{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:13:21Z","timestamp":1777522401698,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[1994,6,1]],"date-time":"1994-06-01T00:00:00Z","timestamp":770428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[1994,6]]},"abstract":"<jats:p>Learning to control dynamic systems with unknown models is a challenging research problem. However, most previous work that learns qualitative control rules does not construct qualitative states; a proper partition of continuous- state variables has to be designed by human users and given to the learning programs. We design a new learning method that learns appropriate qualitative state representation and the control rules simultaneously. Our method can aggressively partition the continuous-state variables into finer, discrete ranges until control rules based on these ranges are learned. As a case study, we apply our method to the benchmark control problem of cart-pole balancing (also known as the inverted pendulum). Experimental results show that our method not only derives different partitions for the cart-pole systems with different parameters but also learns to control the systems for an extended period of time from random initial positions.<\/jats:p>","DOI":"10.1177\/105971239400300103","type":"journal-article","created":{"date-parts":[[2007,3,11]],"date-time":"2007-03-11T01:45:25Z","timestamp":1173577525000},"page":"29-49","source":"Crossref","is-referenced-by-count":1,"title":["Learning to Control Dynamic Systems with Automatic Quantization"],"prefix":"10.1177","volume":"3","author":[{"given":"Charles X.","family":"Ling","sequence":"first","affiliation":[{"name":"University of Western Ontario"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ralph","family":"Buchal","sequence":"additional","affiliation":[{"name":"University of Western Ontario"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[1994,6,1]]},"reference":[{"key":"atypb1","volume-title":"Learning and problem solving with multilayer connectionist systems","author":"Anderson, C.","year":"1986"},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1109\/37.24809"},{"key":"atypb3","volume-title":"A set of challenging control problems","author":"Anderson, C.W.","year":"1990"},{"key":"atypb4","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1983.6313077"},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(92)90020-Z"},{"key":"atypb6","volume-title":"Proceedings of the Sixth Conference on Uncertainty in AI","author":"Berenji, H."},{"key":"atypb7","volume-title":"Dynamics of physical systems","author":"Cannon Jr., R.H.","year":"1967"},{"key":"atypb8","volume-title":"Proceedings of the Twelfth International Joint Conference on Artificial Intelligence","author":"Chapman, 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