Overview
Part of the book series: Synthesis Lectures on Artificial Intelligence and Machine Learning (SLAIML)
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Table of contents (5 chapters)
About this book
Plan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents. This synergistic area of research combines, unites, and makes use of techniques and research from a wide range of areas including user modeling, machine vision, automated planning, intelligent user interfaces, human-computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. It plays a crucial role in a wide variety of applications including assistive technology, software assistants, computer and network security, human-robot collaboration, natural language processing, video games, and many more.
This wide range of applications and disciplines has produced a wealth of ideas, models, tools, and results in the recognition literature. However, it has also contributed to fragmentation in the field, with researchers publishing relevant results in a wide spectrum of journals and conferences.
This book seeks to address this fragmentation by providing a high-level introduction and historical overview of the plan and goal recognition literature. It provides a description of the core elements that comprise these recognition problems and practical advice for modeling them. In particular, we define and distinguish the different recognition tasks. We formalize the major approaches to modeling these problems using a single motivating example. Finally, we describe a number of state-of-the-art systems and their extensions, future challenges, and some potential applications.
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Introduction to Symbolic Plan and Goal Recognition
Authors: Reuth Mirsky, Sarah Keren, Christopher Geib
Series Title: Synthesis Lectures on Artificial Intelligence and Machine Learning
DOI: https://doi.org/10.1007/978-3-031-01589-2
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 10
Copyright Information: Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-031-00461-2Published: 25 January 2021
eBook ISBN: 978-3-031-01589-2Published: 31 May 2022
Series ISSN: 1939-4608
Series E-ISSN: 1939-4616
Edition Number: 1
Number of Pages: XX, 100
Topics: Artificial Intelligence, Machine Learning, Mathematical Models of Cognitive Processes and Neural Networks