.L., Amador, A.V., Starbird, T.W.: Comparison of goal-based operations and command sequencing. In: Proceedings of the 10th International Conference on Space Operations (2008)

    Google Scholar 

  12. Finestrali, G., Muñoz-Avila, H.: Case-based learning of applicability conditions for stochastic explanations. In: Delany, S.J., Ontañón, S. (eds.) ICCBR 2013. LNCS (LNAI), vol. 7969, pp. 89–103. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39056-2_7

    Chapter  Google Scholar 

  13. Floyd, M.W., Karneeb, J., Aha, D.W.: Case-based team recognition using learned opponent models. In: Aha, D.W., Lieber, J. (eds.) ICCBR 2017. LNCS (LNAI), vol. 10339, pp. 123–138. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61030-6_9

    Chapter  Google Scholar 

  14. Fox, M., Gerevini, A., Long, D., Serina, I.: Plan stability: replanning versus plan repair. In: ICAPS, vol. 6, pp. 212–221 (2006)

    Google Scholar 

  15. Ghallab, M., Nau, D., Traverso, P.: The actor’s view of automated planning and acting: a position paper. Artif. Intell. 208, 1–17 (2014)

    Article  Google Scholar 

  16. Hogg, C., Muñoz-Avila, H., Kuter, U.: HTN-MAKER: learning HTNs with minimal additional knowledge engineering required. In: Conference on Artificial Intelligence (AAAI), pp. 950–956. AAAI Press (2008)

    Google Scholar 

  17. Jaidee, U., Muñoz-Avila, H., Aha, D.W.: Learning and reusing goal-specific policies for goal-driven autonomy. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS (LNAI), vol. 7466, pp. 182–195. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32986-9_15

    Chapter  Google Scholar 

  18. Jaidee, U., Muñoz-Avila, H., Aha, D.W.: Integrated learning for goal-driven autonomy. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence-Volume Volume Three, pp. 2450–2455. AAAI Press (2011)

    Google Scholar 

  19. Jaidee, U., Muñoz-Avila, H., Aha, D.W.: Case-based goal-driven coordination of multiple learning agents. In: Delany, S.J., Ontañón, S. (eds.) ICCBR 2013. LNCS (LNAI), vol. 7969, pp. 164–178. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39056-2_12

    Chapter  Google Scholar 

  20. Keller, T., Eyerich, P.: PROST: probabilistic planning based on UCT. In: ICAPS (2012)

    Google Scholar 

  21. Langley, P.: The cognitive systems paradigm. Adv. Cogn. Syst. 1, 3–13 (2012)

    Google Scholar 

  22. Molineaux, M.: Understanding What May Have Happened in Dynamic, Partially Observable Environments. Ph.D. thesis, George Mason University (2017)

    Google Scholar 

  23. Molineaux, M., Aha, D.W.: Learning unknown event models. In: AAAI, pp. 395–401 (2014)

    Google Scholar 

  24. Molineaux, M., Klenk, M., Aha, D.W.: Goal-driven autonomy in a navy strategy simulation. In: AAAI (2010)

    Google Scholar 

  25. Muñoz-Avila, H., Jaidee, U., Aha, D.W., Carter, E.: Goal-driven autonomy with case-based reasoning. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS (LNAI), vol. 6176, pp. 228–241. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14274-1_18

    Chapter  Google Scholar 

  26. Muñoz-Avila, H., Wilson, M.A., Aha, D.W.: Guiding the ass with goal motivation weights. In: Goal Reasoning: Papers from the ACS Workshop, pp. 133–145 (2015)

    Google Scholar 

  27. Nau, D.S., Cao, Y., Lotem, A., Muñoz-Avila, H.: SHOP: simple hierarchical ordered planner. In: Dean, T. (ed.) International Joint Conference on Artificial Intelligence (IJCAI), pp. 968–973. Morgan Kaufmann, August 1999

