Abstract
Agents are frequently required to perform numerous, complicated interactions with the environment around them, necessitating complex internal representations that are difficult to reason with. We investigate a new direction for optimizing reasoning about long action sequences. The motivation is that a reasoning system can keep a window of executed actions and simplify them before handling them in the normal way, e.g., by updating the internal knowledge base. Our contributions are: (i) we extend previous work to include sensing and non-deterministic actions; (ii) we introduce a framework for performing heuristic search over the space of action sequence manipulations, which allows a form of disjunctive information; finally, (iii) we provide an offline precomputation strategy. Our approach facilitates determining equivalent sequences that are easier to reason with via a new form of search. We demonstrate the potential of this approach over two common domains.
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References
Bacchus, F., Halpern, J.Y., Levesque, H.J.: Reasoning about noisy sensors (and effectors) in the situation calculus. In: Dorst, L., Voorbraak, F., van Lambalgen, M. (eds.) RUR 1995. LNCS, vol. 1093. Springer, Heidelberg (1996)
Chrpa, L., McCluskey, T.L., Osborne, H.: Determining redundant actions in sequential plans. In: ICTAI, pp. 484–491 (2012)
De Giacomo, G., Levesque, H.: An incremental interpreter for high-level programs with sensing. In: Levesque, H., Pirri, F. (eds.) Logical Foundations for Cognitive Agents. Artificial Intelligence, pp. 86–102. Springer, Heidelberg (1999)
Ewin, C., Pearce, A.R., Vassos, S.: Transforming situation calculus action theories for optimised reasoning. In: Proceedings of the Fourteenth International Conference on Knowledge Representation and Reasoning, pp. 448–457 (2014)
Helmert, M.: Domains - ipc-2008, deterministic part (2010). http://ipc.informatik.uni-freiburg.de/Domains (accessed February 13, 2015)
Kowalski, R., Sergot, M.: A logic-based calculus of events. New Gen. Comput. 4(1), 67–95 (1986)
Lin, F., Reiter, R.: How to progress a database. Artificial Intelligence 92(1–2), 131–167 (1997)
Liu, Y., Levesque, H.J.: Tractable reasoning with incomplete first-order knowledge in dynamic systems with context-dependent actions. In: Proceedings of the 19th International Joint Conference on Artificial intelligence, IJCAI 2005, pp. 522–527 (2005)
Löwe, B., Pacuit, E., Witzel, A.: DEL planning and some tractable cases. In: van Ditmarsch, H., Lang, J., Ju, S. (eds.) LORI 2011. LNCS, vol. 6953, pp. 179–192. Springer, Heidelberg (2011)
McCarthy, J., Hayes, P.J.: Some philosophical problems from the standpoint of artificial intelligence. Machine Intelligence 4, 463–502 (1969)
Reiter, R.: Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press (2001)
Russell, S., Norving, P.: Artificial Intelligence: A Modern Approach, second edn. Prentice Hall (2003)
Sardina, S., Vassos, S.: The wumpus world in indigolog: a preliminary report. In: Proceedings the Nonmonotonic Reasoning, Action and Change Workshop at IJCAI (NRAC 2005), pp. 90–95 (2005)
Scherl, R., Levesque, H.J.: Knowledge, action, and the frame problem. Artificial Intelligence 144(1–2), 1–39 (2003)
Shanahan, M.: The event calculus explained. In: Veloso, M.M., Wooldridge, M.J. (eds.) Artificial Intelligence Today. LNCS (LNAI), vol. 1600, pp. 409–430. Springer, Heidelberg (1999)
Thielscher, M.: From situation calculus to fluent calculus: State update axioms as a solution to the inferential frame problem. Artificial Intelligence 111(1–2), 277–299 (1999)
Thielscher, M.: FLUX: A logic programming method for reasoning agents. Theory and Practice of Logic Programing 5(4–5), 533–565 (2004)
Wikipedia: Sokoban - Wikipedia, the free encyclopedia (2015). http://en.wikipedia.org/wiki/Sokoban (accessed February 13, 2015)
Yu, Q., Wen, X., Liu, Y.: Multi-agent epistemic explanatory diagnosis via reasoning about actions. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI 2013, pp. 1183–1190. AAAI Press (2013)
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Ewin, C., Pearce, A.R., Vassos, S. (2015). Optimizing Long-Running Action Histories in the Situation Calculus Through Search. In: Chen, Q., Torroni, P., Villata, S., Hsu, J., Omicini, A. (eds) PRIMA 2015: Principles and Practice of Multi-Agent Systems. PRIMA 2015. Lecture Notes in Computer Science(), vol 9387. Springer, Cham. https://doi.org/10.1007/978-3-319-25524-8_6
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DOI: https://doi.org/10.1007/978-3-319-25524-8_6
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