JUDEA PEARL PROBABILISTIC REASONING IN INTELLIGENT SYSTEMS PDF

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.

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It gets more than 50 pages here. Bayesian networks, ingenious and progressive as they were, have peaked in use, though their children are still cutting edge and invaluable for human and nonhuman reasoning. All that said: Pearl thinks very hard about ultimate matters.

On the surface, there is really no compelling reason that beliefs, being mental dispositions about unrepeatable and often unobservable events, should combine by the laws of proportions that govern repeatable trials such as the outcomes of gambling devices. The primary appeal of probability theory is its ability to express useful qualitative relationships among beliefs and to process these relationships in a way that yields intuitively plausible conclusions… What we wish to stress here is that the fortunate match between human intuition and the laws of proportions is not a coincidence.

It came about because beliefs are formed not in a vacuum but rather as a distillation of sensory experiences We therefore take probability calculus as an initial model of human reasoning from which more refined models may originate, if needed.

By exploring the limits of probability in machine implementations, we hope to identify conditions under which extensions, refinements and simplifications are warranted. Building AI as feedback for formal epistemology! My favourite philosophers are technical like David Lewis; my favourite technical people are philosophical like Pearl. PRIS beats the arse off his own effort , perhaps because at this point he was still working incredibly hard to understand and synthesise competing approaches.

Hard to rate. He sent us down a rabbit hole, chasing nonmonotonic logic solutions to a numerical problem. See also Chomsky vs prob language models. Compare noneuclidean geometries. What other dominant calculi would get similarly competing theories, if we threw a few decades of brilliance at them?

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Probabilistic Reasoning in Intelligent Systems : Networks of Plausible Inference

Add to basket Add to wishlist Description Probabilistic Reasoning in Intelligent Systems is a complete and accessible 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.

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