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 #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 #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 #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 #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 #70442. 	[Title] Learning to Predict User Operations for Adaptive Scheduling [Abstract] Mixed-initiative systems present the challenge of finding an effective level of interaction between humans and computers. Machine learning presents a promising approach to this problem in the form of systems that automatically adapt their behavior to accommodate different users. In this paper, we present an empirical study of learning user models in an adaptive assistant for crisis scheduling. We describe the problem domain and the scheduling assistant, then present an initial formulation of the adaptive assistant's learning task and the results of a baseline study. After this, we report the results of three subsequent experiments that investigate the effects of problem reformulation and representation augmentation. The results suggest that problem reformulation leads to significantly better accuracy without sacrificing the usefulness of the learned behavior. The studies also raise several interesting issues in adaptive assistance for scheduling. 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 #70441. 	[Title] on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant [Abstract] In this paper, we present an interactive, case-based approach to crisis response that provides users with the ability to rapidly develop good responses while allowing them to retain ultimate control over the decision-making process. We have implemented this approach in Inca, an INteractive Crisis Assistant for planning and scheduling in crisis domains. Inca relies on case-based methods to seed the response development process with initial candidate solutions drawn from previous cases. The human user then interacts with Inca to adapt these solutions to the current situation. We will discuss this interactive approach to crisis response using an artificial hazardous materials domain, Haz-Mat, that we developed for the purpose of evaluating candidate assistant mechanisms for crisis response. 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 #1107215. 	[Title] Evaluating Computational Assistance for Crisis Response [Abstract] In this paper we examine the behavior of a human-computer system for crisis response. As one instance of crisis management, we describe the task of responding to spills and fires involving hazardous materials. We then describe INCA, an intelligent assistant for planning and scheduling in this domain, and its relation to human users. We focus on INCA's strategy of retrieving a case from a case library, seeding the initial schedule, and then helping the user adapt this seed. We also present three hypotheses about the behavior of this mixed-initiative system and some experiments designed to test them. The results suggest that our approach leads to faster response development than user-generated or automatically-generated schedules but without sacrificing solution quality. Please summarise all the given information.

Paper #128383 has the title `Using Case-Based Reasoning to Acquire User Scheduling Preferences that Change over Time  
` and the 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. 
`.
Question: Which category should Paper #128383 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #128383 has the title `Using Case-Based Reasoning to Acquire User Scheduling Preferences that Change over Time  
` and the 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. 
`.
Question: Which category should Paper #128383 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