Davis, L., Wilson, S., Orvosh, D. (1992), Temporary Memory for Examples Can Speed Learning in a Simple Adaptive System, in Meyer, J.A., Roitblat, H.L., Wilson, S.W. (eds), Proc. Second Intl Conf on Simulation of Adaptive Behaviour - Animals to Animats II, 313-320, MIT Press. Home/Search Document Not in Database Summary Related Articles Check |
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Hierarchy Formation within Classifier Systems - A Review - Barry (1996) (Correct)
....sequence switch was strongly reinforced. It is therefore unclear whether this switching behaviour consistutes behaviour sequencing as a cognitive process. An alternative form of partitioning of a single classifier system is obtained when other helper classifier systems are added. For example, Davis et al. (1992) introduce short term memory to a classifier system, showing an improved rate of learning in terms of the number of external trials required to achieve a given performance level. The external memory they introduce is not itself a classifier system, nor even a classifier storage area. It is simply ....
....signifigantly speed learning given suitable parameterisation. Whilst the memory was not itself a classifier system, it s structure is similar and a more integrated classifier system structure which provides a memory classifier system influencing a competence classifier system was investigated (Davis, 1992). An alternative form of memory decomposition was presented by Zhou (1990) in his CSM system where established rule chains were moved into a Long Term Memory where they could be preserved from competition. This is not strictly hierarchy , but simply an internal structure preservation ....
Davis, L., Wilson, S., Orvosh, D. (1992), Temporary Memory for Examples Can Speed Learning in a Simple Adaptive System, in Meyer, J.A., Roitblat, H.L., Wilson, S.W. (eds), Proc. Second Intl Conf on Simulation of Adaptive Behaviour - Animals to Animats II, 313-320, MIT Press.
Evolution of a Clustering Scheme for a Classifier System: Beyond.. - Tufts (1994) (Correct)
....test the clustering mechanism for two reasons. First, it is a standard problem for learning and optimization schemes. Second, the problem is scalable. The Boolean multiplexer problem is a common benchmark for comparing di erent learning systems. Both Genetic Programming [15] and Classi er Systems [33, 4] have been applied to it. In addition, connectionist networks and decision trees have been applied to the problem according to [4] The 11 multiplexer problem has a search space of approximately 10 616 [15, p. 170] 14 7.2 Animat The Animat problem, rst proposed by Wilson [33] is one where ....
....is scalable. The Boolean multiplexer problem is a common benchmark for comparing di erent learning systems. Both Genetic Programming [15] and Classi er Systems [33, 4] have been applied to it. In addition, connectionist networks and decision trees have been applied to the problem according to [4]. The 11 multiplexer problem has a search space of approximately 10 616 [15, p. 170] 14 7.2 Animat The Animat problem, rst proposed by Wilson [33] is one where an arti cial agent moves about on a two dimensional surface searching for food. The agent can sense its local environment, and ....
Lawrence Davis, Stewart Wilson, and David Orvosh. Temporary memory for examples can speed learning in a simple adaptive system. In Jean-Arcady Meyer, Herbert L Roitblat, and Stewart W. Wilson, editors, From Animals to Animats 2, pages 313-320. MIT Press, 1993.
An Indexed Bibliography of Genetic Algorithms Papers of 1993 - Jarmo T. Alander (1996) (Correct)
....Dandy, Graeme C. 937, 939, 940, 941] D Anjou, A. 615] Darenfeld, S. 198] Das, Rajarshi, 1065] Dasgupta, Dipankar, 199, 200, 201, 704, 705, 706, 707, 708, 709] Dastidar, D. Ghosh, 202] David, E. 963] Authors 17 Davidge, Robert, 203] Davidor, Yuval, 204, 205] Davis, Lawrence, [206, 207, 208, 209, 210] Davis, Thomas Elder, 973] Deb, Kalyanmoy, 217, 375, 376, 378, 381] Deboeck, Guido, 218, 219] deFigueiredo, Rui J. P. 944] Delaney, B. 239] Denham, M. J. 797] Deodhar, D. 538] Deugo, Dwight, 220, 221] Dhawan, Atam P. 222, 223, 657, 806] Dike, B. A. 834, 837] Dissanayake, ....
....T. 496] Okada, Y. 764] Okino, Norio, 1055, 1056, 1057] Okumoto, Takaaki, 427] Oliker, S. 765] Oliver, Jim R. 766] Olsan, James B. 767] Omatu, Sigeru, 344, 345] Ono, Norihiko, 768, 769] Onoda, Junjiro, 770] Opaterny, Thilo, 921] Oppacher, Franz, 220, 221] Orvosh, David, [206, 207, 208] Ost, Alexander, 921] Ostermeier, Andreas, 771] Ostrowski, Tomasz, 772] Ott, K. 773] Paez, Thomas L. 787] Pak, W. H. 788] Pakath, Ramakrishnan, 471] Pal, K. F. 529] Pal, Sankar K. 886] Palareti, Aldopaolo, 164, 165] Palmieri, Francesco, 829] Pao, Yoh Han, 1095, 1096] ....
[Article contains additional citation context not shown here]
Lawrence Davis, Stewart W. Wilson, and David Orvosh. Temporary memory for examples can speed learning in a simple adaptive system. In Roitblat et al. [1117], pages 313--320. ga:Davis93b.
A Learning Classifier Systems Bibliography - Kovacs, Lanzi (1999) (Correct)
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Lawrence Davis, Stewart W. Wilson, and David Orvosh. Temporary Memory for Examples can Speed Learning in a Simple Adaptive System. In Roitblat and Wilson [339], pages 313-320.
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