Above are papers related to the following paper: [Paper_ID] Paper #41417. 	[Title] Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains [Abstract] 1 We have developed an approach to acquire complicated user optimization criteria and use them to guide Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6209. 	[Title] Transfer of Learning by Composing Solutions of Elemental Sequential Tasks [Abstract] Although building sophisticated learning agents that operate in complex environments will require learning to perform multiple tasks, most applications of reinforcement learning have focussed on single tasks. In this paper I consider a class of sequential decision tasks (SDTs), called composite sequential decision tasks, formed by temporally concatenating a number of elemental sequential decision tasks. Elemental SDTs cannot be decomposed into simpler SDTs. I consider a learning agent that has to learn to solve a set of elemental and composite SDTs. I assume that the structure of the composite tasks is unknown to the learning agent. The straightforward application of reinforcement learning to multiple tasks requires learning the tasks separately, which can waste computational resources, both memory and time. I present a new learning algorithm and a modular architecture that learns the decomposition of composite SDTs, and achieves transfer of learning by sharing the solutions of elemental SDTs across multiple composite SDTs. The solution of a composite SDT is constructed by computationally inexpensive modifications of the solutions of its constituent elemental SDTs. I provide a proof of one aspect of the learning algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103394. 	[Title] The evaluation of Anapron: A case study in evaluating a case-based system [Abstract] This paper presents a case study in evaluating a case-based system. It describes the evaluation of Anapron, a system that pronounces names by a combination of rule-based and case-based reasoning. Three sets of experiments were run on Anapron: a set of exploratory measurements to profile the system's operation; a comparison between Anapron and other name-pronunciation systems; and a set of studies that modified various parts of the system to isolate the contribution of each. Lessons learned from these experiments for CBR evaluation methodology and for CBR theory are discussed. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories of Cambridge, Massachusetts; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories. All rights reserved. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22563. 	[Title] A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping [Abstract] The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially. A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10435. 	[Title] Improving accuracy by combining rule-based and case-based reasoning [Abstract] An architecture is presented for combining rule-based and case-based reasoning. The architecture is intended for domains that are understood reasonably well, but still imperfectly. It uses a set of rules, which are taken to be only approximately correct, to obtain a preliminary answer for a given problem; it then draws analogies from cases to handle exceptions to the rules. Having rules together with cases not only increases the architecture's domain coverage, it also allows innovative ways of doing case-based reasoning: the same rules that are used for rule-based reasoning are also used by the case-based component to do case indexing and case adaptation. The architecture was applied to the task of name pronunciation, and, with minimal knowledge engineering, was found to perform almost at the level of the best commercial systems. Moreover, its accuracy was found to exceed what it could have achieved with rules or cases alone, thus demonstrating the accuracy improvement afforded by combining rule-based and case-based reasoning. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories of Cambridge, Massachusetts; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories. All rights reserved. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6213. 	[Title] Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction, [Abstract] This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22566. 	[Title] A comparison of Anapron with seven other name-pronunciation systems [Abstract] This paper presents an experiment comparing a new name-pronunciation system, Anapron, with seven existing systems: three state-of-the-art commercial systems (from Bellcore, Bell Labs, and DEC), two variants of a machine-learning system (NETtalk), and two humans. Anapron works by combining rule-based and case-based reasoning. It is based on the idea that it is much easier to improve a rule-based system by adding case-based reasoning to it than by tuning the rules to deal with every exception. In the experiment described here, Anapron used a set of rules adapted from MITalk and elementary foreign-language textbooks, and a case library of 5000 names. With these components | which required relatively little knowledge engineering | Anapron was found to perform almost at the level of the commercial systems, and significantly better than the two versions of NETtalk. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories of Cambridge, Massachusetts; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories. All rights reserved. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22564. 	[Title] PRONOUNCING NAMES BY A COMBINATION OF RULE-BASED AND CASE-BASED REASONING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #128383. 	