Case-based reasoning (CBR) is becoming a viable real-world technology. First, it fragments each CBR system across many chapters, making it difficult to get the big picture of how the system works and obscuring the interrelatedness of the system's parts. In addition, having each chapter draw its examples from multiple systems adds a certain context-switching overhead: Each time a system is introduced (or reintroduced), the book must set the context anew, and the reader must recall the details of the system. A second drawback to the unified framework is that although it has fairly broad coverage, it is still biased toward those systems that fit it best. As a result, important work sometimes gets only a cursory mention in the book.

Kolodner has long been investigating the use of case-based reasoning for solving a range of complex problems in a variety of domains. Goel is exploring the integration of different types of knowledge and methods of reasoning for planning and design problem solving. In case-based reasoning (Kolodner 1990), a reasoner solves new problems by remembering previous situations similar to the new situation.

INTRODUCTION There is mounting evidence that human experts rely heavily on memory of past cases when solving problems in domains such as law, mathematics, design, and strategic planning. Thus, it seems natural to exploit this idea in constructing Al systems. This is the focus of systems using case-based reasoning; it constitutes a fifth major paradigm of machine learning research. A related approach is that of reasoning by analogy. In case-based reasoning ("CBR"), one uses memory of relevant "past" cases to interpret or to solve a new problem case. Rather than creating a solution from scratch, a reasoner using case-based reasoning recalls cases similar to its current problem situation and solves or interprets a problem by reasoning with past solutions and interpretations. A reasoner using case-based reasoning can derive shortcuts and anticipate problems in new situations that might arise by having previously spotted and dealt with them. This can lead to improvement in the quality and efficiency of the reasoning. Case-based reasoning as a learning paradigm has several advantages. First, there are several performance enhancements it provides for its associated performance element: shortcuts in reasoning, the capability of avoiding past errors; the capability of anticipating and therefore avoiding other previously made mist akes, the capability of focusing in on the most important parts of a problem first. Second, learning can be fairly uncomplicated.

Design-based learning (DBL) has many affordances for promoting deep and lasting learning of both content and complex skills. However, careful orchestration and scaffolding are usually needed to achieve its full potential. In this paper, we describe our efforts at implementing a software suite to meet the cognitive and meta-cognitive needs of learners engaged in DBL. In Study 1, our software suite gave learners the opportunity to design in simulation, to run experiments to learn the effects of variables, and it scaffolded science explanation construction. Through our analysis of study 1 we identified both cognitive and metacognitive needs that the software did not provide for. To meet these additional requirements, we added an interactive science resource and a case library to the software to provide multi-representational content material, to facilitate exploration, and to invite metacognitive reflection needed to do well at learning through design. Learners recognized what they did not understand, took initiative to explore those science concepts, and applied them in novel ways. We present here our analysis of the kinds of metacognitive help learners need to productively learn from design activities and some ways of providing that help. Our conclusion is that cognitive aid without related metacognitive aid is insufficient in a DBL environment.

Sometimes a section will system shells, have made their way anew, and the reader must recall the introduce a list of points to be covered, into the marketplace.

AI research is conducted at a number of academic and research units at the Georgia Institute of Technology. Some of this research is basic in nature, and some has an applied character to it. This article briefly describes basic AI research in the College of Computing at Georgia Tech.

Case-based reasoning provides both a methodology for building systems and a cognitive model of people. It is consistent with much that psychologists have observed in the natural problem solving people do. Psychologists have also observed, however, that people have several problems in doing analogical or case-based reasoning. Although they are good at using analogs to solve new problems, they are not always good at remembering the right ones. However, computers are good at remembering. I present case-based decision aiding as a methodology for building systems in which people and machines work together to solve problems. The case-based decision-aiding system augments the person's memory by providing cases (analogs) for a person to use in solving a problem. The person does the actual decision making using these cases as guidelines. I present an overview of case-based decision aiding, some technical details about how to implement such systems, and several examples of case-based systems.