Above are papers related to the following paper: [Paper_ID] Paper #255233. 	[Title] An Explanation-Based Approach to Improve Retrieval in Case-Based Planning [Abstract] When a case-based planner is retrieving a previous case in preparation for solving a new similar problem, it is often not aware of the implicit features of the new problem situation which determine if a particular case may be successfully applied. This means that some cases may be retrieved in error in that the case may fail to improve the planner's performance. Retrieval may be incrementally improved by detecting and explaining these failures as they occur. In this paper we provide a definition of case failure for the planner, dersnlp (derivation replay in snlp), which solves new problems by replaying its previous plan derivations. We provide EBL (explanation-based learning) techniques for detecting and constructing the reasons for the failure. We also describe how to organize a case library so as to incorporate this failure information as it is produced. Finally we present an empirical study which demonstrates the effectiveness of this approach in improving the performance of dersnlp. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5600. 	[Title] Evaluating the Effectiveness of Derivation Replay in Partial-order vs State-space Planning [Abstract] Case-based planning involves storing individual instances of problem-solving episodes and using them to tackle new planning problems. This paper is concerned with derivation replay, which is the main component of a form of case-based planning called derivational analogy (DA). Prior to this study, implementations of derivation replay have been based within state-space planning. We are motivated by the acknowledged superiority of partial-order (PO) planners in plan generation. Here we demonstrate that plan-space planning also has an advantage in replay. We will argue that the decoupling of planning (derivation) order and the execution order of plan steps, provided by partial-order planners, enables them to exploit the guidance of previous cases in a more efficient and straightforward fashion. We validate our hypothesis through a focused empirical comparison. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #77826. 	[Title] Merge Strategies for Multiple Case Plan Replay [Abstract] Planning by analogical reasoning is a learning method that consists of the storage, retrieval, and replay of planning episodes. Planning performance improves with the accumulation and reuse of a library of planning cases. Retrieval is driven by domain-dependent similarity metrics based on planning goals and scenarios. In complex situations with multiple goals, retrieval may find multiple past planning cases that are jointly similar to the new planning situation. This paper presents the issues and implications involved in the replay of multiple planning cases, as opposed to a single one. Multiple case plan replay involves the adaptation and merging of the annotated derivations of the planning cases. Several merge strategies for replay are introduced that can process with various forms of eagerness the differences between the past and new situations and the annotated justifications at the planning cases. In particular, we introduce an effective merging strategy that considers plan step choices especially appropriate for the interleaving of planning and plan execution. We illustrate and discuss the effectiveness of the merging strategies in specific domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107171. 	[Title] A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture [Abstract] Case-based reasoning (CBR) can be used as a form of "caching" solved problems to speedup later problem solving. Using "cached" cases brings additional costs with it due to retrieval time, case adaptation time and also storage space. Simply storing all cases will result in a situation in which retrieving and trying to adapt old cases will take more time (on average) than not caching at all. This means that caching must be applied selectively to build a case memory that is actually useful. This is a form of the utility problem [4, 2]. The approach taken here is to construct a "cost model" of a system that can be used to predict the effect of changes to the system. In this paper we describe the utility problem associated with "caching" cases and the construction of a "cost model". We present experimental results that demonstrate that the model can be used to predict the effect of certain changes to the case memory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #936. 	[Title] The Use of Explicit Goals for Knowledge to Guide Inference and Learning [Abstract] Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infinite), the inferential resources available to any reasoning system are limited. With limited inferential capacity and very many potential inferences, reasoners must somehow control the process of inference. Not all inferences are equally useful to a given reasoning system. Any reasoning system that has goals (or any form of a utility function) and acts based on its beliefs indirectly assigns utility to its beliefs. Given limits on the process of inference, and variation in the utility of inferences, it is clear that a reasoner ought to draw the inferences that will be most valuable to it. This paper presents an approach to this problem that makes the utility of a (potential) belief an explicit part of the inference process. The method is to generate explicit desires for knowledge. The question of focus of attention is thereby transformed into two related problems: How can explicit desires for knowledge be used to control inference and facilitate resource-constrained goal pursuit in general? and, Where do these desires for knowledge come from? We present a theory of knowledge goals, or desires for knowledge, and their use in the processes of understanding and learning. The theory is illustrated using two case studies, a natural language understanding program that learns by reading novel or unusual newspaper stories, and a differential diagnosis program that improves its accuracy with experience. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1117833. 	[Title] Automatic Storage and Indexing of Plan Derivations based on Replay Failures [Abstract] When a case-based planner is retrieving a previous case in preparation for solving a new similar problem, it is often not aware of all of the implicit features of the new problem situation which determine if a particular case may be successfully applied. This means that some cases may fail to improve the planner's performance. By detecting and explaining these case failures as they occur, retrieval may be improved incrementally. In this paper we provide a definition of case failure for the case-based planner, dersnlp (derivation replay in snlp), which solves new problems by replaying its previous plan derivations. We provide explanation-based learning (EBL) techniques for detecting and constructing the reasons for the case failure. We also describe how the case library is organized so as to incorporate this failure information as it is produced. Finally we present an empirical study which demonstrates the effectiveness of this approach in improving the performance of dersnlp. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3084. 	