Parallel reasoning in recursive casual networks
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Total Pages | : 18 |
Release | : 1988 |
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Author | : |
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Total Pages | : 18 |
Release | : 1988 |
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Total Pages | : 32 |
Release | : 1988 |
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Author | : T.S. Levitt |
Publisher | : Elsevier |
Total Pages | : 435 |
Release | : 2014-06-28 |
Genre | : Computers |
ISBN | : 1483296547 |
Clearly illustrated in this volume is the current relationship between Uncertainty and AI.It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally into four sections which highlight both the strengths and weaknesses of the current state of the relationship between Uncertainty and AI.
Author | : Chi Ping Tsang |
Publisher | : World Scientific |
Total Pages | : 802 |
Release | : 1990-11-01 |
Genre | : |
ISBN | : 9814569674 |
This is a collection of papers on the recent research in Artificial Intelligence in Australia and the Asian region. It contains papers on the theory and practice of AI. Topics dealt with include logic, artificial neural nets, knowledge representation, computer vision, robotics, expert systems and the application of AI in many areas.
Author | : Jonas Peters |
Publisher | : MIT Press |
Total Pages | : 289 |
Release | : 2017-11-29 |
Genre | : Computers |
ISBN | : 0262037319 |
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
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Total Pages | : 892 |
Release | : 1997 |
Genre | : Artificial intelligence |
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Author | : Artur S. D'Avila Garcez |
Publisher | : Springer Science & Business Media |
Total Pages | : 200 |
Release | : 2009 |
Genre | : Computers |
ISBN | : 3540732454 |
This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
Author | : Joseph Y. Halpern |
Publisher | : MIT Press |
Total Pages | : 240 |
Release | : 2016-08-12 |
Genre | : Computers |
ISBN | : 0262035022 |
Explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression.