Abstract
Although evolution has proven to be a powerful search method for discovering effective behaviour for sequential decision-making problems, it seems unlikely that evolving for raw performance could result in behaviour that is distinctly human-like. This chapter demonstrates how human-like behaviour can be evolved by restricting a bot’s actions in a way consistent with human limitations and predilections. This approach evolves good behaviour, but assures that it is consistent with how humans behave. The approach is demonstrated in the \({UT{\char 94}2}\) bot for the commercial first-person shooter videogame Unreal Tournament 2004. \({UT{\char 94}2}\) ’s human-like qualities allowed it to take second place in BotPrize 2010, a competition to develop human-like bots for Unreal Tournament 2004. This chapter analyzes \({UT{\char 94}2}\) , explains how it achieved its current level of humanness, and discusses insights gained from the competition results that should lead to improved human-like bot performance in future competitions and in videogames in general.
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
References
Adobbati, R., Marshall, A.N., Scholer, A., Tejada, S.: Gamebots: a 3D virtual world test-bed for multi-agent research. In: Proceedings of the Second International Workshop on Infrastructure for Agents, MAS, and Scalable MAS (2001)
Bäck, T., Hoffmeister, F., Schwefel, H.P.: A survey of evolution strategies. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 2–9 (1991)
Brooks, R.A.: A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 2(10), 14–23 (1986)
Bryson, J.J.: Intelligence by design: principles of modularity and coordination for engineering complex adaptive agents. Ph.D. thesis, Massachusetts Institute of Technology (2001)
Butz, M., Lonneker, T.: Optimized sensory-motor couplings plus strategy extensions for the TORCS car racing challenge. In: Computational Intelligence and Games, pp. 317–324 (2009)
Darwin, C.: On the Origin of Species by Means of Natural Selection or the Preservation of Favored Races in the Struggle for Life. Murray, London (1859)
Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. PPSN VI, pp. 849–858 (2000)
Fogel, D.B., Atmar, J.W.: Comparing genetic operators with gaussian mutationsin simulated evolutionary processes using linear systems. Biol. Cybern. 63(2), 111–114 (1990)
Gemrot, J., Kadlec, R., Bida, M., Burkert, O., Pibil, R., Havlicek, J., Zemcak, L., Simlovic, J., Vansa, R., Stolba, M., Plch, T.C.B.: Pogamut 3 can assist developers in building AI (not only) for their videogame agents. Agents for Games and Simulations, LNCS, vol. 5920. Springer, Heidelberg (2009)
Gomez, F., Miikkulainen, R.: Active guidance for a finless rocket using neuroevolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 2084–2095. Morgan Kaufmann, San Francisco (2003). http://nn.cs.utexas.edu/keyword?gomez:gecco03
Haykin, S.: Neural Networks, A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)
Hingston, P.: A Turing Test for computer game bots. IEEE Trans. Comput. Intell. AI Games 1(3), 169–186 (2009)
Hingston, P.: A new design for a Turing Test for bots. In: Computational Intelligence and Games (2010)
Isla, D.: Managing complexity in the Halo 2 AI system. In: Proceedings of the Game Developers Conference, San Francisco, CA (2005). http://www.gamasutra.com/gdc2005/features/20050311/isla-01.shtml
Knowles, J., Thiele, L., Zitzler, E.: A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. TIK Report 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich (2006)
Kohl, N., Miikkulainen, R.: Evolving neural networks for strategic decision-making problems. Neural Networks 22, 326–337 (Special issue on Goal-Directed Neural Systems) (2009)
Mouret, J.B., Doncieux, S.: Using behavioral exploration objectives to solve deceptive problems in neuro-evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 627–634. ACM (2009). doi:10.1145/1569901.1569988
Schrum, J., Miikkulainen, R.: Evolving agent behavior in multiobjective domains using fitness-based shaping. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 439–446. Portland, Oregon (2010). http://nn.cs.utexas.edu/?schrum:gecco10
Schrum, J., Miikkulainen, R.: Evolving multi-modal behavior in NPCs. In: Computational Intelligence and Games, pp. 325–332 (2009). http://nn.cs.utexas.edu/?schrum:cig09
Stanley, K.O., Bryant, B.D., Karpov, I., Miikkulainen, R.: Real-time evolution of neural networks in the NERO video game. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence (2006). http://nn.cs.utexas.edu/keyword?stanley:aaai06
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10, 99–127 (2002). http://nn.cs.utexas.edu/keyword?stanley:ec02
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998). http://citeseer.ist.psu.edu/sutton98reinforcement.html
Turing, A.M.: Computing machinery and intelligence. Mind 59(236), 433–460 (1950)
Togelius, J.: Evolution of a subsumption architecture neurocontroller. J. Intell. Fuzzy Syst. 15, 15–20 (2004)
van Hoorn, N., Togelius, J., Schmidhuber, J.: Hierarchical controller learning in a first-person shooter. In: Computational Intelligence and Games, pp. 294–301 (2009)
Waibel, M., Keller, L., Floreano, D.: Genetic team composition and level of selection in the evolution of multi-agent systems. Evol. Comput. 13(3), 648–660 (2009)
Whiteson, S., Stone, P., Stanley, K.O., Miikkulainen, R., Kohl, N.: Automatic feature selection in neuroevolution. In: Proceedings of the Genetic and Evolutionary Computation Conference (2005). http://nn.cs.utexas.edu/keyword?whiteson:gecco05
Zitzler, E., Brockhoff, D., Thiele, L.: The hypervolume indicator revisited: on the design of pareto-compliant indicators via weighted integration. In: Conference on Evolutionary Multi-Criterion Optimization (EMO 2007), vol. 4403, pp. 862–876 (2007)
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Conference on Parallel Problem Solving from Nature, pp. 292–304 (1998)
Acknowledgments
The authors would like to thank Niels van Hoorn for the use of his source code in getting started evolving bots in UT2004. They would also like to thank Christopher Tanguay and Peter Djeu for volunteering to critique and evaluate versions of \({UT{\char 94}2}\) . This research was supported in part by the NSF under grants DBI-0939454 and IIS-0915038 and by the Texas Higher Education Coordinating Board grant 003658-0036-2007.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Schrum, J., Karpov, I.V., Miikkulainen, R. (2013). Human-Like Combat Behaviour via Multiobjective Neuroevolution. In: Hingston, P. (eds) Believable Bots. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32323-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-32323-2_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32322-5
Online ISBN: 978-3-642-32323-2
eBook Packages: Computer ScienceComputer Science (R0)