Holland, J. H.: 1986, `Escaping Brittleness: The Possibilities of General{Purpose Learning Algorithms Applied to Parallel Rule{Based Systems'. In: R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (eds.): Machine Learning { An Arti cial Intelligence Approach, Vol. 2. Palo Alto, CA: Morgan Kaufmann, Chapt. 20, pp. 593-624. Home/Search Document Not in Database Summary Related Articles Check |
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Computational Modeling: Opportunities for the Information and.. - Kimbrough (2002) (Correct)
....(e.g. Donald Campbell) and neurophysiology (e.g. William Calvin) Gerald Edelman, who won a Nobel prize for his contributions to immunology, has also been active. The second way in which a Darwinian, selectionist perspective has proved valuable is in computational learning. As Holland (e.g. [31]) and others have shown, the simple genetic algorithm (GA) and its evolution programming variants are general purpose computational algorithms. Evolution is a form of computation. Not only can we understand natural evolutionary processes as computations, but we can abstract and emulate them for ....
....systems were an early and moderately useful way to encode knowledge for subsequent use. Their range of application has been limited by the cost and trouble of knowledge acquisition, the process of obtaining information in a form that fits the encoding scheme. Holland s classifier systems [9, 28, 31], a software architecture that facilitates automated knowledge acquisition, were conceived, in part, as a response to this problem. There is now an active community working on generalizations of classifier systems, the class usually referred to as Learning Classifier Systems (LCS) 56, 57, 40] ....
John H. Holland. Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning II. Morgan Kaufmann, 1986.
On Generalisation in Michigan-Style Fuzzy Classifier Systems - Carse, Pipe (Correct)
....and are incorrect for others. Despite being unreliable, such overgeneral rules can have more influence and better chances of survival (under action of the evolutionary algorithm) than other more specific and correct rules with which they compete. Traditional Michigan style classifier systems [20] have been strength based in the sense that a classifier accrues strength during interaction with the environment (through rewards and or penalties) This strength is then used for two purposes: resolving conflicts between simultaneously matched classifiers during learning episodes; and as the ....
.... interested reader can find further details in a tutorial paper [3] and in the encyclopaedic text Genetic Fuzzy Systems [4] The first description of a Michigan style fuzzy classifier system is given by Valenzuela Rendn [5] Closely modeled on the discrete valued Holland style classifier system [20] this system contains a fixed size rule base of fuzzy classifiers and a fuzzy message list. An individual rule is represented as a binary string that encodes the membership functions of the fuzzy sets involved in the rule. To illustrate the representation used, consider a variable X for which ....
Holland J.H. 1988. Escaping Brittleness: The Possibilities of General Purpose Machine Learning Algorithms applied to Parallel Rule-based systems. In: Michalski R.S., Carbonell J.G and Mitchell T.M. (Eds.), Machine Learning: an Artificial Intelligence Approach, vol.2. Kaufmann, Los Altos, Calif, 1988.
Towards the use of XCS in Interactive Evolutionary Design - Bull, Wyatt, Parmee (Correct)
....consideration of initial results and subsequent re definition of the space. This last aspect of the process is of interest to us here: we consider a way in which to enhance the presentation of results from a given iteration of the search process through the use of learning classifier systems (LCS) [10]. That is, we are interested in the use of XCS [18] as a data miner in problems where the speed of learning is important as is the ability to respond to changes made by the user in the underlying design space, i.e. between iterations of the IED process. XCS has been shown to perform well on a ....
Holland, J. (1986). Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine learning, an artificial intelligence approach. Los Altos, California: Morgan Kaufmann.
Learning and Planning in Structured Worlds - Dearden (Correct)
....a, r, t as follows: a) 1 a) m, ax a ) a At where is the current learning rate. 5. Go to step 1. Figure 4.3: The Q learning algorithm. 4.2 Model free learning A number of model free learning algorithms have been proposed. We will mention three here, the Adaptive Heuristic Critic [100, 54], TD(Jk) 101] see below) and Q learning [113, 114] Of these, the simplest and most widely used is Q learning (see Figure 4.3 for the algorithm) Q learning works by learning, for each state s and action a, the value of performing a in s and then acting optimally. Given a value function V, the ....
J. H. Holland. Escaping brittleness: The possibilities of general-purpose learn- ing algorithms applied to parallel rule-based systems. In R. Michalski, J. Car- bonell, and T. Mitchell, editors, Machine Learning II. Morgan Kaufmann, 1986.
Cooperative Coevolution of Technical Trading Rules - Becker, Seshadri (2003) (Correct)
....well or poorly which of the individual agents gets the credit or blame. For competitive coevolution systems with only two agents, this is not a problem since one can use a complement. However, when multiple agents are cooperating to achieve a solution, this is a more serious issue. Holland s[14] work on classifier systems used the bucket brigade algorithm for assigning credit. Potter De Jong[15] describe an architecture for cooperative coevolution, in which individuals in one species are evaluated by using collaborators from each of the other coevolving species. There exist a number ....
Holland, J.H. 1986. Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. In PS. Michalski, J.G. Carbonell, and T.M. Mitchell, (Eds.), Machine Learning, Volume 2: 593-623.Los Altos: Morgan Kaufman.
Communicating Neural Network Knowledge between Agents in a .. - Quirolgico, Canfield (1 citation) (Correct)
....and may be applied (i.e. executed) in some domain. The application of a neural network represents the recall phase of a network. In many cases, a trained neural network is used to implement systems that simply classify tuples of input data. Such systems are often referred to as classifier systems [7, 10, 12]. In a classifier system, the parameters of a network remain constant which prevent it from continuing to learn during execution. Neural networks may also be used, however, to implement systems that continue to learn, adapt, and strengthen their classification capabilities during execution by ....
