Explanation-driven case-based reasoning. (1994)

by A Aamodt
Venue:Topics in Case-Based Reasoning,
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CBR in Context: The Present and Future

by David B. Leake, Reasoning From Remindings , 1996
"... This chapter provides an introduction to case-based reasoning, discusses motivations for CBR, and describes the central steps in the CBR process. It examines the relationship of CBR to other approaches and discusses major research areas, open issues, and promising opportunities for CBR. It surveys a ..."
Abstract - Cited by 84 (7 self) - Add to MetaCart
This chapter provides an introduction to case-based reasoning, discusses motivations for CBR, and describes the central steps in the CBR process. It examines the relationship of CBR to other approaches and discusses major research areas, open issues, and promising opportunities for CBR. It surveys and relates numerous approaches within CBR and provides more than 150 references to international CBR research.
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Introspective Multistrategy Learning: Constructing a Learnung Strategy under Reasoning Failure

by Michael Thomas Cox, Kurt Eiselt, Janet Kolodner, Nancy Nersessian, Margaret Recker, Tony Simon - Artificial Intelligence , 1996
"... Officer praised dog for barking at object." Enables Detect Drugs out FK Initiates Retrieval 5 6 Missing Figure 10. Forgetting to fill the tank with gas A=actual intention; E=expectation; Q=question; C=context; I=index; G=goal Tank Out of Gas Tank Full Tank Low Fill Tank ..."
Abstract - Cited by 82 (30 self) - Add to MetaCart
Officer praised dog for barking at object." Enables Detect Drugs out FK Initiates Retrieval 5 6 Missing Figure 10. Forgetting to fill the tank with gas A=actual intention; E=expectation; Q=question; C=context; I=index; G=goal Tank Out of Gas Tank Full Tank Low Fill Tank Should have filled up with gas when tank low Expectation What Action to Do? KEY: G = goal; I = index; C = context; Q = question; E = expectation; A = actual intention Results At Store connections with related concepts. Other learning goals take multiple arguments. For instance, a knowledge differentiation goal (Cox & Ram, 1995) is a goal to determine a change in a body of knowledge such that two items are separated conceptually. In contrast, a knowledge reconciliation goal (Cox & Ram, 1995) is one that seeks to merge two items that were mistakenly considered separate entities. Both expansion goals and reconciliation goals may include or spawn a knowledge organization goal (Ram, 1993) that seeks to reorganize the existing knowledge so that it is made available to the reasoner at the appropriate time, as well as modify the structure or content of a concept itself. Such reorganization of knowledge affects the conditions under which a particular piece of knowledge is retrieved or the kinds of indexes associated with an item in memory.

Adaptation-guided retrieval: Questioning the similarity assumption in reasoning

by Barry Smyth, Mark T. Keane - Artificial Intelligence , 1998
"... One of the major assumptions in Artificial Intelligence is that similar experiences can guide future reasoning, problem solving and learning; what we will call, the similarity assumption. The similarity assumption is used in problem solving and reasoning systems when target problems are dealt with b ..."
Abstract - Cited by 55 (10 self) - Add to MetaCart
One of the major assumptions in Artificial Intelligence is that similar experiences can guide future reasoning, problem solving and learning; what we will call, the similarity assumption. The similarity assumption is used in problem solving and reasoning systems when target problems are dealt with by resorting to a previous situation with common conceptual features. In this article, we question this assumption in the context of case-based reasoning (CBR). In CBR, the similarity assumption plays a central role when new problems are solved, by retrieving similar cases and adapting their solutions. The success of any CBR system is contingent on the retrieval of a case that can be successfully reused to solve the target problem. We show that it is unwarranted to assume that the most similar case is also the most appropriate from a reuse perspective. We argue that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem. We implement this idea in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs. This technique uses specially formulated adaptation knowledge, which, during retrieval, facilitates the computation of a precise measure of a case’s adaptation requirements. In closing, we assess the broader implications of AGR and argue that it is just one of a growing number of methods that seek to overcome the limitations of the traditional, similarity assumption in an effort to deliver more sophisticated and scaleable reasoning systems. Smyth & Keane 3 Adaptation-Guided Retrieval 1
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Cased-Based reasoning in CARE-PARTNER: gathering evidence for evidence-based medical practice

by Isabelle Bichindaritz, Emin Kansu, Keith M. Sullivan - Cunningham (Eds.), Proceedings of 4th European Workshop on CBR , 1998
"... Abstract. This paper presents the CARE-PARTNER system. Functionally, it offers via the WWW knowledge-support assistance to clinicians responsible for the long-term follow-up of stem-cell post-transplant patient care. CARE-PARTNER aims at implementing the concept of evidence-based medical practice, w ..."
Abstract - Cited by 35 (3 self) - Add to MetaCart
Abstract. This paper presents the CARE-PARTNER system. Functionally, it offers via the WWW knowledge-support assistance to clinicians responsible for the long-term follow-up of stem-cell post-transplant patient care. CARE-PARTNER aims at implementing the concept of evidence-based medical practice, which recommends the practice of medicine based on proven and validated knowledge. From an artificial intelligence viewpoint, it proposes a multimodal reasoning framework for the cooperation of case-based reasoning, rule-based reasoning and information retrieval to solve problems. The role of case-based reasoning is presented in this paper as the collection of evidence for evidence-based medical practice. Case-based reasoning permits to refine and complete the knowledge of the system. It enhances the system by conferring an ability to learn from experience, and thus improve results over time. 1
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Different roles and mutual dependencies of data, information, and knowledge -- an AI perspective on their integration

by Agnar Aamodt , Mads Nygård , 1995
"... The unclear distinction between data, information, and knowledge has impaired their combination and utilization for the development of integrated systems. There is need for a unified definitional model of data, information, and knowledge based on their roles in computational and cognitive informatio ..."
Abstract - Cited by 31 (0 self) - Add to MetaCart
The unclear distinction between data, information, and knowledge has impaired their combination and utilization for the development of integrated systems. There is need for a unified definitional model of data, information, and knowledge based on their roles in computational and cognitive information processing. An attempt to clarify these basic notions is made, and a conceptual framework for integration is suggested by focusing on their different roles and frames of reference within a decision-making process. On this basis, ways of integrating the functionalities of databases, information systems and knowledge-based systems are discussed by taking a knowledge level perspective to the analysis and modeling of systems behaviour. Motivated by recent work in the area of case-based reasoning related to decision support systems, it is further shown that a specific problem solving episode, or case, may be viewed as data, information, or knowledge, depending on its role in decision making and...

Explanations and case-based reasoning: Foundational issues

by Thomas R. Roth-berghofer - Berlin / Heidelberg , 2004
"... Abstract. By design, Case-Based Reasoning (CBR) systems do not need deep general knowledge. In contrast to (rule-based) expert systems, CBR systems can already be used with just some initial knowledge. Fur-ther knowledge can then be added manually or learned over time. CBR systems are not addressing ..."
Abstract - Cited by 29 (7 self) - Add to MetaCart
Abstract. By design, Case-Based Reasoning (CBR) systems do not need deep general knowledge. In contrast to (rule-based) expert systems, CBR systems can already be used with just some initial knowledge. Fur-ther knowledge can then be added manually or learned over time. CBR systems are not addressing a special group of users. Expert systems, on the other hand, are intended to solve problems similar to human ex-perts. Because of the complexity and difficulty of building and using expert systems, research in this area addressed generating explanations right from the beginning. But for knowledge-intensive CBR applications, the demand for explanations is also growing. This paper is a first pass on examining issues concerning explanations produced by CBR systems from the knowledge containers perspective. It discusses what naturally can be explained by each of the four knowledge containers (vocabulary, similarity measures, adaptation knowledge, and case base) in relation to scientific, conceptual, and cognitive explanations. 1
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Stress and the women manager

by Tessa Cooper, Cynthia R. Marling, Dennis Irwin , 1983
"... Adaptation is a difficult part of the case-based reasoning (CBR) cycle that is often omitted in medical domains. The CBR paradigm solves new problems based on the solutions to similar, past problems. Adaptation is the process of modifying a past solution to fit a new problem. This thesis identifies ..."
Abstract - Cited by 25 (2 self) - Add to MetaCart
Adaptation is a difficult part of the case-based reasoning (CBR) cycle that is often omitted in medical domains. The CBR paradigm solves new problems based on the solutions to similar, past problems. Adaptation is the process of modifying a past solution to fit a new problem. This thesis identifies the components of solutions and the ways in which they can be modified for an intelligent decision support system for type 1 diabetes management. Type 1 diabetes is an autoimmune disease which affects nearly one million Americans. Maintaining good blood glucose control is essential for these patients to avoid serious complications of the disease. A prototypical CBR system has been built that identifies problems the patient is experiencing with blood glucose control and finds similar past problems with solutions to alleviate these problems. This research was conducted in the context of this prototypical system. This thesis contributes the design, implementation and evaluation of an adaptation module, which tailors past advice to a specific patient for a particular problem. Previously, only a closely matching problem and its unmodified solution could be viewed. Physicians verified that adapted solutions
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Web Service Composition with Case-Based Reasoning

by Benchaphon Limthanmaphon, Yanchun Zhang , 2003
"... To run a smart E-Business or provide efficient Web service, a web services composition model is needed. Web services composition refers to the process of collaborating the heterogeneous web services. This paper presents a model of web services composition by using Case-Based Reasoning (CBR) techniqu ..."
Abstract - Cited by 23 (2 self) - Add to MetaCart
To run a smart E-Business or provide efficient Web service, a web services composition model is needed. Web services composition refers to the process of collaborating the heterogeneous web services. This paper presents a model of web services composition by using Case-Based Reasoning (CBR) techniques. CBR is applied in the process of service discovery, which is the crucial composition process. Our service composition model integrates the two behaviours of proactive and reactive service compositions. We will address dynamic composition and collaboration among services. The similarity feature of CBR is used for efficient service discovery .

Context: Representation and Reasoning -- Representing and Reasoning about Context in a Mobile Environment

by Anders Kofod-Petersen, Marius Mikalsen - REVUE D’INTELLIGENCE ARTIFICIELLE , 2005
"... Today the computer is changing from a big, grey, and noisy thing on our desk to a small, portable, and ever-networked item most of us are carrying around. This new found mobility imposes a shift in how we view computers and the way we work with them. When interaction can occur anywhere at any time i ..."
Abstract - Cited by 23 (10 self) - Add to MetaCart
Today the computer is changing from a big, grey, and noisy thing on our desk to a small, portable, and ever-networked item most of us are carrying around. This new found mobility imposes a shift in how we view computers and the way we work with them. When interaction can occur anywhere at any time it is imperative that the system adapts to the user in whatever situation the user is in. To facilitate this adaptivity we propose a two tier architecture. A middleware layer implementing a general mechanism for aggregating and maintaining contextual information. The second part offers automatic situation assessment through Case-Based Reasoning. We demonstrate a multi-agent system for supplying context-sensitive services in a mobile environment.
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Case-based situation assessment in a mobile context-aware system

by Anders Kofod-petersen, Agnar Aamodt - University des Saarlandes , 2003
"... This paper describes how to utilise Case Based Reasoning for identifying and user situations assessment in a contextaware mobile system. The Case Based Reasoning mechanism attempts to identify what situation the user is in, and utilises a Multi Agent System, consisting of information suppliers, to p ..."
Abstract - Cited by 22 (2 self) - Add to MetaCart
This paper describes how to utilise Case Based Reasoning for identifying and user situations assessment in a contextaware mobile system. The Case Based Reasoning mechanism attempts to identify what situation the user is in, and utilises a Multi Agent System, consisting of information suppliers, to provide the user with personalised and contextsensitive information. 1.
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