[Title] Using Case-Based Reasoning to Acquire User Scheduling Preferences that Change over Time [Abstract] Production/Manufacturing scheduling typically involves the acquisition of user optimization preferences. The ill-structuredness of both the problem space and the desired objectives make practical scheduling problems difficult to formalize and costly to solve, especially when problem configurations and user optimization preferences change over time. This paper advocates an incremental revision framework for improving schedule quality and incorporating user dynamically changing preferences through Case-Based Reasoning. Our implemented system, called CABINS, records situation-dependent tradeoffs and consequences that result from schedule revision to guide schedule improvement. The preliminary experimental results show that CABINS is able to effectively capture both user static and dynamic preferences which are not known to the system and only exist implicitly in a extensional manner in the case base. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108329. 	[Title] Using Case-Based Reasoning as a Reinforcement Learning Framework for Optimization with Changing Criteria [Abstract] Practical optimization problems such as job-shop scheduling often involve optimization criteria that change over time. Repair-based frameworks have been identified as flexible computational paradigms for difficult combinatorial optimization problems. Since the control problem of repair-based optimization is severe, Reinforcement Learning (RL) techniques can be potentially helpful. However, some of the fundamental assumptions made by traditional RL algorithms are not valid for repair-based optimization. Case-Based Reasoning (CBR) compensates for some of the limitations of traditional RL approaches. In this paper, we present a Case-Based Reasoning RL approach, implemented in the C A B I N S system, for repair-based optimization. We chose job-shop scheduling as the testbed for our approach. Our experimental results show that C A B I N S is able to effectively solve problems with changing optimization criteria which are not known to the system and only exist implicitly in a extensional manner in the case base. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #70444. 	[Title] CABINS A Framework of Knowledge Acquisition and Iterative Revision for Schedule Improvement and Reactive Repair [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113551. 	[Title] Modeling Ill-Structured Optimization Tasks through Cases [Abstract] CABINS is a framework of modeling an optimization task in ill-structured domains. In such domains, neither systems nor human experts possess the exact model for guiding optimization. And the user's model of optimality is subjective and situation-dependent. CABINS optimizes a solution through iterative revision using case-based reasoning. In CABINS, task structure analysis was adopted for creating an initial model of the optimization task. Generic vocabularies found in the analysis were specialized into case feature descriptions for application problems. Extensive experimentation on job shop scheduling problems has shown that CABINS can operationalize and improve the model through the accumulation of cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #208345. 	[Title] CHIRON: Planning in an Open-Textured Domain [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #112378. 	[Title] Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion [Abstract] We describe an approach to grapheme-to-phoneme conversion which is both language-independent and data-oriented. Given a set of examples (spelling words with their associated phonetic representation) in a language, a grapheme-to-phoneme conversion system is automatically produced for that language which takes as its input the spelling of words, and produces as its output the phonetic transcription according to the rules implicit in the training data. We describe the design of the system, and compare its performance to knowledge-based and alternative data-oriented approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #198653. 	[Title] Information filtering: Selection mechanisms in learning systems. Machine Learning, 10:113-151, 1993. Generalization as search. Artificial [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8703. 	[Title] Theory Refinement Combining Analytical and Empirical Methods [Abstract] This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory. For each theory fault, correlated subsets of the examples are used to inductively generate a correction. Because the corrections are focused, they tend to preserve the structure of the original theory. Because the system starts with an approximate domain theory, in general fewer training examples are required to attain a given level of performance (classification accuracy) compared to a purely empirical system. The approach applies to classification systems employing a propositional Horn-clause theory. The system has been tested in a variety of application domains, and results are presented for problems in the domains of molecular biology and plant disease diagnosis. Please summarise all the given information.

Paper #41417 has the title `Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains  
` and the abstract `1 We have developed an approach to acquire complicated user optimization criteria and use them to guide 
`.
Question: Which category should Paper #41417 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #41417 has the title `Case-based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains  
` and the abstract `1 We have developed an approach to acquire complicated user optimization criteria and use them to guide 
`.
Question: Which category should Paper #41417 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: Case_Based