    Google Scholar 

  28. Ontanón, S., Synnaeve, G., Uriarte, A., Richoux, F., Churchill, D., Preuss, M.: A survey of real-time strategy game ai research and competition in starcraft. IEEE Trans. Comput. Intell. AI Games 5(4), 293–311 (2013)

    Article  Google Scholar 

  29. Oxenham, M., Green, R.: From direct tasking to goal-driven autonomy for autonomous underwater vehicles. In: 5th Goal Reasoning Workshop at IJCAI 2017 (2017)

    Google Scholar 

  30. Paisner, M., Maynord, M., Cox, M.T., Perlis, D.: Goal-driven autonomy in dynamic environments. In: Goal Reasoning: Papers from the ACS Workshop, p. 79 (2013)

    Google Scholar 

  31. Reifsnyder, N., Munoz-Avila, H.: Goal reasoning with goldilocks and regression expectations in nondeterministic domains. In: 6th Goal Reasoning Workshop at IJCAI/FAIM 2018 (2018)

    Google Scholar 

  32. Reiter, R.: The frame problem in the situation calculus: a simple solution (sometimes) and a completeness result for goal regression. In: Lifschitz, V. (ed.) Artificial Intelligence and Mathematical Theory of Computation: Papers in Honor of John McCarthy, (Ed.). Academic Press (1991)

    Google Scholar 

  33. Roberts, M., et al.: Goal reasoning to coordinate robotic teams for disaster relief. In: Proceedings of ICAPS-15 PlanRob Workshop, pp. 127–138. Citeseer (2015)

    Google Scholar 

  34. Shivashankar, V.: Hierarchical Goal Network Planning: Formalisms and Algorithms for Planning and Acting. Ph.D. thesis, Department of Computer Science, University of Maryland (2015)

    Google Scholar 

  35. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  36. Warfield, I., Hogg, C., Lee-Urban, S., Munoz-Avila, H.: Adaptation of hierarchical task network plans. In: FLAIRS Conference, pp. 429–434 (2007)

    Google Scholar 

  37. Weber, B.G., Mateas, M., Jhala, A.: Applying goal-driven autonomy to starcraft. In: AIIDE (2010)

    Google Scholar 

  38. Wilson, M.A., McMahon, J., Aha, D.W.: Bounded expectations for discrepancy detection in goal-driven autonomy. In: AI and Robotics: Papers from the AAAI Workshop (2014)

    Google Scholar 

  39. Wilson, M.A., Molineaux, M., Aha, D.W.: Domain-independent heuristics for goal formulation. In: FLAIRS Conference (2013)

    Google Scholar 

  40. Zhuo, H.H., Muñoz-Avila, H., Yang, Q.: Learning hierarchical task network domains from partially observed plan traces. Artif. Intell. 212, 134–157 (2014)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This research is supported by ONR under grant N00014-18-1-2009 and by NSF under grant 1217888. No work of this scope can be done by a single person; I will like to thank the following external collaborators: David W. Aha and David Wilson (Naval Research Laboratory), Michael T. Cox and Matthew Molineaux (Wright State University); I will also like to thank the following (current and former) students: Dustin Dannenhauer, Chad Hogg, Sriram Gopalakrishnan, Morgan Fine-Morris, Noah Reifsnyder and Ulit Jaidee.

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Authors and Affiliations

  1. Computer Science and Engineering, Lehigh University, 19 Memorial Drive West, Bethlehem, PA, 18015-3084, USA

    Héctor Muñoz-Avila

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  1. Héctor Muñoz-Avila
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Correspondence to Héctor Muñoz-Avila .

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Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Michael T. Cox

  2. Mälardalen University, Västeras, Sweden

    Peter Funk

  3. Mälardalen University, Västeras, Sweden

    Shahina Begum

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Muñoz-Avila, H. (2018). Adaptive Goal Driven Autonomy. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_1

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  • DOI: https://doi.org/10.1007/978-3-030-01081-2_1

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  • Online ISBN: 978-3-030-01081-2

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