Many and long were the conversations between Lord Byron and Shelley to which I was a devout and silent listener. During one of these, various philosophical doctrines were discussed, and among others the nature of the principle of life, and whether there was any probability of its ever being discovered and communicated. They talked of the experiments of Dr. Darwin (I speak not of what the doctor really did or said that he did, but, as more to my purpose, of what was then spoken of as having been done by him), who preserved a piece of vermicelli in a glass case till by some extraordinary means it began to move with a voluntary motion. Not thus, after all, would life be given. Perhaps a corpse would be reanimated; galvanism had given token of such things: perhaps the component parts of a creature might be manufactured, brought together, and endued with vital warmth (Butler 1998).

This page contains some information on research by David Poole and students on probabilistic reasoning and decision making. It is not intended to be an introduction to the vast literature on these topics, but only the incremental work done by me. For more different perspectives, see the pointers from the Uncertainty in AI (UAI) home page. Maybe someday I will write an online introduction. Probabilistic Horn abduction is a pragmatic combination of logic and probability.

You may skip most of these readings if you have prior programming experience in Java.

The digital computing world is built on a structure of Boolean logic applied to binary values -- one or zero, yes or no, in or out. But this powerful structure is a gross oversimplification of the real world, where many shades of gray exist between black and white. In everyday life, we use quasimetric notions that are clearly related to numerical concepts or values but lack precision or demarcation. If I'm a server time-stamping thousands of files, digital certificates or transactions, I need very fine distinctions. But if I'm asking a co-worker what time it is, do I really care that it's 11:49:54 a.m.

Presented at Land-Information Systems: Developments for planning the sustainable use of land resources, Hanover 20-23 Nov. 1996 Proceedings to be published by European Commission. A paper presented at the Management Science/Operations Research Working Group Session at the SAF National Convention, Washington, D.C. Bare, B. and Mendoza, G. 1992. "Ecosystem analysis using fuzzy set theory." "Modelling management of agricultural ecosystems using fuzzy set theory: methodological issues." Paper presented at the joint meetings of the Western Agricultural Economics Association and the Canadian Agricultural Economics and Farm Management Society, 1993, Edmonton, Alberta. A rational method for assessing irrigation performance at farm level with the aid of fuzzy set theory.

The Association for Uncertainty in Artificial Intelligence is a non-profit organization focused on organizing the annual Conference on Uncertainty in Artificial Intelligence (UAI) and, more generally, on promoting research in pursuit of advances in knowledge representation, learning and reasoning under uncertainty. The next UAI conference is the 33rd conference, UAI-2017 in Sydney, Australia. Join our Facebook group or add yourself to the UAI Mailing list to keep updated on announcements and relevant AI news. Principles and applications developed within the UAI community have been at the forefront of research in Artificial Intelligence. The UAI community and annual meeting have been primary sources of advances in graphical models for representing and reasoning with uncertainty.

Intelligent control and decision methods developed at the AI Center are based upon solid foundations provided by semantic logical models of the approximate-reasoning problem. These models are based on the notion of possible world. Informally, possible worlds correspond to the potential solutions of a system-analysis problem.

Gister supports the rapid development of evidential reasoning systems through an interactive, menu-driven, graphical interface, based upon Grasper-CL. The user interacts with the system in much the same way as with electronic spreadsheets, by simply selecting from menus to add evidential operations to an analysis, to modify data or operation parameters, or to change any portion of the uncertain knowledge base. In response, gister updates its analyses to reflect the new information. Gister supports a wide range of evidential operations, including fusion, source discounting, time projection, summarization, evidence interpretation, and sensitivity analysis. Gister has been applied to a wide range of problems, including multisensor interpretation, mission planning, medical diagnosis, intelligence analysis, underwater vehicle tracking, antiair threat identification, robot vehicle navigation, and management decision support.

Well, the lecture looks good, however, I have not understood the concepts of these neural networks and fuzzy logic on data analysis area. If we use the same data to model itself, then the question is what we are actually modeling - these simply turn out to be nothing but simply some form of advanced curve fitting tools. The most important feature would be if these tools can give us satisfactory modeling of some variables such as heat transfer coeffecient that cannot be measured easily from some other easily measurable variables, then only it makes sense. If it is the case of estimation and cross validation where the estimation depends on measured quantities, then modeling can be easily achieved such as using some dimensionless physical numbers and why we need the fuzzy logic or neural network in such cases, it is not clear yet.

Stuart Russell is a leading researcher in the field of artificial intelligence. He is a Professor of Computer Science at the University of California at Berkeley, Associate Editor of the Journal of the ACM, and author of "Artificial Intelligence: A Modern Approach" (Prentice Hall, 1995, 2003), the leading textbook in the field. His research interests include machine learning, limited rationality, real-time decision-making, intelligent agent architectures, autonomous vehicles, search, game-playing, reasoning under uncertainty, and commonsense knowledge representation. UBIQUITY: The original grand vision of artificial intelligence (AI) in the 1950s and '60s seemed to dissipate into many small, disparate projects. Should this fragmentation be written off as an inevitable Humpty-Dumpty problem or is it possible to bring the fragments back together into a single field? RUSSELL: I think we can put it back together in the sense of being able to join the pieces.