Above are papers related to the following paper: [Paper_ID] Paper #470511. 	[Title] Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0* [Abstract] Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has the potential of achieving better plans than either the human or the machine can create alone. Human planners often take a case-based approach to planning, relying on their past experience and planning by retrieving and adapting past planning cases. Planning by analogical reasoning in which generative and case-based planning are combined, as in Prodigy/Analogy, provides a suitable framework to study this mixed-initiative integration. However, having a human user engaged in this planning loop creates a variety of new research questions. The challenges we found creating a mixed-initiative planning system fall into three categories: planning paradigms differ in human and machine planning; visualization of the plan and planning process is a complex, but necessary task; and human users range across a spectrum of experience, both with respect to the planning domain and the underlying planning technology. This paper presents our approach to these three problems when designing an interface to incorporate a human into the process of planning by analogical reasoning with Prodigy/Analogy. The interface allows the user to follow both generative and case-based planning, it supports visualization of both plan and the planning rationale, and it addresses the variance in the experience of the user by allowing the user to control the presentation of information. * This research is sponsored as part of the DARPA/RL Knowledge Based Planning and Scheduling Initiative under grant number F30602-95-1-0018. A short version of this document appeared as Cox, M. T., & Veloso, M. M. (1997). Supporting combined human and machine planning: An interface for planning by analogical reasoning. In D. B. Leake & E. Plaza (Eds.), Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning (pp. 531-540). Berlin: Springer-Verlag. 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 #77829. 	[Title] Towards Mixed-Initiative Rationale-Supported Planning [Abstract] This paper introduces our work on mixed-initiative, rationale-supported planning. The work centers on the principled reuse and modification of past plans by exploiting their justification structure. The goal is to record as much as possible of the rationale underlying each planning decision in a mixed-initiative framework where human and machine planners interact. This rationale is used to determine which past plans are relevant to a new situation, to focus user's modification and replanning on different relevant steps when external circumstances dictate, and to ensure consistency in multi-user distributed scenarios. We build upon our previous work in Prodigy/Analogy, which incorporates algorithms to capture and reuse the rationale of an automated planner during its plan generation. To support a mixed-initiative environment, we have developed user interactive capabilities in the Prodigy planning and learning system. We are also working towards the integration of the rationale-supported plan reuse in Prodigy/Analogy with the plan retrieval and modification tools of ForMAT. Finally, we have focused on the user's input into the process of plan reuse, in particular when conditional planning is needed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3112. 	[Title] Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning [Abstract] Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has the potential of achieving better plans than either the human or the machine can create alone. Human planners often take a case-based approach to planning, relying on their past experience and planning by retrieving and adapting past planning cases. Planning by analogical reasoning in which generative and case-based planning are combined, as in Prodigy/Analogy, provides a suitable framework to study this mixed-initiative integration. However, having a human user engaged in this planning loop creates a variety of new research questions. The challenges we found creating a mixed-initiative planning system fall into three categories: planning paradigms differ in human and machine planning; visualization of the plan and planning process is a complex, but necessary task; and human users range across a spectrum of experience, both with respect to the planning domain and the underlying planning technology. This paper presents our approach to these three problems when designing an interface to incorporate a human into the process of planning by analogical reasoning with Prodigy/Analogy. The interface allows the user to follow both generative and case-based planning, it supports visualization of both plan and the planning rationale, and it addresses the variance in the experience of the user by allowing the user to control the presentation of information. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #103529. 	[Title] On the Usefulness of Re-using Diagnostic Solutions [Abstract] Recent studies on planning, comparing plan re-use and plan generation, have shown that both the above tasks may have the same degree of computational complexity, even if we deal with very similar problems. The aim of this paper is to show that the same kind of results apply also for diagnosis. We propose a theoretical complexity analysis coupled with some experimental tests, intended to evaluate the adequacy of adaptation strategies which re-use the solutions of past diagnostic problems in order to build a solution to the problem to be solved. Results of such analysis show that, even if diagnosis re-use falls into the same complexity class of diagnosis generation (they are both NP-complete problems), practical advantages can be obtained by exploiting a hybrid architecture combining case-based and model-based diagnostic problem solving in a unifying framework. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #110163. 	[Title] Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning [Abstract] The case-based reasoning process depends on multiple overlapping knowledge sources, each of which provides an opportunity for learning. Exploiting these opportunities requires not only determining the learning mechanisms to use for each individual knowledge source, but also how the different learning mechanisms interact and their combined utility. This paper presents a case study examining the relative contributions and costs involved in learning processes for three different knowledge sources|cases, case adaptation knowledge, and similarity information|in a case-based planner. It demonstrates the importance of interactions between different learning processes and identifies a promising method for integrating multiple learning methods to improve case-based reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #110164. 	