Creative conceptual change involves (a) the construction of new concepts and of coherent belief systems, or theories, relating these concepts, and (b) the modification and extrapolation of existing concepts and theories in novel situations. I discuss these and other types of conceptual change, and present computational models of constructive and extrapolative processes in creative conceptual change. The models have been implemented as computer programs in two very different task domains, autonomous robotic navigation and fictional story understanding.

mbinatorial explosion of inferences has always been one of the classic problems in AI. Resources are limited, and inferences potentially infinite; a reasoner needs to be able to determine which inferences are useful to draw from a given piece of text. But unless one considers the goals of the reasoner, it is very difficult to give a principled definition of what it means for an inference to be "useful." This paper presents a theory of inference control based on the notion of interestingness. W e introduce knowledge goals, the goals of a reasoner to acquire some piece of knowledge required for a reasoning task, as the focussing criteria for inference control. W e argue that knowledge goals correspond to the interests of the reasoner, and present a theory of interestingness that is functionally motivated by consideration of the needs of the reasoner. Although we use story understanding as the reasoning task, many of the arguments carry over to other cognitive tasks as well.

This paper presents a theory of motivational analysis, the construction of volitional explanations to describe the planning behavior of agents. We discuss both the content of such explanations, as well as the process by which an understander builds the explanations. Explanations are constructed from decision models, which describe the planning process that an agent goes through when considering whether to perform an action. Decision models are represented as explanation patterns, which are standard patterns of causality based on previous experiences of the understander. We discuss the nature of explanation patterns, their use in representing decision models, and the process by which they are retrieved, used and evaluated.

Abduction is often viewed as inference to the "best" explanation. However, the evaluation of the goodness of candidate hypotheses remains an open problem. Most artificial intelligence research addressing this problem has concentrated on syntactic criteria, applied uniformly regardless of the explainer's intended use for the explanation. We demonstrate that syntactic approaches are insufficient to capture important differences in explanations, and propose instead that choice of the "best" explanation should be based on explanations' utility for the explainer's purpose. We describe two classes of goals motivating explanation: knowledge goals reflecting internal desires for information, and goals to accomplish tasks in the external world. We describe how these goals impose requirements on explanations, and discuss how we apply those requirements to evaluate hypotheses in two computer story understanding systems.

Successful creative understanding requires that a reasoner be able to manipulate known concepts in order to understand novel ones. A major problem arises, however, when one considers exactly how these manipulations are to be bounded. If a bound is imposed which is too loose, the reasoner is likely to create bizarre understandings rather than useful creative ones. On the other hand, if the bound is too tight, the reasoner will not have the flexibility needed to deal with a wide range of creative understanding experiences. Our approach is to make use of a principled ontology as one source of reasonable bounding. This allows our creative understanding theory to have good explanatory power about the process while allowing the computer implementation of the theory (the ISAAC system) to be flexible without being bizarre in the task domain of reading science fiction short stories.

Reading has been studied for decades by a variety of cognitive disciplines, yet no theories exist which sufficiently describe and explain how people accomplish the complete task of reading real-world texts. In particular, a type of knowledge intensive reading known as creative reading has been largely ignored by the past research. W e argue that creative reading is an aspect of practically all reading experiences; as a result, any theory which overlooks this will be insufficient. W e have built on results from psychology, artificial intelligence, and education in order to produce a functional theory of the complete reading process. The overall framework describes the set of tasks necessary for reading to be performed. Within this framework, we have developed a theory of creative reading. The theory is implemented in the ISAAC (Integrated Story Analysis And Creativity) system, a reading system which reads science fiction stories.

A reasoner may fail at a cognitive task, not because it does not have appropriate knowledge with which to reason, but instead because it does not have the proper index or cue with which to retrieve such knowledge from memory. The reasoner knows this memory item; it simply cannot remember the item. This paper argues that forgetting provides an opportunity for learning through memory reorganization. A reasoner that takes full advantage of such opportunities, however, must be able to reason about its own memory system. To do so, it must possess a language for declaratively representing its reasoning failures and must reflectively inspect such representations if it is to fully explain the reason for its failure. Once such an error is understood as a memory failure, the problem of forgetting is to re-adjust the indexes so that the knowledge is properly retrieved in similar, future situations.

This paper identifies goal handling processes that begin to account for the kind of processes involved in invention. We identify new goal properties and mechanisms for processing goals, as well as means of integrating opportunism, deliberation, and social interaction into goal/plan processes. We focus on enterprise goals, which extend traditional design goals and knowledge goals to address significant enterprises associated with an inventor. Enterprise goals represent "seed" goals of an expert, around which the whole knowledge of an expert gets reorganized and grows more or less opportunistically. Enterprise goals reflect the idiosyncrasy of thematic goals among experts. They constantly increase the sensitivity of individuals for particular events that might contribute to their satisfaction. Our exploration is based on a well-documented example: the invention of the telephone by Alexander Graham Bell. We propose mechanisms to explain: (1) how Bell's early thematic goals gave rise to the new goals to invent the multiple telegraph and the telephone, and (2) how the new goals interacted opportunistically. Finally we describe our implemented computational model, ALEC (Analogical Learning by Explaining Creatively), that accounts for the role of enterprise goals in invention.