Hamscher, Walter

COMET: An Application of Model-Based Reasoning to Accounting Systems

AI Magazine

An important problem faced by auditors is gauging how much reliance can be placed on the accounting systems that process millions of transactions to produce the numbers summarized in a company's financial statements. In a complex accounting system, it can be an extremely difficult task for the auditor to anticipate the possible errors that can occur and evaluate the effectiveness of the controls at detecting them. An accurate analysis must take into account the unique features of each company's business processes. An auditor uses COMET to create a hierarchical flowchart model that describes the intended processing of business transactions by an accounting system and the operation of its controls.

Principles of Diagnosis: Current Trends and a Report on the First International Workshop

AI Magazine

Automated diagnosis is an important AI problem not only for its potential practical applications but also because it exposes issues common to all automated reasoning efforts and presents real challenges to existing paradigms. Current research in this area addresses many problems, including managing and structuring probabilistic information, modeling physical systems, reasoning with defeasible assumptions, and interleaving deliberation and action. Furthermore, diagnosis programs must face these problems in contexts where scaling up to deal with cases of realistic size results in daunting combinatorics. This article presents these and other issues as discussed at the First International Workshop on Principles of Diagnosis.

Model-based reasoning: Troubleshooting


That simple observation underlies some of the considerable interest generated in recent years on the topic of model-based reasoning, particularly its application to diagnosis and troubleshooting. This paper surveys the current state of the art, reviewing areas that are well understood and exploring areas that present challenging research topics. It views the fundamental paradigm as the interaction of prediction and observation, and explores it by examining three fundamental subproblems: Generating hypotheses by reasoning from a symptom to a collection of components whose misbehavior may plausibly have caused that symptom; testing each hypothesis to see whether it can account for all available observations of device behavior; then discriminating among the ones that survive testing. Their diversity lies primarily in the varying amounts of kinds of knowledge they bring to bear at each stage of the process; the underlying paradigm is fundamentally the same.