[Title] A Case Study of Case-Based CBR [Abstract] Case-based reasoning depends on multiple knowledge sources beyond the case library, including knowledge about case adaptation and criteria for similarity assessment. Because hand coding this knowledge accounts for a large part of the knowledge acquisition burden for developing CBR systems, it is appealing to acquire it by learning, and CBR is a promising learning method to apply. This observation suggests developing case-based CBR systems, CBR systems whose components themselves use CBR. However, despite early interest in case-based approaches to CBR, this method has received comparatively little attention. Open questions include how case-based components of a CBR system should be designed, the amount of knowledge acquisition effort they require, and their effectiveness. This paper investigates these questions through a case study of issues addressed, methods used, and results achieved by a case-based planning system that uses CBR to guide its case adaptation and similarity assessment. The paper discusses design considerations and presents empirical results that support the usefulness of case-based CBR, that point to potential problems and tradeoffs, and that directly demonstrate the overlapping roles of different CBR knowledge sources. The paper closes with general lessons about case-based CBR and areas for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152564. 	[Title] S o l u t i o n Relevant A b s t r a [Abstract] Two major problems in case-based reasoning are the efficient and justified retrieval of source cases and the adaptation of retrieved solutions to the conditions of the target. For analogical theorem proving by induction, we describe how a solution-relevant abstraction can restrict the retrieval of source cases and the mapping from the source problem to the target problem and how it can determine reformulations that further adapt the source solution. 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 #470511 has the title `Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0*  
` and the abstract `Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has the potential of achieving better plans than either the human or the machine can create alone. Human planners often take a case-based approach to planning, relying on their past experience and planning by retrieving and adapting past planning cases. Planning by analogical reasoning in which generative and case-based planning are combined, as in Prodigy/Analogy, provides a suitable framework to study this mixed-initiative integration. However, having a human user engaged in this planning loop creates a variety of new research questions. The challenges we found creating a mixed-initiative planning system fall into three categories: planning paradigms differ in human and machine planning; visualization of the plan and planning process is a complex, but necessary task; and human users range across a spectrum of experience, both with respect to the planning domain and the underlying planning technology. This paper presents our approach to these three problems when designing an interface to incorporate a human into the process of planning by analogical reasoning with Prodigy/Analogy. The interface allows the user to follow both generative and case-based planning, it supports visualization of both plan and the planning rationale, and it addresses the variance in the experience of the user by allowing the user to control the presentation of information. * This research is sponsored as part of the DARPA/RL Knowledge Based Planning and Scheduling Initiative under grant number F30602-95-1-0018. A short version of this document appeared as Cox, M. T., & Veloso, M. M. (1997). Supporting combined human and machine planning: An interface for planning by analogical reasoning. In D. B. Leake & E. Plaza (Eds.), Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning (pp. 531-540). Berlin: Springer-Verlag. 
`.
Question: Which category should Paper #470511 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #470511 has the title `Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0*  
` and the abstract `Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has the potential of achieving better plans than either the human or the machine can create alone. Human planners often take a case-based approach to planning, relying on their past experience and planning by retrieving and adapting past planning cases. Planning by analogical reasoning in which generative and case-based planning are combined, as in Prodigy/Analogy, provides a suitable framework to study this mixed-initiative integration. However, having a human user engaged in this planning loop creates a variety of new research questions. The challenges we found creating a mixed-initiative planning system fall into three categories: planning paradigms differ in human and machine planning; visualization of the plan and planning process is a complex, but necessary task; and human users range across a spectrum of experience, both with respect to the planning domain and the underlying planning technology. This paper presents our approach to these three problems when designing an interface to incorporate a human into the process of planning by analogical reasoning with Prodigy/Analogy. The interface allows the user to follow both generative and case-based planning, it supports visualization of both plan and the planning rationale, and it addresses the variance in the experience of the user by allowing the user to control the presentation of information. * This research is sponsored as part of the DARPA/RL Knowledge Based Planning and Scheduling Initiative under grant number F30602-95-1-0018. A short version of this document appeared as Cox, M. T., & Veloso, M. M. (1997). Supporting combined human and machine planning: An interface for planning by analogical reasoning. In D. B. Leake & E. Plaza (Eds.), Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning (pp. 531-540). Berlin: Springer-Verlag. 
`.
Question: Which category should Paper #470511 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