[Title] A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems [Abstract] The utility problem in learning systems occurs when knowledge learned in an attempt to improve a system's performance degrades performance instead. We present a methodology for the analysis of utility problems which uses computational models of problem solving systems to isolate the root causes of a utility problem, to detect the threshold conditions under which the problem will arise, and to design strategies to eliminate it. We present models of case-based reasoning and control-rule learning systems and compare their performance with respect to the swamping utility problem. Our analysis suggests that case-based reasoning systems are more resistant to the utility problem than control-rule learning systems. 1 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33301. 	[Title] THE DESIGN AND IMPLEMENTATION OF A CASE-BASED PLANNING FRAMEWORK WITHIN A PARTIAL-ORDER PLANNER [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #54550. 	[Title] Analysis and Empirical Studies of Derivational Analogy [Abstract] Derivational analogy is a technique for reusing problem solving experience to improve problem solving performance. This research addresses an issue common to all problem solvers that use derivational analogy: overcoming the mismatches between past experiences and new problems that impede reuse. First, this research describes the variety of mismatches that can arise and proposes a new approach to derivational analogy that uses appropriate adaptation strategies for each. Second, it compares this approach with seven others in a common domain. This empirical study shows that derivational analogy is almost always more efficient than problem solving from scratch, but the amount it contributes depends on its ability to overcome mismatches Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33303. 	[Title] Design and Implementation of a Replay Framework based on a Partial Order Planner [Abstract] In this paper we describe the design and implementation of the derivation replay framework, dersnlp+ebl (Derivational snlp+ebl), which is based within a partial order planner. dersnlp+ebl replays previous plan derivations by first repeating its earlier decisions in the context of the new problem situation, then extending the replayed path to obtain a complete solution for the new problem. When the replayed path cannot be extended into a new solution, explanation-based learning (ebl) techniques are employed to identify the features of the new problem which prevent this extension. These features are then added as censors on the retrieval of the stored case. To keep retrieval costs low, dersnlp+ebl normally stores plan derivations for individual goals, and replays one or more of these derivations in solving multi-goal problems. Cases covering multiple goals are stored only when subplans for individual goals cannot be successfully merged. The aim in constructing the case library is to predict these goal interactions and to store a multi-goal case for each set of negatively interacting goals. We provide empirical results demonstrating the effectiveness of dersnlp+ebl in improving planning performance on randomly-generated problems drawn from a complex domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1022969. 	[Title] Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures [Abstract] Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner dersnlp+ebl. der-snlp+ebl extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #143323. 	[Title] AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING [Abstract] The utility problem in speedup learning describes a common behavior of machine learning methods: the eventual degradation of performance due to increasing amounts of learned knowledge. The shape of the learning curve (cost of using a learning method vs. number of training examples) over several domains suggests a parameterized model relating performance to the amount of learned knowledge and a mechanism to limit the amount of learned knowledge for optimal performance. Many recent approaches to avoiding the utility problem in speedup learning rely on sophisticated utility measures and significant numbers of training data to accurately estimate the utility of control knowledge. Empirical results presented here and elsewhere indicate that a simple selection strategy of retaining all control rules derived from a training problem explanation quickly defines an efficient set of control knowledge from few training problems. This simple selection strategy provides a low-cost alternative to example-intensive approaches for improving the speed of a problem solver. Experimentation illustrates the existence of a minimum (representing least cost) in the learning curve which is reached after a few training examples. Stress is placed on controlling the amount of learned knowledge as opposed to which knowledge. An attempt is also made to relate domain characteristics to the shape of the learning curve. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #198653. 	[Title] Information filtering: Selection mechanisms in learning systems. Machine Learning, 10:113-151, 1993. Generalization as search. Artificial [Abstract]  Please summarise all the given information.

Paper #255233 has the title `An Explanation-Based Approach to Improve Retrieval in Case-Based Planning  
` and the abstract `When a case-based planner is retrieving a previous case in preparation for solving a new similar problem, it is often not aware of the implicit features of the new problem situation which determine if a particular case may be successfully applied. This means that some cases may be retrieved in error in that the case may fail to improve the planner's performance. Retrieval may be incrementally improved by detecting and explaining these failures as they occur. In this paper we provide a definition of case failure for the planner, dersnlp (derivation replay in snlp), which solves new problems by replaying its previous plan derivations. We provide EBL (explanation-based learning) techniques for detecting and constructing the reasons for the failure. We also describe how to organize a case library so as to incorporate this failure information as it is produced. Finally we present an empirical study which demonstrates the effectiveness of this approach in improving the performance of dersnlp.
`.
Question: Which category should Paper #255233 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #255233 has the title `An Explanation-Based Approach to Improve Retrieval in Case-Based Planning  
` and the abstract `When a case-based planner is retrieving a previous case in preparation for solving a new similar problem, it is often not aware of the implicit features of the new problem situation which determine if a particular case may be successfully applied. This means that some cases may be retrieved in error in that the case may fail to improve the planner's performance. Retrieval may be incrementally improved by detecting and explaining these failures as they occur. In this paper we provide a definition of case failure for the planner, dersnlp (derivation replay in snlp), which solves new problems by replaying its previous plan derivations. We provide EBL (explanation-based learning) techniques for detecting and constructing the reasons for the failure. We also describe how to organize a case library so as to incorporate this failure information as it is produced. Finally we present an empirical study which demonstrates the effectiveness of this approach in improving the performance of dersnlp.
`.
Question: Which category should Paper #255233 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