Abstract
As a foundation for goal-directed behavior, the reactive and deliberative systems of a hybrid agent can share a single, unifying representation of intention. In this paper, we present a framework for incorporating dynamical intention into hybrid agents, based on ideas from spreading activation models and belief-desire-intention (BDI) models. In this framework, intentions and other cognitive elements are represented as continuously varying quantities, employed by both sub-deliberative and deliberative processes: On the reactive level, representations support some real-time responsive task re-sequencing; on the deliberative level, representations support common logical reasoning. Because cognitive representations are shared across both levels, inter-level integration is straightforward. Furthermore, dynamical intention is demonstrably consistent with philosophical observations that inform conventional BDI models, so dynamical intentions function as conventional intentions. After describing our framework, we briefly summarize simple demonstrations of our approach, suggesting that dynamical intention-guided intelligence can potentially extend benefits of reactivity without compromising advantages of deliberation in a hybrid agent.
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References
Bratman, M.: Intentions, Plans, and Practical Reason. Harvard University Press, Cambridge (1987)
Georgeff, M., Lansky, A.: Reactive reasoning and planning. In: AAAI 1987, pp. 677–682 (1987)
Rao, A.S., Georgeff, M.P.: Modeling rational agents within a BDI-architecture. In: Proc. of Principles of Knowledge Representation and Reasoning, pp. 473–484 (1991)
Collins, A.M., Loftus, E.F.: A spreading activation theory of semantic priming. Psychological Review 82, 407–428 (1975)
Maes, P.: The dynamics of action selection. In: IJCAI 1989, pp. 991–997 (1989)
Aaron, E., Ivančić, F., Metaxas, D.: Hybrid system models of navigation strategies for games and animations. In: Hybrid Systems: Computation and Control, pp. 7–20 (2002)
Alur, R., Henzinger, T., Lafferriere, G., Pappas, G.: Discrete abstractions of hybrid systems. Proc. of the IEEE 88(7), 971–984 (2000)
Axelsson, H., Egerstedt, M., Wardi, Y.: Reactive robot navigation using optimal timing control. In: American Control Conference (2005)
Aaron, E., Sun, H., Ivančić, F., Metaxas, D.: A hybrid dynamical systems approach to intelligent low-level navigation. In: Proceedings of Computer Animation, pp. 154–163 (2002)
Goldenstein, S., Karavelas, M., Metaxas, D., Guibas, L., Aaron, E., Goswami, A.: Scalable nonlinear dynamical systems for agent steering and crowd simulation. Computers And Graphics 25(6), 983–998 (2001)
Aaron, E., Admoni, H.: Supplementary HAIS 2009 material (2009), http://eaaron.web.wesleyan.edu/hais09_supplement.html
Asarin, E., Dang, T., Girard, A.: Hybridization methods for the analysis of non-linear systems. Acta Informatica 43(7), 451–476 (2007)
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Aaron, E., Admoni, H. (2009). A Framework for Dynamical Intention in Hybrid Navigating Agents. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_3
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DOI: https://doi.org/10.1007/978-3-642-02319-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02318-7
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