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 appropriate direction Once constructed, the relationships can easily be reversed into the less intuitive direction in order to perform inference and diagnosis, In this way, knowledge acquisition is made cognitively simple; the machine carries the burden of translating the representation "OK," we replied, "If the tiger were present, what is the probability that you would see that image? On the other hand, if the tiger were not present, what is the probability you would see it?" Before we could say "what is the probability there is a tiger in the first place?" Since then, we have pondered this question. Why is it that we want to look at problems of evidential reasoning backward?

Department of Computer Science Carnegae-Mellon Unaverszty P&burg, PA 15213 Editors' Note: Many expert systems require some means of handling heuristic rules whose conclusions are less than certain Baysian techniques and other numerical scoring methods have been developed to combine and propagate certainty measures as the expert system draws inferences in solving different problems. Doyle's paper argues that it is difficult for a human expert to produce reliable probabilities or numerical scoring factors for an inference rule, and that a radically different approach to the problem should be considered He essentially suggests that the expert be encouraged to think in terms of specific instances which would conflict with the general rule and to encode this knowledge explicitly. Methodologically this seems to be very appealing, and helps to make both explicit and rigorous some of the techniques currently used by knowledge engineers whm they encode and refine the expert's knowledge We would welcome comments and criticisms of this approach from those steeped in the practical issues of constructing large rule-based expert systems. Probabilistic rules and their variants have recently supported several successful applications of expert systems, in spite of the difficulty of committing informants to particular conditional probabilities or "certainty factors," and in spite of the experimentally observed insensitivity of system performance to perturbations of the chosen values Here we survey recent developments concerning reasoned assumptions which offer hope for avoiding the practical elusiveness of probabilistic rules while retaining theoretical power, for basing systems on the information unhesitatingly gained from expert informants, and reconstructing the entailed degrees of belief later @

The system consists of an overall agent architecture and five components within the architecture. The five components are (1) goaldirected learning (GDL), a decisiontheoretic method for selecting learning goals; (2) probabilistic bias evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals; (3) uniquely predictive theories (UPTs) and probability computation using independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories; (4) a probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories; and (5) a decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA's initial learning goal is just An autonomous agent must be able to select biases (Mitchell 1980) for new learning tasks as they arise. PBE uses probabilistic background knowledge and a model of the system's expected learning performance to compute the expected value of learning biases for each learning goal. The resulting expected discounted future accuracy is used as the expected value of the bias.

Today, mobile robotics is an increasingly important bridge between the two areas. It is advancing the theory and practice of cooperative cognition, perception, and action and serving to reunite planning techniques with sensing and real-world performance. Further, developments in mobile robotics can have important practical economic and military consequences. For some time now, amateurs, hobbyists, students, and researchers have had access to how-to books on the low-level mechanical and electronic aspects of mobile-robot construction (Everett 1995; McComb 1987). The famous Massachusetts Institute of Technology (MIT) 6.270 robot-building course has contributed course notes and hardware kits that are now available commercially and in the form of an influential book (Jones 1998; Jones and Flynn 1993).

The First International Workshop on Rough Sets: State of the Art and Perspectives was held on 2-4 September 1992 in Kiekrz, Poland. To stimulate the discussion, the participation was limited to 40 researchers who are involved in fundamental research in rough set theory and its extensions, logic for approximate reasoning, machine learning, knowledge representation and transfer, and applications of rough set methodology. The workshop focused primarily on applications of the basic idea of the approximate definition of a set and its consequences in other areas of science and engineering. Applications discussed at the workshop included machine learning, medical diagnosis, fault detection, medical image processing, neural net training, database organization, drug research, and digital circuit design. The workshop was the first international meeting of researchers working in this relatively new area. The approximate definition of a set in terms of lower and upper bounds, as introduced in the ...

This article is a sequel to an article titled "A New Direction in AI--Toward a Computational Theory of Perceptions," which appeared in the Spring 2001 issue of AI Magazine (volume 22, No. 1, 73-84). The concept of precisiated natural language (PNL) was briefly introduced in that article, and PNL was employed as a basis for computation with perceptions. In what follows, the conceptual structure of PNL is described in greater detail, and PNL's role in knowledge representation, deduction, and concept definition is outlined and illustrated by examples. What should be understood is that PNL is in its initial stages of development and that the exposition that follows is an outline of the basic ideas that underlie PNL rather than a definitive theory. A natural language is basically a system for describing perceptions.

Li is not small compared to that of A. However, To understand how this rule works, let us return to the submarine example and assume that there are two groups of experts El,..., As is pointed out in Zadeh (1979a), the Dempster rule P*(notA) 1. This, in a nutshell, is the basic idea underly-of combination of evidence may lead to counterintuitive coning the Dempster-Shafer theory. The An important observation is in order at this juncture. P(A), that S is in A, the answer would be (after the object under consideration does not exist. P*(A) are the degrees of belief and plausibility associated of evidence, consider the following situation.

This article is an edited transcript of a lecture given at IJCAI-99, Stockholm, Sweden, on 4 August 1999. The article summarizes concepts, principles, and tools that were found useful in applications involving causal modeling. The principles are based on structural-model semantics in which functional (or counterfactual) relationships representing autonomous physical processes are the fundamental building blocks. The article presents the conceptual basis of this semantics, illustrates its application in simple problems, and discusses its ramifications to computational and cognitive problems concerning causation. It is not an easy topic to speak about, but it is a fun topic to speak about.

In fact, we have a knowledge infrastructure already, and it is already immense. AI Mugaztine 7(l): 34- served the most successful work on expert systems: that (today) knowledge comes (mostly) from people. Editor: Mark Stefik Xerox PARC 3333 Coyote Hill Road Palo Alto, California 94304 Workshop on the Foundations of Al: An On-The-Spot Report The NSF and AAAI sponsored Workshop on the Foundations of AI (6-8 February 1986, Las Cruces, New Mexico) is over and, from my perspective at least, it was a very worthwhile event. I am preparing a report that I will send to you in due course. In addition, I noticed that John McCarthy was snapping freely with his camera at the workshop.

Decision theory and nonmonotonic logics are formalisms that can be employed to represent and solve problems of planning under uncertainty. We analyze the usefulness of these two approaches by establishing a simple correspondence between the two formalisms. The analysis indicates that planning using nonmonotonic logic comprises two decision-theoretic concepts: probabilities (degrees of belief in planning hypotheses) and utilities (degrees of preference for planning outcomes). We present and discuss examples of the following lessons from this decision-theoretic view of nonmonotonic reasoning: (1) decision theory and nonmonotonic logics are intended to solve different components of the planning problem; (2) when considered in the context of planning under uncertainty, nonmonotonic logics do not retain the domain-independent characteristics of classical (monotonic) logic; and (3) because certain nonmonotonic programming paradigms (for example, frame-based inheritance, nonmonotonic logics) are inherently problem specific, they might be inappropriate for use in solving certain types of planning problems. We discuss how these conclusions affect several current AI research issues.