Integrated learning for interactive synthetic characters Export

@inproceedings{citeulike:86241, abstract = {The ability to learn is a potentially compelling and important quality for interactive synthetic characters. To that end, we describe a practical approach to real-time learning for synthetic characters. Our implementation is grounded in the techniques of reinforcement learning and informed by insights from animal training. It simplifies the learning task for characters by (a) enabling them to take advantage of predictable regularities in their world, (b) allowing them to make maximal use of any supervisory signals, and (c) making them easy to train by humans.We built an autonomous animated dog that can be trained with a technique used to train real dogs called "clicker training". Capabilities demonstrated include being trained to recognize and use acoustic patterns as cues for actions, as well as to synthesize new actions from novel paths through its motion space.A key contribution of this paper is to demonstrate that by addressing the three problems of state, action, and state-action space discovery at the same time, the solution for each becomes easier. Finally, we articulate heuristics and design principles that make learning practical for synthetic characters.}, address = {New York, NY, USA}, author = {Blumberg, Bruce and Downie, Marc and Ivanov, Yuri and Berlin, Matt and Johnson, Michael P. and Tomlinson, Bill}, booktitle = {SIGGRAPH '02: Proceedings of the 29th annual conference on Computer graphics and interactive techniques}, citeulike-article-id = {86241}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=566597}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/566570.566597}, doi = {10.1145/566570.566597}, isbn = {1-58113-521-1}, keywords = {ai, animation, autonomous, character\_animation, humanfitnessfunction, learning, memory, natureinspired, optimization}, location = {San Antonio, Texas}, month = {July}, number = {3}, pages = {417--426}, posted-at = {2008-10-29 16:30:19}, priority = {2}, publisher = {ACM}, title = {Integrated learning for interactive synthetic characters}, url = {http://dx.doi.org/10.1145/566570.566597}, volume = {21}, year = {2002} }

TY - CONF ID - citeulike:86241 L3 - citeulike-article-id:86241 N2 - The ability to learn is a potentially compelling and important quality for interactive synthetic characters. To that end, we describe a practical approach to real-time learning for synthetic characters. Our implementation is grounded in the techniques of reinforcement learning and informed by insights from animal training. It simplifies the learning task for characters by (a) enabling them to take advantage of predictable regularities in their world, (b) allowing them to make maximal use of any supervisory signals, and (c) making them easy to train by humans.We built an autonomous animated dog that can be trained with a technique used to train real dogs called "clicker training". Capabilities demonstrated include being trained to recognize and use acoustic patterns as cues for actions, as well as to synthesize new actions from novel paths through its motion space.A key contribution of this paper is to demonstrate that by addressing the three problems of state, action, and state-action space discovery at the same time, the solution for each becomes easier. Finally, we articulate heuristics and design principles that make learning practical for synthetic characters. CY - San Antonio, Texas IS - 3 SN - 1-58113-521-1 AD - New York, NY, USA EP - 426 TI - Integrated learning for interactive synthetic characters PB - ACM VL - 21 T2 - SIGGRAPH '02: Proceedings of the 29th annual conference on Computer graphics and interactive techniques SP - 417 KW - ai KW - animation KW - autonomous KW - character_animation KW - humanfitnessfunction KW - learning KW - memory KW - natureinspired KW - optimization AU - Blumberg, Bruce AU - Downie, Marc AU - Ivanov, Yuri AU - Berlin, Matt AU - Johnson, Michael AU - Tomlinson, Bill PY - 2002/07// UR - http://dx.doi.org/10.1145/566570.566597 ER -