Above are papers related to the following paper: [Paper_ID] Paper #1153724. 	[Title] Abstract [Abstract] We describe an ongoing project to develop an adaptive training system (ATS) that dynamically models a students learning processes and can provide specialized tutoring adapted to a students knowledge state and learning style. The student modeling component of the ATS, ML-Modeler, uses machine learning (ML) techniques to emulate the students novice-to-expert transition. ML-Modeler infers which learning methods the student has used to reach the current knowledge state by comparing the students solution trace to an expert solution and generating plausible hypotheses about what misconceptions and errors the student has made. A case-based approach is used to generate hypotheses through incorrectly applying analogy, overgeneralization, and overspecialization. The student and expert models use a network-based representation that includes abstract concepts and relationships as well as strategies for problem solving. Fuzzy methods are used to represent the uncertainty in the student model. This paper describes the design of the ATS and ML-Modeler, and gives a detailed example of how the system would model and tutor the student in a typical session. The domain we use for this example is high-school level chemistry. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153728. 	[Title] Abstract [Abstract] Metacognition addresses the issues of knowledge about cognition and regulating cognition. We argue that the regulation process should be improved with growing experience. Therefore mental models are needed which facilitate the re-use of previous regulation processes. We will satisfy this requirement by describing a case-based approach to Introspection Planning which utilises previous experience obtained during reasoning at the meta-level and at the object level. The introspection plans used in this approach support various metacognitive tasks which are identified by the generation of self-questions. As an example of introspection planning, the metacognitive behaviour of our system, IULIAN, is described. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1953. 	[Title] Representing Self-knowledge for Introspection about Memory Search [Abstract] This position paper sketches a framework for modeling introspective reasoning and discusses the relevance of that framework for modeling introspective reasoning about memory search. It argues that effective and flexible memory processing in rich memories should be built on five types of explicitly represented self-knowledge: knowledge about information needs, relationships between different types of information, expectations for the actual behavior of the information search process, desires for its ideal behavior, and representations of how those expectations and desires relate to its actual performance. This approach to modeling memory search is both an illustration of general principles for modeling introspective reasoning and a step towards addressing the problem of how a reasoner human or machinecan acquire knowledge about the properties of its own knowledge base. 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 #1153166. 	[Title] Abstract [Abstract] We describe an ongoing project to develop an adaptive training system (ATS) that dynamically models a students learning processes and can provide specialized tutoring adapted to a students knowledge state and learning style. The student modeling component of the ATS, ML-Modeler, uses machine learning (ML) techniques to emulate the students novice-to-expert transition. ML-Modeler infers which learning methods the student has used to reach the current knowledge state by comparing the students solution trace to an expert solution and generating plausible hypotheses about what misconceptions and errors the student has made. A case-based approach is used to generate hypotheses through incorrectly applying analogy, overgeneralization, and overspecialization. The student and expert models use a network-based representation that includes abstract concepts and relationships as well as strategies for problem solving. Fuzzy methods are used to represent the uncertainty in the student model. This paper describes the design of the ATS and ML-Modeler, and gives a detailed example of how the system would model and tutor the student in a typical session. The domain we use for this example is high-school level chemistry. 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 #64271. 	[Title] Generic Teleological Mechanisms and their Use in Case Adaptation [Abstract] In experience-based (or case-based) reasoning, new problems are solved by retrieving and adapting the solutions to similar problems encountered in the past. An important issue in experience-based reasoning is to identify different types of knowledge and reasoning useful for different classes of case-adaptation tasks. In this paper, we examine a class of non-routine case-adaptation tasks that involve patterned insertions of new elements in old solutions. We describe a model-based method for solving this task in the context of the design of physical devices. The method uses knowledge of generic teleological mechanisms (GTMs) such as cascading. Old designs are adapted to meet new functional specifications by accessing and instantiating the appropriate GTM. The Kritik2 system evaluates the computational feasibility and sufficiency of this method for design adaptation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #198866. 	[Title] Using Knowledge of Cognitive Behavior to Learn from Failure [Abstract] When learning from reasoning failures, knowledge of how a system behaves is a powerful lever for deciding what went wrong with the system and in deciding what the system needs to learn. A number of benefits arise when systems possess knowledge of their own operation and of their own knowledge. Abstract knowledge about cognition can be used to select diagnosis and repair strategies from among alternatives. Specific kinds of self-knowledge can be used to distinguish between failure hypothesis candidates. Making self-knowledge explicit can also facilitate the use of such knowledge across domains and can provide a principled way to incorporate new learning strategies. To illustrate the advantages of self-knowledge for learning, we provide implemented examples from two different systems: A plan execution system called RAPTER and a story understanding system called Meta-AQUA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105810. 	[Title] Using Case-Based Reasoning for Mobile Robot Navigation [Abstract] This paper presents an approach to mobile robot path planning using case-based reasoning together with map-based path planning. The map-based path planner is used to seed the case-base with innovative solutions. The casebase stores the paths and the information about their traversability. While planning the route those paths are preferred that according to the former experience are least risky. 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 #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.

Paper #1153724 has the title `Abstract  
` and the abstract `We describe an ongoing project to develop an adaptive training system (ATS) that dynamically models a students learning processes and can provide specialized tutoring adapted to a students knowledge state and learning style. The student modeling component of the ATS, ML-Modeler, uses machine learning (ML) techniques to emulate the students novice-to-expert transition. ML-Modeler infers which learning methods the student has used to reach the current knowledge state by comparing the students solution trace to an expert solution and generating plausible hypotheses about what misconceptions and errors the student has made. A case-based approach is used to generate hypotheses through incorrectly applying analogy, overgeneralization, and overspecialization. The student and expert models use a network-based representation that includes abstract concepts and relationships as well as strategies for problem solving. Fuzzy methods are used to represent the uncertainty in the student model. This paper describes the design of the ATS and ML-Modeler, and gives a detailed example of how the system would model and tutor the student in a typical session. The domain we use for this example is high-school level chemistry. 
`.
Question: Which category should Paper #1153724 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1153724 has the title `Abstract  
` and the abstract `We describe an ongoing project to develop an adaptive training system (ATS) that dynamically models a students learning processes and can provide specialized tutoring adapted to a students knowledge state and learning style. The student modeling component of the ATS, ML-Modeler, uses machine learning (ML) techniques to emulate the students novice-to-expert transition. ML-Modeler infers which learning methods the student has used to reach the current knowledge state by comparing the students solution trace to an expert solution and generating plausible hypotheses about what misconceptions and errors the student has made. A case-based approach is used to generate hypotheses through incorrectly applying analogy, overgeneralization, and overspecialization. The student and expert models use a network-based representation that includes abstract concepts and relationships as well as strategies for problem solving. Fuzzy methods are used to represent the uncertainty in the student model. This paper describes the design of the ATS and ML-Modeler, and gives a detailed example of how the system would model and tutor the student in a typical session. The domain we use for this example is high-school level chemistry. 
`.
Question: Which category should Paper #1153724 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