Abstract
Many artificial intelligence techniques have been developed to construct intelligent non-player characters (NPCs) in computer games. As games are gradually becoming an integral part of our life, they require human-like NPCs that shall exhibit believable behavior in the game-play. In this paper, we present an optimization approach to designing believable behavior models for NPCs. We quantify the notion of believability using a multi-objective function, and subsequently convert the achieving of believable behavior into one function optimization problem. We compute its analytical solutions and demonstrate the performance in a practical game.
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References
Amato, C., Shani, G.: High-level reinforcement learning in strategy games. In: Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent (AAMAS), pp. 75–82 (2010)
Bates, J.: Virtual reality, art and entertainment. Presence 1(1), 133–138 (1992)
Champandard, A.J.: Behavior trees for next-gen game ai. Tutorial, AiGameDev.com (2008)
Chang, Y., Maheswaran, R., Levinboim, T., Rajan, V.: Learning and evaluating human-like npc behaviors in dynamic games. In: Proceedings of the Seventh Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), pp. 8–13 (2011)
Cover, T.M., Thomas, J.A.: Elements of information theory. Wiley-Interscience, New York (1991)
Doirado, E., Martinho, C.: I mean it!: detecting user intentions to create believable behaviour for virtual agents in games. In: Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent (AAMAS), pp. 83–90 (2010)
Isla, D.: Handling complexity in the halo 2 ai. In: Proceedings of the Fifteenth Conference on Game Developers Conference (2005)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Statist. 22(1), 79–86 (1951)
Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: An architecture for general intelligence. Artificial Intelligence 33(1), 1–64 (1987)
Langley, P., Choi, D.: A unified cognitive architecture for physical agents. In: Proceedings of the Twenty-First AAAI Conference on Artificial Intelligence (AAAI), pp. 876–881 (2006)
Martinho, C., Paiva, A.: Using anticipation to create believable behaviour. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI), pp. 175–180 (2006)
Rabin, S.: AI Game Programming Wisdom 4. Course Technology (2009)
Scott Neal Reilly, W.: Believable Social and Emotional Agents. PhD thesis, School of Computer Science, Carnegie Mellon University (1996)
Riedl, M.O., Stern, A.: Believable agents and intelligent scenario direction for social and cultural leadership training. In: Proceedings of the Fifteenth Conference on Behavior Representation in Modeling and Simulation (2006)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice-Hall (2003)
Tan, C.T., Cheng, H.: Implant: An integrated mdp and pomdp learning agent for adaptive games. In: Proceedings of the Fifth Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), pp. 94–99 (2009)
Tence, F., Buche, C., De Loor, P., Marc, O.: The challenge of believability in video games: Definitions, agents models and imitation learning. CoRR abs/1009.0451 (2010)
Witten, I.H., Bell, T.C.: The zero-frequency problem: estimating the probabilities of novel events in adaptive text compression. IEEE Transactions on Information Theory 37(4), 1085–1094 (1991)
Xu, J.Z., Laird, J.E.: Combining learned discrete and continuous action models. In: Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI), pp. 1449–1454 (2011)
Zeng, Y., Buus, D.P., Hernandez, J.C.: Multiagent based construction for human-like architecture. In: Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2007), pp. 409–411 (2007)
Zeng, Y., Hernandez, J.C., Buus, D.P.: Swarmarchitect: a swarm framework for collaborative construction. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 186–186 (2007)
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Zeng, Y., Mao, H., Yang, F., Luo, J. (2013). An Optimization Approach to Believable Behavior in Computer Games. In: Cao, L., Zeng, Y., Symeonidis, A.L., Gorodetsky, V.I., Yu, P.S., Singh, M.P. (eds) Agents and Data Mining Interaction. ADMI 2012. Lecture Notes in Computer Science(), vol 7607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36288-0_8
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DOI: https://doi.org/10.1007/978-3-642-36288-0_8
Publisher Name: Springer, Berlin, Heidelberg
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