5 edition of Representing and reasoning with probabilistic knowledge found in the catalog.
Includes bibliographical references (p. -227) and index.
|Series||Artificial intelligence, Artificial intelligence (Cambridge, Mass.)|
|LC Classifications||QA273 .B24 1990|
|The Physical Object|
|Pagination||233 p. :|
|Number of Pages||233|
|LC Control Number||90013555|
Probabilistic Models for the Semantic Web: A Survey: /ch Recently, there has been an increasing interest in formalisms for representing uncertain information on the Semantic Web. This interest is triggered by theCited by: A probabilistic graphical model is a tool to represent beliefs and uncertain knowledge about facts and events using probabilities. It is also one of the most advanced machine learning techniques nowadays and has many industrial success stories.
For first-order probabilistic knowledge representation, grounding is an important means to define a semantics for knowledge bases which extends the propositional semantics. Abstract. Most techniques for probabilistic reasoning focus on reasoning about conditional probability constraints. However, human experts are accustomed to representing uncertain knowledge in the form of expectation rather than probability distribution directly in many : Kedian Mu, Zuoquan Lin, Zhi Jin, Ruqian Lu.
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Representing and Reasoning with Probabilistic Knowledge: A [Bacchus, Fahiem] on *FREE* shipping on qualifying offers. Representing and Reasoning with Probabilistic Knowledge: A.
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.
Representing and reasoning with probabilistic knowledge. Abstract. This thesis presents a logical formalism for representing and reasoning with probabilistic knowledge. The formalism differs from previous efforts in this area in a number of ways.
Most previous work has investigated ways of assigning probabilities to the sentences of a. 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 : PAGODA (Probabilistic Autonomous GOal-Directed Agent) is a model for autonomous learning in probabilistic domains [desJardins, ] that incorporates innovative techniques for using the agent's existing knowledge to guide and constrain the learning process and for representing, reasoning with, and learning probabilistic by: 1.
Open Library is an open, editable library catalog, building towards a web page for every book ever published. Representing and reasoning with probabilistic knowledge by Fahiem Bacchus,MIT Press edition, in English/5(6). Representing and reasoning with probabilistic knowledge: a logical approach to probabilities.
[Fahiem Bacchus] This book explores logical formalisms for representing and reasoning with probabilistic Representing and reasoning with probabilistic knowledge book that will be of particular value to researchers in nonmonotonic reasoning.
Book Selection; Published: 01 September ; Representing and Reasoning with Probabilistic Knowledge. Evans Journal of the Operational Research Society vol page ()Cite this articleCited by: Request PDF | Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach | PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for autonomous learning in Author: Marie Desjardins.
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities January three-layered organization for representing knowledge and reasoning with ﬁrst-order logic and probabilities in open worlds [Hanheide et al., ]. Some general formula-tions that combine logical and probabilistic reasoning include Markov logic network [Richardson and Domingos, ], Bayesian logic [Milch et al., ], and probabilistic exten.
This book examines a range of topics that push logic and probability into wider, more interesting areas. After a brief introduction, Halpern introduces upper and lower probabilities representing partial knowledge, and other measures representing belief, plausibility, possibility, and by: Reasoning about Knowledge and Probability In many of the application areas for reasoning about knowledge, it is important to be able to reason about the probability of certain events as well as the knowledge of agents.
In particular, this arises in distributed systems applications when we want to analyze randomized or probabilistic Size: 2MB. Andreas Falkner, Herwig Schreiner, in Knowledge-Based Configuration, Other Issues.
The preceding paragraphs concentrate on knowledge representation and reasoning issues of the core configuration task. Of course, the configurator application as a whole has to deal with much more: Sales and pricing topics play a role in the bidding phase, although not as prominently as in consumer.
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. Uncertainty is a fundamental and unavoidable feature of daily life; in order to deal with uncertaintly intelligently, we need to be able to represent it and reason about it.
In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis.
Purchase Knowledge Representation and Reasoning - 1st Edition. Print Book & E-Book. ISBNAll the concepts, such as answering queries, planning, diagnostics, and probabilistic reasoning, are illustrated by programs of ASP.
The text can be used for AI-related undergraduate and graduate classes and by researchers who would like to learn more about ASP and knowledge by: • Associated methods of automated reasoning • The three systems that we saw – use symbolic knowledge representation and reasoning – But, they also use non-symbolic methods • Non-symbolic methods are covered in other courses (CS, CS, ) • This course would be better labeled as a course on Symbolic Representation and Reasoning.
•Probabilistic logic (Chapter 6): here we are back to merging logical and probabilistic reasoning (as in the early days of AI), but now in a single powerful and mathematically sound framework. This chapter reﬂects recent advances in knowledge representation and reasoning in AI.
Traditional philosophical discussions of knowledge have focused on the epistemic status of full beliefs. Sarah Moss argues that in addition to full beliefs, credences can constitute knowledge.
For instance, your credence that it is raining outside can constitute knowledge, in just the .The aim of a probabilistic logic (also probability logic and probabilistic reasoning) is to combine the capacity of probability theory to handle uncertainty with the capacity of deductive logic to exploit structure of formal result is a richer and more expressive formalism with a broad range of possible application areas.
Probabilistic logics attempt to find a natural extension of.Reasoning under Uncertainty (Chapters 13 and - ) Probability theory will serve as the formal language for representing and reasoning with uncertain knowledge.
Representing Belief about Propositions. Bayes's Rule is the basis for probabilistic reasoning because given a prior model of the world in the form of P(A) and a new piece.