Representing and Reasoning with Probabilistic Knowledge - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Representing and Reasoning with Probabilistic Knowledge write by Fahiem Bacchus. This book was released on 1990. Representing and Reasoning with Probabilistic Knowledge available in PDF, EPUB and Kindle. Probabilistic information has many uses in an intelligent system. This book explores logical formalisms for representing and reasoning with probabilistic information that will be of particular value to researchers in nonmonotonic reasoning, applications of probabilities, and knowledge representation. It demonstrates that probabilities are not limited to particular applications, like expert systems; they have an important role to play in the formal design and specification of intelligent systems in general. Fahiem Bacchus focuses on two distinct notions of probabilities: one propositional, involving degrees of belief, the other proportional, involving statistics. He constructs distinct logics with different semantics for each type of probability that are a significant advance in the formal tools available for representing and reasoning with probabilities. These logics can represent an extensive variety of qualitative assertions, eliminating requirements for exact point-valued probabilities, and they can represent firstshy;order logical information. The logics also have proof theories which give a formal specification for a class of reasoning that subsumes and integrates most of the probabilistic reasoning schemes so far developed in AI. Using the new logical tools to connect statistical with propositional probability, Bacchus also proposes a system of direct inference in which degrees of belief can be inferred from statistical knowledge and demonstrates how this mechanism can be applied to yield a powerful and intuitively satisfying system of defeasible or default reasoning. Fahiem Bacchus is Assistant Professor of Computer Science at the University of Waterloo, Ontario. Contents: Introduction. Propositional Probabilities. Statistical Probabilities. Combining Statistical and Propositional Probabilities Default Inferences from Statistical Knowledge.
Representing and Reasoning with Probabilistic Knowledge
Representing and Reasoning with Probabilistic Knowledge - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Representing and Reasoning with Probabilistic Knowledge write by Fahiem Bacchus. This book was released on 1988. Representing and Reasoning with Probabilistic Knowledge available in PDF, EPUB and Kindle.
Knowledge Representation and Reasoning
Knowledge Representation and Reasoning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Knowledge Representation and Reasoning write by Ronald Brachman. This book was released on 2004-05-19. Knowledge Representation and Reasoning available in PDF, EPUB and Kindle. Knowledge representation is at the very core of a radical idea for understanding intelligence. This book talks about the central concepts of knowledge representation developed over the years. It is suitable for researchers and practitioners in database management, information retrieval, object-oriented systems and artificial intelligence.
Probabilistic Reasoning in Intelligent Systems
Probabilistic Reasoning in Intelligent Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Probabilistic Reasoning in Intelligent Systems write by Judea Pearl. This book was released on 2014-06-28. Probabilistic Reasoning in Intelligent Systems available in PDF, EPUB and Kindle. 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. 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.
Reasoning with Models of Probabilistic Knowledge Over Probabilistic Knowledge
Reasoning with Models of Probabilistic Knowledge Over Probabilistic Knowledge - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Reasoning with Models of Probabilistic Knowledge Over Probabilistic Knowledge write by Afsaneh H. Shirazi. This book was released on 2011. Reasoning with Models of Probabilistic Knowledge Over Probabilistic Knowledge available in PDF, EPUB and Kindle. In multi-agent systems, the knowledge of agents about other agents0́9 knowledge often plays a pivotal role in their decisions. In many applications, this knowledge involves uncertainty. This uncertainty may be about the state of the world or about the other agents0́9 knowledge. In this thesis, we answer the question of how to model this probabilistic knowledge and reason about it efficiently. Modal logics enable representation of knowledge and belief by explicit reference to classical logical formulas in addition to references to those formulas0́9 truth values. Traditional modal logics (see e.g. [Fitting, 1993; Blackburn et al., 2007]) cannot easily represent scenarios involving degrees of belief. Works that combine modal logics and probabilities apply the representation power of modal operators for representing beliefs over beliefs, and the representation power of probability for modeling graded beliefs. Most tractable approaches apply a single model that is either engineered or learned, and reasoning is done within that model. Present model-based approaches of this kind are limited in that either their semantics is restricted to have all agents with a common prior on world states, or are resolving to reasoning algorithms that do not scale to large models. In this thesis we provide the first sampling-based algorithms for model-based reasoning in such combinations of modal logics and probability. We examine a different point than examined before in the expressivity-tractability tradeoff for that combination, and examine both general models and also models which use Bayesian Networks to represent subjective probabilistic beliefs of agents. We provide exact inference algorithms for the two representations, together with correctness results, and show that they are faster than comparable previous ones when some structural conditions hold. We also present sampling-based algorithms, show that those converge under relaxed conditions and that they may not converge otherwise, demonstrate the methods on some examples, and examine the performance of our algorithms experimentally.
