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.
Probabilistic Knowledge
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 Probabilistic Knowledge write by Sarah Moss. This book was released on 2018. Probabilistic Knowledge available in PDF, EPUB and Kindle. Sarah Moss argues that in addition to full beliefs, credences can constitute knowledge. She introduces the notion of probabilistic content and shows how it plays a central role not only in epistemology, but in the philosophy of mind and language. Just you can believe and assert propositions, you can believe and assert probabilistic contents.
Probabilistic Reasoning in Multiagent Systems
Probabilistic Reasoning in Multiagent 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 Multiagent Systems write by Yang Xiang. This book was released on 2002-08-26. Probabilistic Reasoning in Multiagent Systems available in PDF, EPUB and Kindle. This 2002 book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.
Markov Random Fields and Their Applications
Markov Random Fields and Their Applications - 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 Markov Random Fields and Their Applications write by Ross Kindermann. This book was released on 1980. Markov Random Fields and Their Applications available in PDF, EPUB and Kindle. The study of Markov random fields has brought exciting new problems to probability theory which are being developed in parallel with basic investigation in other disciplines, most notably physics. The mathematical and physical literature is often quite technical. This book aims at a more gentle introduction to these new areas of research.
Reasoning with Incomplete Probabilistic Knowledge Using de Finetti's Fundamental Theorem of Probability
Reasoning with Incomplete Probabilistic Knowledge Using de Finetti's Fundamental Theorem of Probability - 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 Incomplete Probabilistic Knowledge Using de Finetti's Fundamental Theorem of Probability write by Tracy Scott Myers. This book was released on 1997. Reasoning with Incomplete Probabilistic Knowledge Using de Finetti's Fundamental Theorem of Probability available in PDF, EPUB and Kindle.
