The age of a case (in the CBR sense) is the amount of time that has elapsed between the time that the case originally occurred and the time of the current reasoning activity. People engaged in plausible reasoning tasks will, under appropriate circumstances, use the age of retrieved prior cases to filter and discard them, or to select among alternatives by their recency. This paper examines how the age of a case (and its spatial analog) are used by people in plausible and case-based reasoning tasks. I will argue that (1) The age of a retrieved case is an important factor in case relevance judgments for certain kinds of inferences. (2) When case age is relevant, more recent cases are usually, but not always, preferred to older ones (the all other things being equal" caveat). Finally, I will argue that, somewhat surprisingly, (3) case age cannot be used as an index into memory given some commonly held assumptions about the nature of the retrieval process because it varies with the time of retrieval. This limits its use to post-retrieval processes, such as the filtering of already retrieved cases.

Descriptions of scientific and technical systems take a number of different forms. Depending upon thepurpose of a description, it may focus on a system's behavior, causaliiy, physical or functional topology,or structural composition. An analogical explanation used to teach someone about such a system is alsotypically geared to one or another of these purposes. In this paper we describe some research leading tothe development of a theory of the role of explanatory model types in the generation of analogicalmappings. The work is motivated by the larger question of how explanations presented as analogies areapplied by students learning about new domains. Our long term goals are (1) the development of atheory of purpose-guided analogical learning, based on a coherent taxonomy of mental model types, and(2) the development of a theory of the integration of partial mental models during learning, usingprinciples for relating different explanatory model types.

When one looks at transcripts of people answering questions or carrying on dialogues about everyday matters, their comments are filled with plausible inferences -- inferences that are not certain, but that make sense. Often, in forming these inferences, generalizations are made that are equally uncertain, but are nevertheless useful as a guide to their reasoning. This paper describes some extensions to our earlier description of a core theory of plausible reasoning (Collins and Michalski, 1989), based in large part on a recent protocol study. The primary focus is on the inductive inference patterns people use to form plausible generalizations, weakly held beliefs based on few examples. W e also show how the model was extended to deal with plausible inferences involving continuous quantities and inequalities.