This article examines the direction in which knowledge bases are constructed for diagnosis and decision making. When building an expert system, it is traditional to elicit knowledge from an expert in the direction in which the knowledge is to be applied, namely, from observable evidence toward unobservable hypotheses. However, experts usually find it simpler to reason in the opposite direction-from hypotheses to unobservable evidence-because this direction reflects causal relationships. Therefore, we argue that a knowledge base be constructed following the expert's natural reasoning direction, and then reverse the direction for use. This choice of representation direction facilitates knowledge acquisition in deterministic domains and is essential when a problem involves uncertainty. We illustrate this concept with influence diagrams, a methodology for graphically representing a joint probability distribution. Influence diagrams provide a practical means by which an expert can characterize the qualitative and quantitative relationships among evidence and hypotheses in the apporiate direction. Once constructed, the relationships can easily be reserved into the less intuitive direction in order to perform inference inference and diagnosis. In this way, knowledge acquisition is made cognitively simple; the machine carries the burden of translating the representation.

The questions of how humans process uncertain information is important to the development of knowledge-based systems in term of both knowledge acquisition and knowledge representation. This article reviews three bodies of psychological research that address this question: human perception, human probabilistic and statistical judgement, and human choice behavior. The general conclusion is that human behavior under certainty is often suboptimal and sometimes even fallacious. Suggestions for knowledge engineers in detecting and obviating such errors are discussed. The requirements for a system designed to reduce the effects of human factors in the processing of uncertain knowledge are introduced.

The Oak Ridge National Laboratory (ORNL) Center for Engineering Systems Advanced Research (CESAR) is a national center for multidisciplinary long-range research and development (R&D) in machine intelligence and advanced control theory. Intelligent machines (including sensor-based robots) can be viewed as artificially created operational systems capable of autonomous decision making and action. One goal of the research is autonomous remote operations in hazardous environments. This review describes highlights of CESAR research through 1986 and alludes to future plans.

This report contains the reading list for the Qualifying Examination in Artificial Intelligence. Areas covered include search, representation, reasoning, planning and problem solving, learning, expert systems, vision, robotics, natural language, perspectives and AI programming. An extensive bibliography is also provided.

Chapman and Hall. See also: Influence diagrams for Bayesian decision analysis, European Journal of Operational Research, Volume 40, Issue 3, 15 June 1989, Pages 363–376 (http://www.sciencedirect.com/science/article/pii/0377221789904293). Bayesian Decision Analysis: Principles and Practice, Cambridge University Press, 2010 (https://books.google.com/books/about/Bayesian_Decision_Analysis.html?id=O1lXnQAACAAJ).

During the past two years, the Dempster-Shafer theory of evidence has attracted considerable attention within the AI community as a promising method of dealing with uncertainty in expert systems. As presented in the literature, the theory is hard to master. In a simple approach that is outlined in this paper, the Dempster-Shafer theory is viewed in the context of relational databases as the application of familiar retrieval techniques to second-order relations in first normal form. The relational viewpoint clarifies some of the controversial issues in the Dempster-Shafer theory and facilities its use in AI-oriented applications.

During the past two years, the Dempster-Shafer theory of evidence has attracted considerable attention within the AI community as a promising method of dealing with uncertainty in expert systems. As presented in the literature, the theory is hard to master. In a simple approach that is outlined in this paper, the Dempster-Shafer theory is viewed in the context of relational databases as the application of familiar retrieval techniques to second-order relations in first normal form. The relational viewpoint clarifies some of the controversial issues in the Dempster-Shafer theory and facilities its use in AI-oriented applications.

In fact, such a pattern can itself be considered a frame, where the position of each pixel is a slot, and the shade or A recent article by Ronald Brachman (Brachman, color at each pixel is then the attached value. It should 1985) points out some philosophical or semantic problems then be possible to represent this pattern as I have just in using the notion of a prototype, which is described by described it-z.e., by a frame representing the background, using default properties. The problem arises since default partially obscured or covered by a frame representing the properties can be overridden or cancelled in representing object of interest, partially obscured or covered by some particular instances, and therefore lack definitional power: other objects. The fact that some part of the object of interest is obscured does not mean that it is no longer there, nor As an example, Brachman presents an elephant joke: that it is not intrinsic to the object's definition. Q: What's big and gray, has a trunk, and lives in the trees?

Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to represent the generic knowledge of a domain expert, and it turns into a computational architecture if the links are used not merely for storing factual knowledge but also for directing and activating the data flow in the computations which manipulate this knowledge. The first part of the paper deals with the task of fusing and propagating the impacts of new information through the networks in such a way that, when equilibrium is reached, each proposition will be assigned a measure of belief consistent with the axioms of probability theory. It is shown that if the network is singly connected (e.g. The second part of the paper deals with the problem of finding a tree-structured representation for a collection of probabilistically coupled propositions using auxiliary (dummy) variables, colloquially called “hidden causes.”

We present a logical relationship between a small number of intuitive properties for measures of belief and the axioms of probability theory. The relationship was first demonstrated several decades ago but has remained obscure. We introduce the proof and discuss its relevance to research on reasoning under uncertainty in artificial intelligence. In particular, we demonstrate that the logical relationship can facilitate the identification of differences among alternative plausible reasoning methodologies. Finally, we make use of the relationship to examine popular non-probabilistic strategies.