Above are papers related to the following paper: [Paper_ID] Paper #1131828. 	[Title] Problem Solving for Redesign [Abstract] A knowledge-level analysis of complex tasks like diagnosis and design can give us a better understanding of these tasks in terms of the goals they aim to achieve and the different ways to achieve these goals. In this paper we present a knowledge-level analysis of redesign. Redesign is viewed as a family of methods based on some common principles, and a number of dimensions along which redesign problem solving methods can vary are distinguished. By examining the problem-solving behavior of a number of existing redesign systems and approaches, we came up with a collection of problem-solving methods for redesign and developed a task-method structure for redesign. In constructing a system for redesign a large number of knowledge-related choices and decisions are made. In order to describe all relevant choices in redesign problem solving, we have to extend the current notion of possible relations between tasks and methods in a PSM architecture. The realization of a task by a problem-solving method, and the decomposition of a problem-solving method into subtasks are the most common relations in a PSM architecture. However, we suggest to extend these relations with the notions of task refinement and method refinement. These notions represent intermediate decisions in a task-method structure, in which the competence of a task or method is refined without immediately paying attention to its operationalization in terms of subtasks. Explicit representation of this kind of intermediate decisions helps to make and represent decisions in a more piecemeal fashion. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #656048. 	[Title] Learning Control Knowledge in Models of Expertise ECML'95 Workshop on Knowledge-Level Modelling and Machine Learning [Abstract] During the development and the life-cycle of knowledge-based systems the requirements on the system and the knowledge in the system will change. One of the types of knowledge affected by changing requirements is control-knowledge, which prescribes the ordering of problem-solving steps. Machine-learning can aid developers of knowledge-based systems in adapting their systems to changing requirements. A number of machine-learning techniques for learning control-knowledge have been applied to problem-solvers (Prodigy-EBL, LEX). In knowledge engineering, the focus has shifted to the construction of knowledge-level models of problem-solving instead of directly constructing a knowledge-based system in a problem-solver. In this paper we describe work in progress on how to apply machine learning techniques to the KADS model of expertise. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #103531. 	[Title] A Performance Model for Knowledge-based Systems [Abstract] Most techniques for verification and validation are directed at functional properties of programs. However, other properties of programs are also essential. This paper describes a model for the average computing time of a KADS knowledge-based system based on its structure. An example taken from an existing knowledge-based system is used to demonstrate the use of the cost-model in designing the system. Please summarise all the given information.

Paper #1131828 has the title `Problem Solving for Redesign  
` and the abstract `A knowledge-level analysis of complex tasks like diagnosis and design can give us a better understanding of these tasks in terms of the goals they aim to achieve and the different ways to achieve these goals. In this paper we present a knowledge-level analysis of redesign. Redesign is viewed as a family of methods based on some common principles, and a number of dimensions along which redesign problem solving methods can vary are distinguished. By examining the problem-solving behavior of a number of existing redesign systems and approaches, we came up with a collection of problem-solving methods for redesign and developed a task-method structure for redesign. In constructing a system for redesign a large number of knowledge-related choices and decisions are made. In order to describe all relevant choices in redesign problem solving, we have to extend the current notion of possible relations between tasks and methods in a PSM architecture. The realization of a task by a problem-solving method, and the decomposition of a problem-solving method into subtasks are the most common relations in a PSM architecture. However, we suggest to extend these relations with the notions of task refinement and method refinement. These notions represent intermediate decisions in a task-method structure, in which the competence of a task or method is refined without immediately paying attention to its operationalization in terms of subtasks. Explicit representation of this kind of intermediate decisions helps to make and represent decisions in a more piecemeal fashion. 
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Question: Which category should Paper #1131828 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1131828 has the title `Problem Solving for Redesign  
` and the abstract `A knowledge-level analysis of complex tasks like diagnosis and design can give us a better understanding of these tasks in terms of the goals they aim to achieve and the different ways to achieve these goals. In this paper we present a knowledge-level analysis of redesign. Redesign is viewed as a family of methods based on some common principles, and a number of dimensions along which redesign problem solving methods can vary are distinguished. By examining the problem-solving behavior of a number of existing redesign systems and approaches, we came up with a collection of problem-solving methods for redesign and developed a task-method structure for redesign. In constructing a system for redesign a large number of knowledge-related choices and decisions are made. In order to describe all relevant choices in redesign problem solving, we have to extend the current notion of possible relations between tasks and methods in a PSM architecture. The realization of a task by a problem-solving method, and the decomposition of a problem-solving method into subtasks are the most common relations in a PSM architecture. However, we suggest to extend these relations with the notions of task refinement and method refinement. These notions represent intermediate decisions in a task-method structure, in which the competence of a task or method is refined without immediately paying attention to its operationalization in terms of subtasks. Explicit representation of this kind of intermediate decisions helps to make and represent decisions in a more piecemeal fashion. 
`.
Question: Which category should Paper #1131828 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