RT Conference Proceedings
T1 Combining expert knowledge and learning from demonstration in real-time strategy games
A1 Palma, Ricardo
A1 Sánchez Ruiz-Granados, Antonio Alejandro
A1 Gómez Martín, Marco Antonio
A1 Gómez Martín, Pedro Pablo
A1 González Calero, Pedro Antonio
A2 Ram, Ashwin
A2 Wiratunga, Nirmalie
AB 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.
SN 9783642232909
SN 9783642232916
SN 0302-9743
SN 1611-3349
YR 2011
FD 2011-09-12
LK https://hdl.handle.net/20.500.14352/133555
UL https://hdl.handle.net/20.500.14352/133555
LA eng
NO 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
DS Docta Complutense
RD 18 mar 2026