Above are papers related to the following paper: [Paper_ID] Paper #1118286. 	[Title] Reports of the GMU Machine Learning and Inference HOW DID AQ FACE THE EAST-WEST CHALLENGE? [Abstract] The East-West Challenge is the title of the second international competition of machine learning programs, organized in the Fall 1994 by Donald Michie, Stephen Muggleton, David Page and Ashwin Srinivasan from Oxford University. The goal of the competition was to solve the TRAINS problems, that is to discover the simplest classification rules for train-like structured objects. The rule complexity was judged by a Prolog program that counted the number of various components in the rule expressed in the from of Prolog Horn clauses. There were 65 entries from several countries submitted to the competition. The GMU teams entry was generated by three members of the AQ family of learning programs: AQ-DT, INDUCE and AQ17-HCI. The paper analyses the results obtained by these programs and compares them to those obtained by other learning programs. It also presents ideas for further research that were inspired by the competition. One of these ideas is a challenge to the machine learning community to develop a measure of knowledge complexity that would adequately capture the cognitive complexity of knowledge. A preliminary measure of such cognitive complexity, called Ccomplexity, different from the Prolog-complexity (P-complexity) used in the competition, is briefly discussed. The authors thank Professors Donald Michie, Steve Muggleton, David Page and Ashwin Srinivasan for organizing the West-East Challenge competition of machine learning programs, which provided us with a stimulating challenge for our learning programs and inspired new ideas for improving them. The authors also thank Nabil Allkharouf and Ali Hadjarian for their help and suggestions in the efforts to solve problems posed by the competition. This research was conducted in the Center for Machine Learning and Inference at George Mason University. The Center's research is supported in part by the Advanced Research Projects Agency under Grant No. N00014-91-J-1854, administered by the Office of Naval Research, and Grant No. F49620-92-J-0549, administered by the Air Force Office of Scientific Research, in part by the Office of Naval Research under Grant No. N00014-91-J-1351, and in part by the National Science Foundation under Grants No. IRI-9020266, CDA-9309725 and DMI-9496192. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153101. 	[Title] Machine Learning and Inference [Abstract] Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: onefor the best representation space, and twofor the best hypothesis in that space. In data-driven constructive induction (DCI), a learning system searches for a better representation space by analyzing the input examples (data). The presented data-driven constructive induction method combines an AQ-type learning algorithm with two classes of representation space improvement operators: constructors, and destructors. The implemented system, AQ17-DCI, has been experimentally applied to a GNP prediction problem using a World Bank database. The results show that decision rules learned by AQ17-DCI outperformed the rules learned in the original representation space both in predictive accuracy and rule simplicity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #137359. 	[Title] CONSTRUCTIVE INDUCTION FROM DATA IN AQ17-DCI: Further Experiments [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108728. 	[Title] Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Constructive Induction-based Learning Agents: [Abstract] This paper introduces a new type of intelligent agent called a constructive induction-based learning agent (CILA). This agent differs from other adaptive agents because it has the ability to not only learn how to assist a user in some task, but also to incrementally adapt its knowledge representation space to better fit the given learning task. The agents ability to autonomously make problem-oriented modifications to the originally given representation space is due to its constructive induction (CI) learning method. Selective induction (SI) learning methods, and agents based on these methods, rely on a good representation space. A good representation space has no misclassification noise, inter-correlated attributes or irrelevant attributes. Our proposed CILA has methods for overcoming all of these problems. In agent domains with poor representations, the CI-based learning agent will learn more accurate rules and be more useful than an SI-based learning agent. This paper gives an architecture for a CI-based learning agent and gives an empirical comparison of a CI and SI for a set of six abstract domains involving DNF-type (disjunctive normal form) descriptions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153150. 	[Title] Machine Learning and Inference [Abstract] Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: onefor the best representation space, and twofor the best hypothesis in that space. In data-driven constructive induction (DCI), a learning system searches for a better representation space by analyzing the input examples (data). The presented data-driven constructive induction method combines an AQ-type learning algorithm with two classes of representation space improvement operators: constructors, and destructors. The implemented system, AQ17-DCI, has been experimentally applied to a GNP prediction problem using a World Bank database. The results show that decision rules learned by AQ17-DCI outperformed the rules learned in the original representation space both in predictive accuracy and rule simplicity. Please summarise all the given information.

