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001 978-3-031-01589-2
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020 _a9783031015892
_9978-3-031-01589-2
024 7 _a10.1007/978-3-031-01589-2
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aMirsky, Reuth.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979653
245 1 0 _aIntroduction to Symbolic Plan and Goal Recognition
_h[electronic resource] /
_cby Reuth Mirsky, Sarah Keren, Christopher Geib.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXX, 100 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPreface -- Acknowledgments -- Introduction -- Defining a Recognition Problem -- Implicit vs. Explicit Representation of Knowledge -- Improving a Recognizer -- Future Directions -- Bibliography -- Authors' Biographies.
520 _aPlan 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979654
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aKeren, Sarah.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979655
700 1 _aGeib, Christopher.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979656
710 2 _aSpringerLink (Online service)
_979657
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000348
776 0 8 _iPrinted edition:
_z9783031004612
776 0 8 _iPrinted edition:
_z9783031027178
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979658
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01589-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c84821
_d84821