J. H. Holland. Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rulebased systems. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning, an Artificial Intelligence Approach. Morgan Kaufmann, 1986.
Experimental Comparison of Symbolic and Subsymbolic Learning - Wnek, Michalski (1992) (Correct)
.... can influence future generations; 2) a mating operator, which produces offspring for the next generation; and (3) genetic operators, which determine the genetic makeup of offspring from the genetic material of the parents [22] Classifier systems were first introduced by Holland and Reitman [23, 24]. The shell for the classifier system used in the experiments was developed by Riolo [25] The CFS package of subroutines and data structures is domain . independent and provides routines to perform the major cycle of the classifier system. The CFS system was run in the stimulus response mode, ....
Holland, J.H. 1986. Escaping Brittleness: The Possibilities of General Purpose Learning Algorithms Applied to Parallel Rule-Based Systems. In Machine Learning: An Artificial Intelligence Approach, ed. R.S. Michalski. J.G. Carbonell and T.M. Mitchell. Los Altos, CA: Morgan Kaufmann.
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Holland, J. H.: 1986, `Escaping Brittleness: The Possibilities of General{Purpose Learning Algorithms Applied to Parallel Rule{Based Systems'. In: R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (eds.): Machine Learning { An Arti cial Intelligence Approach, Vol. 2. Palo Alto, CA: Morgan Kaufmann, Chapt. 20, pp. 593-624.
Learning Feature Extraction for Learning from - Audio Data Ingo (Correct)
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J. H. Holland. Escaping brittleness: The possibilities of general{purpose learning algorithms applied to parallel rule{based systems. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning { An Arti cial Intelligence Approach, volume 2, chapter 20, pages 593-624. Morgan Kaufmann, Palo Alto, CA, 1986. 11
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Holland, J. H. (1986) Escaping brittleness: The possibilities of general purpose learning algorithms applied in parallel rule-based systems. In R. S. Michaiski, J. G. Carbonell, & T. M. Mitchell (Eds. ), Machine Learning II (pp. 593-623). Los Altos, CA: Morgan Kaufmann.
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Holland, J. H.: 1986, `Escaping Brittleness: The Possibilities of General{Purpose Learning Algorithms Applied to Parallel Rule{Based Systems'. In: R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (eds.): Machine Learning { An Arti cial Intelligence Approach, Vol. 2. Palo Alto, CA: Morgan Kaufmann, Chapt. 20, pp. 593-624.
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Holland, J. H. (1986) , Escaping Brittleness: The possibilities of General-purpose Learning Algorithms Applied to Parallel Rule-Based Systems, in Mitchell, T.M., Michalski, R. S., and Carbonell, J.G. (eds.), Machine Learning, An Artificial Intelligence Approach, Vol. II, ch. 20, 593-623, Morgan Kaufmann.
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John H. Holland. Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In Mitchell, Michalski, and Carbonell, editors, Machine learning, an arti#cial intelligence approach. Volume II,chapter 20, pages 593#623. Morgan Kaufmann, 1986.
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J. H. Holland. Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In T. Mitchell, R. Michalski, and J. Carbonell, editors, Machine Learning, Volume 2, chapter 20, pages 593--
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John H. Holland, \Escaping Brittleness: The Possibilities of General Purpose Learning Algorithms Applied to Parallel Rule-Based Systems," Machine Learning: An Arti cial Intelligence Approach, Vol. II, pp. 593-623, 1986.
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John H. Holland. Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In R. S. Michalski, J. G. Carbonell and T. M. Mitchell, editors, Machine learning: An artificial intelligence approach, volume II, pages 593--623, Los Altos, CA, 1986. Morgan Kaufmann.
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John H. Holland. Escaping Brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems. In Mitchell, Michalski, and Carbonell, editors, Machine learning, an artificial intelligence approach. Volume II, chapter 20, pages 593--623. Morgan Kaufmann, 1986.
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J. H. Holland. Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In T. Mitchell, R. Michalski, and J. Carbonell, editors, Machine Learning, Volume 2, chapter 20, pages 593-- 623. Morgan Kaufmann, San Mateo, CA, 1986.
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J. H. Holland. Escaping Brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. Machine Learning: An Arti cial Intelligence Approach, Volume II. Edited by R. S. Michalski, J. G. Carbonell, and T.M. Mitchell, Morgan Kaufmann Publishers, 1986.
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J. H. Holland. Escaping brittleness: the possibilities of general purpose algorithms applied to parallel rule-based systems. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning, an Artificial Intelligence Approach, pages 593--623. Morgan Kaufmann, San Mateo, California, 1986.
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J.H. Holland. Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. In R.S. Michalski, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning II, pages 593-623. Kaufman, 1986.
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John Holland, "Escaping Brittleness: the Possibilities of General-Purpose Learning Algorithms applied to Parallel Rule-Based Systems," in Machine Learning, an Artificial Intelligence Approach, Volume II, edited by R.S. Michalski, J.G. Carbonell and T.M. Mitchell, Morgan Kaufmann, 1986.
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J. Holland, "Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems," in Machine Learning: An Artificial Intelligence Approach, R. Michalski, J. Carbonell, and T. Mitchell, Eds. San Mateo, CA: Morgan Kaufmann, 1986.
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Holland, J. H. (1986). Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In R. S. Michalski, J. G. Carbonell & T. M. Mitchell (eds.), Machine Learning, an Artificial Intelligence Approach. Volume II. Los Altos, California: Morgan Kau#mann, pages 593--623.
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Holland, J. H. (1986), Escaping Brittleness: the Possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems, Machine Learning, an Artificial Intelligence approach Volume II, Morgan Kaufmann, pp 593-623.
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