Audit sampling is selecting a group of items such as invoices for investigation to draw inferences about an account balance. Ratio analysis involves comparisons between two financial statement accounts such as current ratios and gross profit percentage. Reasonable tests involve using financial and nonfinancial data to estimate an account balance. An example would be multiplying items sold by price to determine expected revenue. However, there are audit engagement risks with current auditing techniques.

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The General Motors Variation-Reduction Adviser is a knowledge system built on case-based reasoning principles that is currently in use in a dozen General Motors Assembly Centers. This paper reviews the overall characteristics of the system and then focuses on various AI elements critical to support its deployment to a production system. A key AI enabler is ontology-guided search using domain-specific ontologies. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.

We are going to present an implementation of an AI system, CaBMA, built on top of a commercial project management tool, MS Project. Project management is a business process for successfully delivering one-of-a kind products and services under real-world time and resource constraints. CaBMA (for: Case-Based Project Management Assistant) provides the following functionalities: (1) It captures cases from project plans. CaBMA adds a knowledge layer on top of MS Project to assist the user with his project management tasks. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.

Since 1994 GE Plastics has employed a case-based reasoning tool that determines color formulas which match requested colors. This tool, called FormTool, has saved GE millions of dollars in productivity and material (i.e. The technology developed in FormTool has been used to create an on-line color selection tool for our customers called ColorXpress Select. A customer innovation center has been developed around the FormTool software. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.

Over the last eight years, we have been working on the problem of case-based reasoning (CBR) for medical diagnosis. Through a succession of research projects, we developed a system that used physiologic causes to match findings in cases, evaluated the system on 240 cases, and developed a system that divides cases and memory based on the diagnostic units in the case. Each of these steps has been a significant advance toward diagnostic systems that can effectively learn from experience. Still, it is clear that CBR has not reached its potential to effectively handle the case material and work in concert with a model-based program.

FindMe systems originated in work I did with Kristian Hammond when we were both at the Computer Science Department of the University of Chicago. These systems use case-based reasoning as a way of recommending products in e-commerce catalogs and provide critique-based navigation as a primary user interface. One interesting outcome of this work has been to emphasize the complexity of the common-sense notion of similarity demanded by a user of such catalogs as compared to the metrics used by many CBR systems.

For AI to become truly robust, we must further our understanding of similarity-driven reasoning, analogy, learning, and explanation. Here are some suggested research directions.This article is part of a special issue on the Future of AI.

Case-based reasoning is a problem solving paradigm that in many respects is fundamentally different from other major AI approaches. Instead of relying solely on general knowledge of a problem domain, or making associations along generalized relationships between problem descriptors and conclusions, CBR is able to utilize the specific knowledge of previously experienced, concrete problem situations (cases). A new problem is solved by finding a similar past case, and reusing it in the new problem situation. A second important difference is that CBR also is an approach to incremental, sustained learning, since a new experience is retained each time a problem has been solved, making it immediately available for future problems. The CBR field has grown rapidly over the last few years, as seen by its increased share of papers at major conferences, available commercial tools, and successful applications in daily use.