{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:41:45Z","timestamp":1750308105523,"version":"3.41.0"},"reference-count":19,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2006,10,1]],"date-time":"2006-10-01T00:00:00Z","timestamp":1159660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Comput. Entertain."],"published-print":{"date-parts":[[2006,10]]},"abstract":"<jats:p>Modern, commercial computer games rely primarily on AI techniques that were developed several decades ago, and until recently there has been little impetus to change this. Despite the fact that the computer-controlled agents in such games often possess abilities far in advance of the limits imposed on human participants, competent players are capable of easily beating their artificial opponents, suggesting that approaches based on the analysis and imitation of human play may produce superior agents, in terms of both performance and believability.In this article, we describe our work in imitating the observed goal-oriented behaviors of a human player, based on concepts from data analysis and reinforcement learning. Since even the most intelligent artificial agent will be quickly identified as such if it is observed to move in a robotic manner, we also seek to incorporate mechanisms that will result in believably human-like motion. We then present some illustrative examples, demonstrating the effectiveness of our model. Finally, we discuss future work in this field.<\/jats:p>","DOI":"10.1145\/1178418.1178432","type":"journal-article","created":{"date-parts":[[2007,1,16]],"date-time":"2007-01-16T19:38:29Z","timestamp":1168976309000},"page":"10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Towards integrated imitation of strategic planning and motion modeling in interactive computer games"],"prefix":"10.1145","volume":"4","author":[{"given":"Bernard","family":"Gorman","sequence":"first","affiliation":[{"name":"Dublin City University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Humphrys","sequence":"additional","affiliation":[{"name":"Dublin City University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2006,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Byrne R. W. and Russon A. E. 1998. Learning by imitation: A hierarchical approach. Behavioral and Brain Sciences 21.  Byrne R. W. and Russon A. E. 1998. Learning by imitation: A hierarchical approach. Behavioral and Brain Sciences 21.","DOI":"10.1017\/S0140525X98001745"},{"key":"e_1_2_1_2_1","first-page":"167","volume-title":"CGAIDE","author":"Charles D.","unstructured":"Charles , D. and Mcglinchey , S . 2004. The past, present and future of artificial neural networks in games . In CGAIDE , p. 167 . Charles, D. and Mcglinchey, S. 2004. The past, present and future of artificial neural networks in games. In CGAIDE, p. 167."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 20th ICML Conference.","author":"Elkan C.","year":"2003","unstructured":"Elkan , C. 2003 . Using the triangle inequality to accelerate k-means . In Proceedings of the 20th ICML Conference. Elkan, C. 2003. Using the triangle inequality to accelerate k-means. 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Human Reliability Analysis: Context and Control . Academic Press , London . Hollnagel, E. 1993. Human Reliability Analysis: Context and Control. Academic Press, London."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/860575.860612"},{"volume-title":"Proceedings of the AAAI Fall Symposium.","author":"Laird J. E.","key":"e_1_2_1_9_1","unstructured":"Laird , J. E. and Duchi , J. C . 2000. Creating human-like synthetic characters with multiple skill-levels: A case study using the Soar quakebot . In Proceedings of the AAAI Fall Symposium. Laird, J. E. and Duchi, J. C. 2000. Creating human-like synthetic characters with multiple skill-levels: A case study using the Soar quakebot. In Proceedings of the AAAI Fall Symposium."},{"key":"e_1_2_1_10_1","volume-title":"M","author":"Laird J. E.","year":"2000","unstructured":"Laird , J. E. and V. Lent , M . 2000 . Interactive computer games: Human-level AIs killer app. AAAI, 1171-1178 Laird, J. E. and V. Lent, M. 2000. 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