This paper describes a general mechanism for the qualitative interpretation of simple arithmetic relations. This mechanism is useful for the understanding and reasoning about domains that can be modeled by systems of simple arithmetic equations. Our representation attempts to model the underlying arithmetic in its complete detail. Reasoning from these forms provides the completeness and consistency that cannot be always guaranteed by a pure production-rule based system. We describe an experimental architecture for Equation Reasoning (ER), and illustrate its applicability using examples from the financial domain.
Within the academic and professional auditing communities, there has been growing concern about how to accurately assess the various risks associated with performing an audit. These risks are difficult to conceptualize in terms of numeric estimates. Models of decision making under conditions of risk are well established in decisiontheory literature. In these models, risk and return (payoffs) are specified in terms of numeric estimates, and the goal is to make a decision that maximizes some expected value. In addition, new information can be combined using a decision rule (such as Bayes' rule) for deriving revised estimates of risk.
Within the academic and professional auditing communities, there has been growing concern about how to accurately assess the various risks associated with performing an audit. These risks are difficult to conceptualize in terms of numeric estimates. This article discusses the development of a prototype computational model (computer program) that assesses one of the major audit risks -- inherent risk. This program bases most of its inferencing activities on a qualitative model of a typical business enterprise.
Decision analysis and knowledge-based expert systems share some common goals. Both technologies are designed to improve human decision making; they attempt to do this by formalizing human expert knowledge so that it is amenable to mechanized reasoning. However, the technologies are based on rather different principles. Decision analysis is the application of the principles of decision theory supplemented with insights from the psychology of judgment. Expert systems, at least as we use this term here, involve the application of various logical and computational techniques of AI to the representation of human knowledge for automated inference.
Decision analysis and expert systems are technologies intended to support human reasoning and decision making by formalizing expert knowledge so that it is amenable to mechanized reasoning methods. Despite some common goals, these two paradigms have evolved divergently, with fundamental differences in principle and practice. Recent recognition of the deficiencies of traditional AI techniques for treating uncertainty, coupled with the development of belief nets and influence diagrams, is stimulating renewed enthusiasm among AI researchers in probabilistic reasoning and decision analysis. We present the key ideas of decision analysis and review recent research and applications that aim toward a marriage of these two paradigms. This work combines decision-analytic methods for structuring and encoding uncertain knowledge and preferences with computational techniques from AI for knowledge representation, inference, and explanation. We end by outlining remaining research issues to fully develop the potential of this enterprise.