Above are papers related to the following paper: [Paper_ID] Paper #219976. 	[Title] Rule Based Database Integration in HIPED Heterogeneous Intelligent Processing in Engineering Design [Abstract] In this paper 1 we describe one aspect of our research in the project called HIPED, which addressed the problem of performing design of engineering devices by accessing heterogeneous databases. The front end of the HIPED system consisted of interactive KRI-TIK, a multimodal reasoning system that combined case based and model based reasoning to solve a design problem. This paper focuses on the backend processing where five types of queries received from the front end are evaluated by mapping them appropriately using the "facts" about the schemas of the underlying databases and "rules" that establish the correspondance among the data in these databases in terms of relationships such as equivalence, overlap and set containment. The uniqueness of our approach stems from the fact that the mapping process is very forgiving in that the query received from the front end is evaluated with respect to a large number of possibilities. These possibilities are encoded in the form of rules that consider various ways in which the tokens in the given query may match relation names, attrribute names, or values in the underlying tables. The approach has been implemented using CORAL deductive database system as the rule processing engine. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35490. 	[Title] A Model-Based Approach to Blame-Assignment in Design [Abstract] We analyze the blame-assignment task in the context of experience-based design and redesign of physical devices. We identify three types of blame-assignment tasks that differ in the types of information they take as input: the design does not achieve a desired behavior of the device, the design results in an undesirable behavior, a specific structural element in the design misbehaves. We then describe a model-based approach for solving the blame-assignment task. This approach uses structure-behavior-function models that capture a designer's comprehension of the way a device works in terms of causal explanations of how its structure results in its behaviors. We also address the issue of indexing the models in memory. We discuss how the three types of blame-assignment tasks require different types of indices for accessing the models. Finally we describe the KRITIK2 system that implements and evaluates this model-based approach to blame assignment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #91853. 	[Title] GIT-CC-92/60 A Model-Based Approach to Analogical Reasoning and Learning in Design [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #95188. 	[Title] KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM [Abstract] In the late 1980s, we developed one of the early case-based design systems called Kritik. Kritik autonomously generated preliminary (conceptual, qualitative) designs for physical devices by retrieving and adapting past designs stored in its case memory. Each case in the system had an associated structure-behavior-function (SBF) device model that explained how the structure of the device accomplished its functions. These casespecific device models guided the process of modifying a past design to meet the functional specification of a new design problem. The device models also enabled verification of the design modifications. Kritik2 is a new and more complete implementation of Kritik. In this paper, we take a retrospective view on Kritik. In early papers, we had described Kritik as integrating case-based and model-based reasoning. In this integration, Kritik also grounds the computational process of case-based reasoning in the SBF content theory of device comprehension. The SBF models not only provide methods for many specific tasks in case-based design such as design adaptation and verification, but they also provide the vocabulary for the whole process of case-based design, from retrieval of old cases to storage of new ones. This grounding, we believe, is essential for building well-constrained theories of case-based design. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116410. 	[Title] METHOD-SPECIFIC KNOWLEDGE COMPILATION: TOWARDS PRACTICAL DESIGN SUPPORT SYSTEMS [Abstract] Modern knowledge systems for design typically employ multiple problem-solving methods which in turn use different kinds of knowledge. The construction of a heterogeneous knowledge system that can support practical design thus raises two fundamental questions: how to accumulate huge volumes of design information, and how to support heterogeneous design processing? Fortunately, partial answers to both questions exist separately. Legacy databases already contain huge amounts of general-purpose design information. In addition, modern knowledge systems typically characterize the kinds of knowledge needed by specific problem-solving methods quite precisely. This leads us to hypothesize method-specific data-to-knowledge compilation as a potential mechanism for integrating heterogeneous knowledge systems and legacy databases for design. In this paper, first we outline a general computational architecture called HIPED for this integration. Then, we focus on the specific issue of how to convert data accessed from a legacy database into a form appropriate to the problem-solving method used in a heterogeneous knowledge system. We describe an experiment in which a legacy knowledge system called Interactive Kritik is integrated with an ORACLE database using IDI as the communication tool. The limited experiment indicates the computational feasibility of method-specific data-to-knowledge compilation, but also raises additional research issues. Please summarise all the given information.

Paper #219976 has the title `Rule Based Database Integration in HIPED Heterogeneous Intelligent Processing in Engineering Design  
` and the abstract `In this paper 1 we describe one aspect of our research in the project called HIPED, which addressed the problem of performing design of engineering devices by accessing heterogeneous databases. The front end of the HIPED system consisted of interactive KRI-TIK, a multimodal reasoning system that combined case based and model based reasoning to solve a design problem. This paper focuses on the backend processing where five types of queries received from the front end are evaluated by mapping them appropriately using the "facts" about the schemas of the underlying databases and "rules" that establish the correspondance among the data in these databases in terms of relationships such as equivalence, overlap and set containment. The uniqueness of our approach stems from the fact that the mapping process is very forgiving in that the query received from the front end is evaluated with respect to a large number of possibilities. These possibilities are encoded in the form of rules that consider various ways in which the tokens in the given query may match relation names, attrribute names, or values in the underlying tables. The approach has been implemented using CORAL deductive database system as the rule processing engine. 
`.
Question: Which category should Paper #219976 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #219976 has the title `Rule Based Database Integration in HIPED Heterogeneous Intelligent Processing in Engineering Design  
` and the abstract `In this paper 1 we describe one aspect of our research in the project called HIPED, which addressed the problem of performing design of engineering devices by accessing heterogeneous databases. The front end of the HIPED system consisted of interactive KRI-TIK, a multimodal reasoning system that combined case based and model based reasoning to solve a design problem. This paper focuses on the backend processing where five types of queries received from the front end are evaluated by mapping them appropriately using the "facts" about the schemas of the underlying databases and "rules" that establish the correspondance among the data in these databases in terms of relationships such as equivalence, overlap and set containment. The uniqueness of our approach stems from the fact that the mapping process is very forgiving in that the query received from the front end is evaluated with respect to a large number of possibilities. These possibilities are encoded in the form of rules that consider various ways in which the tokens in the given query may match relation names, attrribute names, or values in the underlying tables. The approach has been implemented using CORAL deductive database system as the rule processing engine. 
`.
Question: Which category should Paper #219976 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