Above are papers related to the following paper: [Paper_ID] Paper #1108363. 	[Title] Synergy and Commonality in Case-Based and Constraint-Based Reasoning [Abstract] Although Case-Based Reasoning (CBR) is a natural formulation for many problems, our previous work on CBR as applied to design made it apparent that there were elements of the CBR paradigm that prevented it from being more widely applied. At the same time, we were evaluating Constraint Satisfaction techniques for design, and found a commonality in motivation between repair-based constraint satisfaction problems (CSP) and case adaptation. This led us to combine the two methodologies in order to gain the advantages of CSP for case-based reasoning, allowing CBR to be more widely and flexibly applied. In combining the two methodologies, we found some unexpected synergy and commonality between the approaches. This paper describes the synergy and commonality that emerged as we combined case-based and constraint-based reasoning, and gives a brief overview of our continuing and future work on exploiting the emergent synergy when combining these reasoning modes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1121313. 	[Title] A CBR Integration From Inception to Productization [Abstract] Our case-based reasoning (CBR) integration with the constraint satisfaction problem (CSP) formalism has undergone several transformations on its journey from initial research idea to product-intent design. Both unexpected research results as well as interesting insights into the real-world applicability of the integrated methodology emerged as the integration was explored from alternative viewpoints. In this paper, the alternative viewpoints and the results that were enabled by these viewpoints are described. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #129042. 	[Title] Towards Improving Case Adaptability with a Genetic Algorithm [Abstract] Case combination is a difficult problem in Case Based Reasoning, as sub-cases often exhibit conflicts when merged together. In our previous work we formalized case combination by representing each case as a constraint satisfaction problem, and used the minimum conflicts algorithm to systematically synthesize the global solution. However, we also found instances of the problem in which the minimum conflicts algorithm does not perform case combination efficiently. In this paper we describe those situations in which initially retrieved cases are not easily adaptable, and propose a method by which to improve case adaptability with a genetic algorithm. We introduce a fitness function that maintains as much retrieved case information as possible, while also perturbing a sub-solution to allow subsequent case combination to proceed more efficiently. 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 #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.

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 #1108363 has the title `Synergy and Commonality in Case-Based and Constraint-Based Reasoning  
` and the abstract `Although Case-Based Reasoning (CBR) is a natural formulation for many problems, our previous work on CBR as applied to design made it apparent that there were elements of the CBR paradigm that prevented it from being more widely applied. At the same time, we were evaluating Constraint Satisfaction techniques for design, and found a commonality in motivation between repair-based constraint satisfaction problems (CSP) and case adaptation. This led us to combine the two methodologies in order to gain the advantages of CSP for case-based reasoning, allowing CBR to be more widely and flexibly applied. In combining the two methodologies, we found some unexpected synergy and commonality between the approaches. This paper describes the synergy and commonality that emerged as we combined case-based and constraint-based reasoning, and gives a brief overview of our continuing and future work on exploiting the emergent synergy when combining these reasoning modes. 
`.
Question: Which category should Paper #1108363 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1108363 has the title `Synergy and Commonality in Case-Based and Constraint-Based Reasoning  
` and the abstract `Although Case-Based Reasoning (CBR) is a natural formulation for many problems, our previous work on CBR as applied to design made it apparent that there were elements of the CBR paradigm that prevented it from being more widely applied. At the same time, we were evaluating Constraint Satisfaction techniques for design, and found a commonality in motivation between repair-based constraint satisfaction problems (CSP) and case adaptation. This led us to combine the two methodologies in order to gain the advantages of CSP for case-based reasoning, allowing CBR to be more widely and flexibly applied. In combining the two methodologies, we found some unexpected synergy and commonality between the approaches. This paper describes the synergy and commonality that emerged as we combined case-based and constraint-based reasoning, and gives a brief overview of our continuing and future work on exploiting the emergent synergy when combining these reasoning modes. 
`.
Question: Which category should Paper #1108363 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