Abstract
Case-based planning (CBP) is usually considered a good solution to solve the knowledge acquisition problem that arises when developing AIs for real-time strategy games. Unlike more classical approaches, such as state machines or rule-based systems, CBP allows experts to train AIs directly from games recorded by expert players. Unfortunately, this simple approach has also some drawbacks, for example it is not easy to refine an existing case base to learn specific strategies when a long game session is needed to create a new trace. Furthermore, CBP may be too reactive to small changes in the game state and, at the same time, do not respond fast enough to important changes in the opponent’s strategy. We propose to alleviate these problems by letting experts to inject decision making knowledge into the system in the form of behavior trees, and we show promising results in some experiments using Starcraft.
Supported by the Spanish Ministry of Science and Education (TIN2009-13692-C03-03).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
AIIDE: StarCraft AI competition. As Part of the Program of the Artificial Intelligence and Interactive Digital Entertainment Conference (2010)
Blizzard: Starcraft game (1998), http://us.blizzard.com/en-us/games/sc
Flórez-Puga, G., Llansó, D., Gómez-Martín, M.A., Gómez-Martín, P.P., Díaz-Agudo, B., González-Calero, P.A.: Empowering Designers with Libraries of Self-validated Query-enabled Behaviour Trees. In: Artificial Intelligence for Computer Games, pp. 55–82. Springer, Heidelberg (2011)
Isla, D.: Halo 3 - building a better battle. In: Game Developers Conference (2008)
Krajewski, J.: Creating all humans: A data-driven AI framework for open game worlds. Gamasutra (February 2009)
Lee, G.H.: Rule-based and case-based reasoning approach for internal audit of bank. Know.-Based Syst. 21(2), 140–147 (2008)
Marling, C.R., Whitehouse, P.: Case-based reasoning in the care of alzheimer’s disease patients. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 702–715. Springer, Heidelberg (2001)
Millington, I., Funge, J.: Artificial Intelligence for Games, 2nd edn. Morgan Kaufmann, San Francisco (2009)
Muñoz-Avila, H., Aha, D.W., Nau, D.S., Weber, R., Breslow, L., Yamal, F.: Sin: integrating case-based reasoning with task decomposition. In: IJCAI 2001: Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 999–1004 (2001)
Ontañón, S., Bonnette, K., Mahindrakar, P., Gómez-Martín, M.A., Long, K., Radhakrishnan, J., Shah, R., Ram, A.: Learning from human demonstrations for real-time case-based planning. In: Kuter, U., Muñoz-Avila, H. (eds.) Proceedings of the IJCAI 2009 Workshop on Learning Structural Knowledge From Observations (2009), http://www.cs.umd.edu/~ukuter/struck09/index.html
Ontañón, S., Mishra, K., Sugandh, N., Ram, A.: On-line case-based planning. Computational Intelligence 26(1), 84–119 (2010), http://dx.doi.org/10.1111/j.1467-8640.2009.00344.x
Palma, R., González-Calero, P.A., Gómez-Martín, M.A., Gómez-Martín, P.P.: Extending case-based planning with behavior trees. In: 24th Florida Artificial Intelligence Research Society Conference (to appear, 2011)
Prentzas, J., Hatzilygeroudis, I.: Categorizing approaches combining rule-based and case-based reasoning. Expert Systems 24(2), 97–122 (2007)
Weber, B.: Integrating expert knowledge and experience. In: AAAI Doctoral Consortium (2010)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Palma, R., Sánchez-Ruiz, A.A., Gómez-Martín, M.A., Gómez-Martín, P.P., González-Calero, P.A. (2011). Combining Expert Knowledge and Learning from Demonstration in Real-Time Strategy Games. In: Ram, A., Wiratunga, N. (eds) Case-Based Reasoning Research and Development. ICCBR 2011. Lecture Notes in Computer Science(), vol 6880. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23291-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-23291-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23290-9
Online ISBN: 978-3-642-23291-6
eBook Packages: Computer ScienceComputer Science (R0)