Many concepts and situations are best explained by sketching. This paper describes our work on sKEA, the sketching Knowledge Entry Associate, a system designed for knowledge capture via sketching. We discuss the key ideas of sKEA: blob semantics for glyphs to sidestep recognition for visual symbols, qualitative spatial reasoning to provide richer visual and conceptual understanding of what is being communicated, arrows to express domain relationships, layers to express within-sketch segmentation (including a meta-layer to express subsketch relationships themselves via sketching), and analogical comparison to explore similarities and differences between sketched concepts. Experiences with sKEA to date and future plans are also discussed.

This paper describes an analogy ontology, a formal representation of some key ideas in analogical processing, that supports the integration of analogical processing with first-principles reasoners. The ontology is based on Gentner's structure-mapping theory, a psychological account of analogy and similarity. The semantics of the ontology are enforced via procedural attachment, using cognitive simulations of structure-mapping to provide analogical processing services. Queries that include analogical operations can be formulated in the same way as standard logical inference, and analogical processing systems in turn can call on the services of first-principles reasoners for creating cases and validating their conjectures.

Understanding common sense reasoning about the physical world is one of the goals of qualitative reasoning research. This paper describes how we combine qualitative mechanics and analogy to solve problems posed as sketches. The problems are drawn from the Bennett Mechanical Comprehension Test, which is used to evaluate technician candidates. We discuss sketch annotations, which define conceptual quantities in terms of visual measurements, how modeling decisions are made by analogy, and how analogy can be used to frame comparative analysis problems. Experimental results are presented indicating that this approach has promise. 1

The US Army Battle Command Battle Lab conducted an experiment with the ICCES system -- an integrated decision aid for performing several critical steps of a US Army Brigade Military Decision Making Process: from capturing a high-level Course of Action to producing a detailed analysis and plan of tasks. The system integrated several available technologies based largely on AI techniques, ranging from qualitative spatial interpretation of course-ofaction diagrams to interleaved adversarial planning and scheduling. The experiment dispelled concerns about potential negative impacts of such tools on the creative aspects of the art of war, showed a potential for dramatic time savings in the MDMP process, and confirmed the maturity and suitability of the technologies for near-future deployment.

Advocates of dynamic systems have suggested that higher mental processes are based on continuous representations. In order to evaluate this claim, we first define the concept of representation, and rigorously distinguish between discrete representations and continuous representations. We also explore two important bases of representational content. Then, we present seven arguments that discrete representations are necessary for any system that must discriminate between two or more states. It follows that higher mental processes require discrete representations. We also argue that discrete representations are more influenced by conceptual role than continuous representations. We end by

Understanding how analogy is used in problem solving is an important problem in cognitive science. This paper describes a model of using worked solutions to solve new problems, in terms of structure-mapping processes in the Companions cognitive architecture. The Educational Testing Service independently evaluated the flexibility of the system by using AP Physics problems that were systematically varied to test different types of transfer. We also show that the model provides an explanation for many of the analogy events in VanLehn’s (1998) analysis of the use of analogy by students

We describe the use of analogies in 75 one-on-one tutoring sessions with first-year medical students carried on by two professors of physiology at Rush Medical College. Analogies were not very frequent, but were extremely effective when they were used. We have analyzed the goals, the topics, and the discourse strategies for proposing analogies. We have also studied the ways that the tutors follow up on these analogies and clarify them when necessary, with the goal of implementing analogies in our intelligent tutoring system, CIRCSIM-Tutor. Our knowledge representation scheme is based on Gentner’s theory and Forbus ’ MAC/FAC model, which allows for structural mappings between analogies.

An important class of questions for knowledge based systems concern comparisons, such as “How is X like Y?” and “How are X and Y different? ” This paper describes how we have used a cognitive simulation of analogical processing to answer such questions, to support domain experts in entering new knowledge. We outline techniques for case construction and summarization of comparison results that have been developed and refined based on an independent formative evaluation. In addition to these techniques, we discuss the role of the comparison system in SHAKEN, the larger system in which they are embedded, and our plans for further improvements.

Our project is aimed at integrating and extending inferential, analogical, and intelligent IR technologies to create power tools for intelligence analysts. Our goal is to discover interesting and powerful functional integrations that permit these technologies to exploit each others ’ strengths to mitigate their weaknesses. From the perspective of knowledge-based AI technology, a key goal of the project is to extend the reach of such systems into the world of unstructured data and text. From the perspective of IR technology, it is to leverage the application of inferential and analogical techniques to structured representations in order to achieve significant new functionality. Background Intelligence analysts must sift through massive amounts of data, using perspective gained from history and experience to pull together from disparate sources the best coherent picture of what is happening. Intelligent information technology has the potential to create new software tools that could aid analysts in a number of critical and mutually reinforcing ways: Analysts make heavy use of precedents and analogies. This sometimes leads to vital “trans-logical ” leaps. Because of fundamental human cognitive limitations, it also sometimes leads to false analogies, where the matches are too superficial, and to missed opportunities, where the matches are too obscure for unaided human reasoning to uncover. A suite of knowledge-based software power tools could help analysts recognize deeper or less obvious analogies, could help them apply these analogies to the current situation, and could help them reject superficially plausible but useless analogies and precedents more rapidly. Analysts make heavy use of scenario generation, both to interpret reported data and to project plausible future events. Again because of fundamental human cognitive limitations, the first one or two plausible interpretations (or

A corpus consisting of eighty-one one-on-one tutoring sessions with first-year medical students carried on by two professors of physiology at Rush Medical College was analyzed for the use of analogies to facilitate understanding of the topics covered. Analogies were infrequently used, but had a positive effect on improving student comprehension of the topics tutored. The human tutor’s goals, topics, discourse strategies, follow-up, and clarification in the presence of misunderstanding were analyzed with the long term goal of implementing analogies in an intelligent tutoring system.