Introduction to Symbolic Plan and Goal Recognition by Reuth Mirsky
Preface.- Acknowledgments.- Introduction.- Defining a Recognition Problem.- Implicit vs. Explicit Representation of Knowledge.- Improving a Recognizer.- Future Directions.- Bibliography.- Authors' Biographies.-
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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An Introduction to Constraint-Based Temporal Reasoning
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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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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
Reuth Mirsky is a postdoctoral fellow at the Computer Science department in the University of Texas at Austin, under the mentorship of Prof. Peter Stone. Reuth received her Ph.D. from the Department of Software and Information Systems Engineering at Ben Gurion University, under the supervision of Prof. Kobi Gal. Her Ph.D. thesis was on plan recognition in exploratory environments. Reuth’s research focuses on improving existing AI with human-inspired design, and her algorithms have been applied in various tasks for education, clinical treatment, and finance. Reuth’s contributions were published by leading AI and HRI conferences and journals. Her longterm research vision is to enable better collaborations in mixed human-and-artificial agents settings.Sarah Keren is a postdoctoral fellow at Harvard University, where she is affiliated with the Center for Research on Computation and Society (CRCS). Her mentors are Prof. Barbara Grosz and Prof. David Parkes. Before coming to Harvard, Sarah completed her Ph.D. at the Faculty of Industrial Engineering and Management of the Technion–Israel Institute of Technology, where she was advised by Prof. Avigdor Gal and Dr. Erez Karpas. Sarah’s research focuses on manipulating and redesigning environments for optimizing their utility. In particular, her Ph.D. work established the task of Goal Recognition Design, where environments are manipulated to maximize the ability to recognize the goals of agents acting within them. Sarah’s work has appeared in three leading artificial intelligence conferences (AAAI, ICAPS, and IJCAI). She has received various excellence awards, including an honorable mention for best paper at ICAPS 2014 as well as the Eric and Wendy Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences.Christopher Geib is a Principal Researcher at SIFT LLC and an internationally recognized researcher in probabilistic plan recognition and planning. He received his Ph.D. in Computer Science from the University of Pennsylvania in 1995. Prior to joining SIFT, he had an extensive career both in academia as an Associate Professor at Drexel University and a Research Fellow at the University of Edinburgh, and in industry as a Principal Research Scientist at Honeywell. He has published more than 50 scholarly publications. His interests include probabilistic plan recognition and planning under uncertainty based on formal grammars and interaction between human and synthetic agents using actions and language. He has been the principal architect of multiple plan recognition systems over the last 20+ years.
| SKU | Unavailable |
| ISBN 13 | 9783031004612 |
| ISBN 10 | 3031004612 |
| Title | Introduction to Symbolic Plan and Goal Recognition |
| Author | Reuth Mirsky |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2021-01-25 |
| Number of pages | 100 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |