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Books on the topic 'Automaton inference'

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Author: Grafiati

Published: 1 June 2024

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1

Lee, Won Don. Probabilistic inference. Urbana, Ill: Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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2

Lee, Won Don. Probabilistic inference: Theory and practice. Urbana, Ill: Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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3

Pouly, Marc. Generic Inference: A Unifying Theory for Automated Reasoning. Hoboken, New Jersey: Wiley, 2011.

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4

Farreny, Henri. AI and expertise: Heuristic search, inference engines, automatic proving. Chichester: E. Horwood, 1989.

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5

Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

6

Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

7

Higuera, Colin De La. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.

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8

Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.

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9

Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2014.

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10

Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.

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11

Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2011.

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12

Farreny, Henri. AI & Expertise: Heuristic Search, Inference Engines, Automatic Proving. Prentice Hall, 1989.

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13

Cappé, Olivier, Eric Moulines, and Tobias Ryden. Inference in Hidden Markov Models. Springer, 2010.

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14

Hadjicostis, Christoforos N. Estimation and Inference in Discrete Event Systems: A Model-Based Approach with Finite Automata. Springer International Publishing AG, 2020.

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15

Hadjicostis, Christoforos N. Estimation and Inference in Discrete Event Systems: A Model-Based Approach with Finite Automata. Springer, 2019.

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16

Zhou, Xuefeng. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Nature, 2020.

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17

Wu, Hongmin, Zhihao Xu, Shuai Li, Xuefeng Zhou, and Juan Rojas. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Singapore Pte. Limited, 2020.

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18

Wu, Hongmin, Zhihao Xu, Shuai Li, Xuefeng Zhou, and Juan Rojas. Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Singapore Pte. Limited, 2020.

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19

Ramonyai, Jerry. Data Science: Algorithm Analysis, Approximation, Automata, Bayesian Inference, Statistics, Science,TR6 Binary Tree, Bioinformatics, Algorithms, Complexity Theory, Computability and Research. Independently Published, 2022.

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20

Hilton, Denis. Social Attribution and Explanation. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.33.

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Attribution processes appear to be an integral part of human visual perception, as low-level inferences of causality and intentionality appear to be automatic and are supported by specific brain systems. However, higher-order attribution processes use information held in memory or made present at the time of judgment. While attribution processes about social objects are sometimes biased, there is scope for partial correction. This chapter reviews work on the generation, communication, and interpretation of complex explanations, with reference to explanation-based models of text understanding that result in situation models of narratives. It distinguishes between causal connection and causal selection, and suggests that a factor will be discounted if it is not perceived to be connected to the event and backgrounded if it is perceived to be causally connected to that event, but is not selected as relevant to an explanation. The final section focuses on how interpersonal explanation processes constrain causal selection.

21

Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.

22

Hatlebrekke, Kjetil Anders. The Problem of Secret Intelligence. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9780748691838.001.0001.

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Why is intelligence so hard to define? Why is there no systematic or adequate theory of intelligence? This book argues that classic intelligence production has been premised on an ill-founded belief in an automatic inference between history and the future, and that the lack of a working theory has exacerbated this problem. The book uses classic cases of intelligence failure to demonstrate how this problem creates a restricted language in intelligence communities that undermines threat perception. From these cases it concludes that intelligence needs to be re-thought, and argues that good intelligence is the art of threat perception beyond the limits of our habitual thinking and shared experience. This book therefore argues that intelligence can never be truths, only uncertain theories about the future. Qualified intelligence work is, accordingly, ideas that lead to theories about the future. These theories should always seek to explain a comprehension of the wholeness of threats. The hypothesis derived from these theories must thereafter be tested, as tests that make the theories less uncertain. This implies that intelligence never can be anything but uncertain theories about the future that are made less uncertain through scientific, critical tests of hypotheses derived from these theories. High quality intelligence institutions conduct these tests in what is known as the intelligence cycle. This cycle works well if it mirrors good thinking.