Agnar Aamodt

Case-based reasoning: foundational issues, methodological variations, and system approaches

Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

Retrieval, reuse, revision and retention in case-based reasoning

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

Different roles and mutual dependencies of data, information, and knowledge—an AI perspective on their integration

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 learning from experience. An outline of a case-based system architecture is presented, and used to show that a focus on the retaining and reuse of past cases facilitates a gradual and evolutionary transition from an information system to a knowledge-based system.

Explanation in Case-Based Reasoning---Perspectives and Goals

We present an overview of different theories of explanation from the philosophy and cognitive science communities. Based on these theories, as well as models of explanation from the knowledge-based systems area, we present a framework for explanation in case-based reasoning (CBR) based on explanation goals. We propose ways that the goals of the user and system designer should be taken into account when deciding what is a good explanation for a given CBR system. Some general types of goals relevant to many CBR systems are identified, and used to survey existing methods of explanation in CBR. Finally, we identify some future challenges.

Knowledge-Intensive Case-Based Reasoning in CREEK

Knowledge-intensive CBR assumes that cases are enriched with general domain knowledge. In CREEK, there is a very strong coupling between cases and general domain knowledge, in that cases are embedded within a general domain model. This increases the knowledge-intensiveness of the cases themselves. A knowledge-intensive CBR method calls for powerful knowledge acquisition and modeling techniques, as well as machine learning methods that take advantage of the general knowledge represented in the system. The focusing theme of the paper is on cases as knowledge within a knowledge-intensive CBR method. This is made concrete by relating it to the CREEK architecture and system, both in general terms, and through a set of example projects where various aspects of this theme have been studied.

Explanation-Driven Case-Based Reasoning

Problem solving in weak theory domains should compensate for the lack of strong theories by combining the various other knowledge types involved. Such methods should be able to effectively combine general domain knowledge with specific case knowledge. A method is described that utilises a presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning. A generic reasoning method — captured in what is called the Activate-explain-focus cycle — is able to utilise a rich knowledge model in producing context-dependent explanations. A specialisation of this method for each of the main subprocesses of case-based reasoning is presented, and illustrated with examples.

A Two Layer Case-Based Reasoning Architecture for Medical Image Understanding

The paper describes a novel architecture for image understanding. It is based on acquisition of radiologist knowledge, and combines low-level structure analysis with high-level interpretation of image content, within a task-oriented model. A case based reasoner working on a segment case-base contains the individual image segments. These cases with labels are considered indexes for another case based reasoner working on an organ interpretation case base. Both are Creek type case based reasoners, here operating within a propose-critique-modify task structure. Methods for criticizing suggested interpretations by way of explanation, and how interpretations may be modified, are presented. An example run illustrates the system architecture and its key concepts.

A context model for knowledge-intensive case-based reasoning

Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real-world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient or too brittle to handle new problems. In our research, we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem-solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem-solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained and an example shows its integration into an existing knowledge-intensive CBR system.

Representing Temporal Knowledge for Case-Based Prediction

Cases are descriptions of situations limited in time and space. The research reported here introduces a method for representation and reasoning with time-dependent situations, or temporal cases, within a knowledge-intensive CBR framework. Most current CBR methods deal with snapshot cases, descriptions of a world state at a single time stamp. In many timedependent situations, value sets at particular time points are less important than the value changes over some interval of time. Our focus is on prediction problems for avoiding faulty situations. Based on a well-established theory of temporal intervals, we have developed a method for representing temporal cases inside the knowledge-intensive CBR system Creek. The paper presents the theoretical foundation of the method, the representation formalism and basic reasoning algorithms, and an example applied to the prediction of unwanted events in oil well drilling.

Contextualised ambient intelligence through case-based reasoning

Ambient Intelligence is a research area that has gained a lot of attention in recent years. One of the most important issues for ambient intelligent systems is to perceive the environment and assess occurring situations, thus allowing systems to behave intelligently. As the ambient intelligence area has been largely technology driven, the abilities of systems to understand their surroundings have largely been ignored. This work demonstrates the first steps towards an ambient intelligent system, which is able to appreciate the environment and reason about occurring situations. This situation awareness is achieved through knowledge intensive case-based reasoning.