
An Introduction to Constraint-Based Temporal Reasoning by Robert Vorobeychik
Solving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field. This book provides a concise introduction to the core computational elements of temporal reasoning for use in AI systems for planning and scheduling, as well as systems that extract temporal information from data. It presents a survey of temporal frameworks based on constraints, both qualitative and quantitative, as well as of major temporal consistency techniques. The book also introduces the reader to more recent extensions to the core model that allow AI systems to explicitly represent temporal preferences and temporal uncertainty. This book is intended for students and researchers interested in constraint-based temporal reasoning. It provides a self-contained guide to the different representations of time, as well as examples of recent applications of time in AI systems.-
Graph Representation Learning
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A Concise Introduction to Models and Methods for Automated Planning
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Learning with Support Vector Machines
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Reasoning with Probabilistic and Deterministic Graphical Models
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Action Programming Languages
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Human Computation
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Learning and Decision-Making from Rank Data
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Explainable Human-AI Interaction
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Network Embedding
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Predicting Human Decision-Making
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Game Theory for Data Science
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Strategic Voting
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Statistical Relational Artificial Intelligence
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A Short Introduction to Preferences
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Case-Based Reasoning
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Representing and Reasoning with Qualitative Preferences
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Robot Learning from Human Teachers
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Visual Object Recognition
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General Game Playing
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Essential Principles for Autonomous Robotics
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Federated Learning
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Graph-Based Semi-Supervised Learning
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Intelligent Autonomous Robotics
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Answer Set Solving in Practice
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Representation Discovery using Harmonic Analysis
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Representations and Techniques for 3D Object Recognition and Scene Interpretation
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Introduction to Intelligent Systems in Traffic and Transportation
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Introduction to Symbolic Plan and Goal Recognition
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Judgment Aggregation
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Metric Learning
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Data Integration
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Trading Agents
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Introduction to Semi-Supervised Learning
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Transfer Learning for Multiagent Reinforcement Learning Systems
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Introduction to Graph Neural Networks
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Introduction to Logic Programming
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An Introduction to the Planning Domain Definition Language
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Lifelong Machine Learning, Second Edition
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Adversarial Machine Learning
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Multi-Objective Decision Making
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Active Learning
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Planning with Markov Decision Processes
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Computational Aspects of Cooperative Game Theory
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Algorithms for Reinforcement Learning
Roman Bartak is a professor at Charles University, Prague (Czech Republic). He leads the Constraint Satisfaction and Optimization Research Group that performs basic and applied research in the areas of satisfiability and discrete optimization problems. His work focuses on techniques of constraint satisfaction and their application to planning and scheduling. The research results are used in products of ILOG, Visopt, and ManOPT/Entellexi. Professor Bártak is teaching courses on artificial intelligence, planning, scheduling, and constraint programming at Charles University and he presented several tutorials on these topics at major conferences such as IJCAI, AAAI, ICAPS, SAC etc.; he is author of the On-line Guide to Constraint Programming (#2 source for Constraint Programming in Google).Robert A. Morris is a senior researcher in Computer Science in the Exploration Technology Directorate, Intelligent Systems Division at NASA Ames Research Center. His primary professional goal is the application of advanced AI technology in planning, scheduling, and plan execution to the next generation of NASA's exploration systems. His primary research interests include temporal constraint-based reasoning for automated planning and scheduling.K. Brent Venable is an associate professor in the Department of Computer Science of Tulane University and research scientist at IHMC, the Florida Institute of Human and Machine Cognition. In the past she has been an assistant professor in the Department of Pure and Applied Mathematics at the University of Padova (Italy). Her main research interests are within artificial intelligence and regard, in particular, compact preference representation formalisms, computational social choice, temporal reasoning and, more in general, constraint-based optimization. Her list of publications includes more than 70 papers including journals and proceedings of the main international conferences on the topics relevant to her interests. She is involved in a lively international scientific exchange and, among others, she collaborates with researchers from NASA Ames, SRI International, NICTA-UNSW (Australia), University of Amsterdam (The Netherlands), 4C (Ireland), and Ben-Gurion University (Israel).
| SKU | Unavailable |
| ISBN 13 | 9783031004391 |
| ISBN 10 | 3031004396 |
| Title | An Introduction to Constraint-Based Temporal Reasoning |
| Author | Robert Vorobeychik |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2014-03-06 |
| Number of pages | 107 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |












































