Above are papers related to the following paper: [Paper_ID] Paper #82666. 	[Title] Goal-Driven Learning: Fundamental Issues (A Symposium Report) [Abstract] In Artificial Intelligence, Psychology, and Education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with different goals process information differently; studies in education show that goals have strong effects on what students learn; and functional arguments from machine learning support the necessity of goal-based focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goal-driven learning. This article presents the fundamental points illuminated by the symposium, placing them in the context of open questions and current research di rections in goal-driven learning. fl Appears in AI Magazine, 14(4):67-72, 1993 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1952. 	[Title] Combining Rules and Cases to Learn Case Adaptation [Abstract] Computer models of case-based reasoning (CBR) generally guide case adaptation using a fixed set of adaptation rules. A difficult practical problem is how to identify the knowledge required to guide adaptation for particular tasks. Likewise, an open issue for CBR as a cognitive model is how case adaptation knowledge is learned. We describe a new approach to acquiring case adaptation knowledge. In this approach, adaptation problems are initially solved by reasoning from scratch, using abstract rules about structural transformations and general memory search heuristics. Traces of the processing used for successful rule-based adaptation are stored as cases to enable future adaptation to be done by case-based reasoning. When similar adaptation problems are encountered in the future, these adaptation cases provide task- and domain-specific guidance for the case adaptation process. We present the tenets of the approach concerning the relationship between memory search and case adaptation, the memory search process, and the storage and reuse of cases representing adaptation episodes. These points are discussed in the context of ongoing research on DIAL, a computer model that learns case adaptation knowledge for case-based disaster response planning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1956. 	[Title] INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning [Abstract] The development of multistrategy learning systems should be based on a clear understanding of the roles and the applicability conditions of different learning strategies. To this end, this chapter introduces the Inferential Theory of Learning that provides a conceptual framework for explaining logical capabilities of learning strategies, i.e., their competence. Viewing learning as a process of modifying the learners knowledge by exploring the learners experience, the theory postulates that any such process can be described as a search in a knowledge space, triggered by the learners experience and guided by learning goals. The search operators are instantiations of knowledge transmutations, which are generic patterns of knowledge change. Transmutations may employ any basic type of inferencededuction, induction or analogy. Several fundamental knowledge transmutations are described in a novel and general way, such as generalization, abstraction, explanation and similization, and their counterparts, specialization, concretion, prediction and dissimilization, respectively. Generalization enlarges the reference set of a description (the set of entities that are being described). Abstraction reduces the amount of the detail about the reference set. Explanation generates premises that explain (or imply) the given properties of the reference set. Similization transfers knowledge from one reference set to a similar reference set. Using concepts of the theory, a multistrategy task-adaptive learning (MTL) methodology is outlined, and illustrated b y an example. MTL dynamically adapts strategies to the learning task, defined by the input information, learners background knowledge, and the learning goal. It aims at synergistically integrating a whole range of inferential learning strategies, such as empirical generalization, constructive induction, deductive generalization, explanation, prediction, abstraction, and similization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1959. 	[Title] Introspective reasoning using meta-explanations for multistrategy learning [Abstract] In order to learn effectively, a reasoner must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pieces of knowledge it needs to improve its performance at the current task. Introspection requires declarative representations of meta-knowledge of the reasoning performed by the system during the performance task, of the system's knowledge, and of the organization of this knowledge. This chapter presents a taxonomy of possible reasoning failures that can occur during a performance task, declarative representations of these failures, and associations between failures and particular learning strategies. The theory is based on Meta-XPs, which are explanation structures that help the system identify failure types, formulate learning goals, and choose appropriate learning strategies in order to avoid similar mistakes in the future. The theory is implemented in a computer model of an introspective reasoner that performs multistrategy learning during a story understanding task. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #82664. 	[Title] Learning Adaptation Strategies by Introspective Reasoning about Memory Search [Abstract] In case-based reasoning systems, the case adaptation process is traditionally controlled by static libraries of hand-coded adaptation rules. This paper proposes a method for learning adaptation knowledge in the form of adaptation strategies of the type developed and hand-coded by Kass [90] . Adaptation strategies differ from standard adaptation rules in that they encode general memory search procedures for finding the information needed during case adaptation; this paper focuses on the issues involved in learning memory search procedures to form the basis of new adaptation strategies. It proposes a method that starts with a small library of abstract adaptation rules and uses introspective reasoning about the system's memory organization to generate the memory search plans needed to apply those rules. The search plans are then packaged with the original abstract rules to form new adaptation strategies for future use. This process allows a CBR system not only to learn about its domain, by storing the results of case adaptation, but also to learn how to apply the cases in its memory more effectively. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #596075. 	[Title] AN ARCHITECTURE FOR GOAL-DRIVEN EXPLANATION [Abstract] In complex and changing environments explanation must be a a dynamic and goal-driven process. This paper discusses an evolving system implementing a novel model of explanation generation | Goal-Driven Interactive Explanation | that models explanation as a goal-driven, multi-strategy, situated process inter-weaving reasoning with action. We describe a preliminary implementation of this model in gobie, a system that generates explanations for its internal use to support plan generation and execution. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1949. 	[Title] Learning to Improve Case Adaptation by Introspective Reasoning and CBR [Abstract] In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the task and domain that may not be available a priori, presenting a serious impediment to endowing CBR systems with the needed adaptation knowledge. This paper describes ongoing research on a method to address this problem by acquiring adaptation knowledge from experience. The method uses reasoning from scratch, based on introspective reasoning about the requirements for successful adaptation, to build up a library of adaptation cases that are stored for future reuse. We describe the tenets of the approach and the types of knowledge it requires. We sketch initial computer implementation, lessons learned, and open questions for further study. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #73327. 	[Title] Modeling Case-based Planning for Repairing Reasoning Failures [Abstract] One application of models of reasoning behavior is to allow a reasoner to introspectively detect and repair failures of its own reasoning process. We address the issues of the transferability of such models versus the specificity of the knowledge in them, the kinds of knowledge needed for self-modeling and how that knowledge is structured, and the evaluation of introspective reasoning systems. We present the ROBBIE system which implements a model of its planning processes to improve the planner in response to reasoning failures. We show how ROBBIE's hierarchical model balances model generality with access to implementation-specific details, and discuss the qualitative and quantitative measures we have used for evaluating its introspective component. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #129045. 	[Title] Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques [Abstract] The Dynamic Constraint Satisfaction Problem (DCSP) formalism has been gaining attention as a valuable and often necessary extension of the static CSP framework. Dynamic Constraint Satisfaction enables CSP techniques to be applied more extensively, since it can be applied in domains where the set of constraints and variables involved in the problem evolves with time. At the same time, the Case-Based Reasoning (CBR) community has been working on techniques by which to reuse existing solutions when solving new problems. We have observed that dynamic constraint satisfaction matches very closely the case-based reasoning process of case adaptation. These observations emerged from our previous work on combining CBR and CSP to achieve a constraint-based adaptation. This paper summarizes our previous results, describes the similarity of the challenges facing both DCSP and case adaptation, and shows how CSP and CBR can together begin to address these chal lenges. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #101143. 	[Title] Issues in Goal-Driven Explanation [Abstract] When a reasoner explains surprising events for its internal use, a key motivation for explaining is to perform learning that will facilitate the achievement of its goals. Human explainers use a range of strategies to build explanations, including both internal reasoning and external information search, and goal-based considerations have a profound effect on their choices of when and how to pursue explanations. However, standard AI models of explanation rely on goal-neutral use of a single fixed strategy|generally backwards chaining|to build their explanations. This paper argues that explanation should be modeled as a goal-driven learning process for gathering and transforming information, and discusses the issues involved in developing an active multi-strategy process for goal-driven explanation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #101145. 	[Title] Abduction, Experience, and Goals: A Model of Everyday Abductive Explanation* [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106236. 	[Title] BECOMING AN EXPERT CASE-BASED REASONER: LEARNING TO ADAPT PRIOR CASES [Abstract] Experience plays an important role in the development of human expertise. One computational model of how experience affects expertise is provided by research on case-based reasoning, which examines how stored cases encapsulating traces of specific prior problem-solving episodes can be retrieved and re-applied to facilitate new problem-solving. Much progress has been made in methods for accessing relevant cases, and case-based reasoning is receiving wide acceptance both as a technology for developing intelligent systems and as a cognitive model of a human reasoning process. However, one important aspect of case-based reasoning remains poorly understood: the process by which retrieved cases are adapted to fit new situations. The difficulty of encoding effective adaptation rules by hand is widely recognized as a serious impediment to the development of fully autonomous case-based reasoning systems. Consequently, an important question is how case-based reasoning systems might learn to improve their expertise at case adaptation. We present a framework for acquiring this expertise by using a combination of general adaptation rules, introspective reasoning, and case-based reasoning about the case adaptation task itself. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3101. 	[Title] Acquiring Case Adaptation Knowledge: A Hybrid Approach [Abstract] The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring flexible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the benefits of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3097. 	[Title] Towards A Computer Model of Memory Search Strategy Learning [Abstract] Much recent research on modeling memory processes has focused on identifying useful indices and retrieval strategies to support particular memory tasks. Another important question concerning memory processes, however, is how retrieval criteria are learned. This paper examines the issues involved in modeling the learning of memory search strategies. It discusses the general requirements for appropriate strategy learning and presents a model of memory search strategy learning applied to the problem of retrieving relevant information for adapting cases in case-based reasoning. It discusses an implementation of that model, and, based on the lessons learned from that implementation, points towards issues and directions in refining the model. Please summarise all the given information.

Paper #82666 has the title `Goal-Driven Learning: Fundamental Issues (A Symposium Report)  
` and the abstract `In Artificial Intelligence, Psychology, and Education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with different goals process information differently; studies in education show that goals have strong effects on what students learn; and functional arguments from machine learning support the necessity of goal-based focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goal-driven learning. This article presents the fundamental points illuminated by the symposium, placing them in the context of open questions and current research di rections in goal-driven learning. fl Appears in AI Magazine, 14(4):67-72, 1993
`.
Question: Which category should Paper #82666 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #82666 has the title `Goal-Driven Learning: Fundamental Issues (A Symposium Report)  
` and the abstract `In Artificial Intelligence, Psychology, and Education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with different goals process information differently; studies in education show that goals have strong effects on what students learn; and functional arguments from machine learning support the necessity of goal-based focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goal-driven learning. This article presents the fundamental points illuminated by the symposium, placing them in the context of open questions and current research di rections in goal-driven learning. fl Appears in AI Magazine, 14(4):67-72, 1993
`.
Question: Which category should Paper #82666 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