From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between

Maintaining knowledge about temporal intervals

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Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.

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Sequential Monte Carlo methods in practice

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

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Perceptual symbol systems.

In 1978, Patrick Hayes promulgated the Naive Physics Manifesto. (It finally appeared as an “official” publication in Hobbs and Moore 1985.) In this paper, he proposed that an allout effort be mounted to formalize commonsense knowledge, using first-order logic as a notation. This effort had its roots in earlier research, especially the work of John McCarthy, but the scope of Hayes’s proposal was new and ambitious. He suggested that the use of Tarskian seniantics could allow us to study a large volume of knowledge-representation problems free from the confines of computer programs. The suggestion inspired a small community of people to actually try to write down all (or most) of commonsense knowledge in predictate calculus. He launched the effort with his own paper on “Liquids” (also in Hobbs and Moore 1985), a fascinating attempt to fix ontology and notation for a realistic domain. Since then several papers in this vein have appeared (Allen 1984; Hobbs 1986; Shoham 1985). I myself have been an enthusiastic advocate of the movement, having written general boosting papers (1978) as well as attempts to actually get on with the work. (1982, 1985). I even coauthored a textbook oriented around Hayes’s idea (Charniak and McDermott 1985). It is therefore with special pain that I produce this report, which draws mostly negative conclusions about progress on Hayes’s project so far, and the progress we can expect. In a nutshell, I will argue that the skimpy progress observed so far is no accident, that in fact it is going to be very difficult to do much better in the future. The reason is that the unspoken premise in Hayes’s arguments, that a lot of reasoning can be analyzed as deductive or approximately deductive, is erroneous. I don’t want what I say in this paper to be taken as a criticism of Pat Hayes, for the simple reason that he is not solely to blame for the position I am criticizing. I will therefore refer to it as the “logicist” position in what follows. It is really the joint work of several people, including John McCarthy, Robert Moore, James Allen, Jerry Hobbs, Patrick Hayes, and me, of whom Hayes is simply the most eloquent.

A critique of pure reason

This book is an introduction on artificial intelligence. Topics include reasoning under uncertainty, robot plans, language understanding, and learning. The history of the field as well as intellectual ties to related disciplines are presented.

Introduction to Artificial Intelligence

In this paper we present DAML-S, a DAML+OIL ontology for describing the properties and capabilities of Web Services. Web Services - Web-accessible programs and devices - are garnering a great deal of interest from industry, and standards are emerging for low-level descriptions of Web Services. DAML-S complements this effort by providing Web Service descriptions at the application layer, describing what a service can do, and not just how it does it. In this paper we describe three aspects of our ontology: the service profile, the process model, and the service grounding. The paper focuses on the grounding, which connects our ontology with low-level XML-based descriptions of Web Services.

Drew McDermott

Papers

A critique of pure reason

Introduction to Artificial Intelligence

DAML-S: Web Service Description for the Semantic Web

Non-monotonic logic I

A temporal logic for reasoning about processes and plans