Paper #1118286 has the title `Reports of the GMU Machine Learning and Inference HOW DID AQ FACE THE EAST-WEST CHALLENGE?
` and the abstract `The East-West Challenge is the title of the second international competition of machine learning programs, organized in the Fall 1994 by Donald Michie, Stephen Muggleton, David Page and Ashwin Srinivasan from Oxford University. The goal of the competition was to solve the TRAINS problems, that is to discover the simplest classification rules for train-like structured objects. The rule complexity was judged by a Prolog program that counted the number of various components in the rule expressed in the from of Prolog Horn clauses. There were 65 entries from several countries submitted to the competition. The GMU teams entry was generated by three members of the AQ family of learning programs: AQ-DT, INDUCE and AQ17-HCI. The paper analyses the results obtained by these programs and compares them to those obtained by other learning programs. It also presents ideas for further research that were inspired by the competition. One of these ideas is a challenge to the machine learning community to develop a measure of knowledge complexity that would adequately capture the cognitive complexity of knowledge. A preliminary measure of such cognitive complexity, called Ccomplexity, different from the Prolog-complexity (P-complexity) used in the competition, is briefly discussed. The authors thank Professors Donald Michie, Steve Muggleton, David Page and Ashwin Srinivasan for organizing the West-East Challenge competition of machine learning programs, which provided us with a stimulating challenge for our learning programs and inspired new ideas for improving them. The authors also thank Nabil Allkharouf and Ali Hadjarian for their help and suggestions in the efforts to solve problems posed by the competition. This research was conducted in the Center for Machine Learning and Inference at George Mason University. The Center's research is supported in part by the Advanced Research Projects Agency under Grant No. N00014-91-J-1854, administered by the Office of Naval Research, and Grant No. F49620-92-J-0549, administered by the Air Force Office of Scientific Research, in part by the Office of Naval Research under Grant No. N00014-91-J-1351, and in part by the National Science Foundation under Grants No. IRI-9020266, CDA-9309725 and DMI-9496192. 
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Question: Which category should Paper #1118286 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1118286 has the title `Reports of the GMU Machine Learning and Inference HOW DID AQ FACE THE EAST-WEST CHALLENGE?
` and the abstract `The East-West Challenge is the title of the second international competition of machine learning programs, organized in the Fall 1994 by Donald Michie, Stephen Muggleton, David Page and Ashwin Srinivasan from Oxford University. The goal of the competition was to solve the TRAINS problems, that is to discover the simplest classification rules for train-like structured objects. The rule complexity was judged by a Prolog program that counted the number of various components in the rule expressed in the from of Prolog Horn clauses. There were 65 entries from several countries submitted to the competition. The GMU teams entry was generated by three members of the AQ family of learning programs: AQ-DT, INDUCE and AQ17-HCI. The paper analyses the results obtained by these programs and compares them to those obtained by other learning programs. It also presents ideas for further research that were inspired by the competition. One of these ideas is a challenge to the machine learning community to develop a measure of knowledge complexity that would adequately capture the cognitive complexity of knowledge. A preliminary measure of such cognitive complexity, called Ccomplexity, different from the Prolog-complexity (P-complexity) used in the competition, is briefly discussed. The authors thank Professors Donald Michie, Steve Muggleton, David Page and Ashwin Srinivasan for organizing the West-East Challenge competition of machine learning programs, which provided us with a stimulating challenge for our learning programs and inspired new ideas for improving them. The authors also thank Nabil Allkharouf and Ali Hadjarian for their help and suggestions in the efforts to solve problems posed by the competition. This research was conducted in the Center for Machine Learning and Inference at George Mason University. The Center's research is supported in part by the Advanced Research Projects Agency under Grant No. N00014-91-J-1854, administered by the Office of Naval Research, and Grant No. F49620-92-J-0549, administered by the Air Force Office of Scientific Research, in part by the Office of Naval Research under Grant No. N00014-91-J-1351, and in part by the National Science Foundation under Grants No. IRI-9020266, CDA-9309725 and DMI-9496192. 
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Question: Which category should Paper #1118286 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Rule_Learning