Plan, Activity, and Intent Recognition: Theory and Practice by Gita Sukthankar

By Gita Sukthankar

Plan acceptance, task popularity, and rationale reputation jointly mix and unify strategies from consumer modeling, laptop imaginative and prescient, clever consumer interfaces, human/computer interplay, self sustaining and multi-agent platforms, ordinary language realizing, and laptop studying.

Plan, task, and motive Recognition explains the the most important function of those strategies in a large choice of functions together with:

. own agent assistants . computing device and community defense . opponent modeling in video games and simulation platforms . coordination in robots and software program brokers . net e-commerce and collaborative filtering . conversation modeling . video surveillance . clever houses during this e-book, stick with the heritage of this learn sector and witness fascinating new advancements within the box made attainable via more suitable sensors, elevated computational strength, and new software areas.

  • Combines simple idea on algorithms for plan/activity reputation besides effects from contemporary workshops and seminars
  • Explains tips to interpret and realize plans and actions from sensor data
  • Provides priceless heritage wisdom and assembles key innovations into one consultant for researchers or scholars learning those disciplines

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Plan, Activity, and Intent Recognition: Theory and Practice

Plan acceptance, task acceptance, and cause attractiveness jointly mix and unify recommendations from consumer modeling, computing device imaginative and prescient, clever consumer interfaces, human/computer interplay, self sufficient and multi-agent structures, usual language realizing, and computing device studying. Plan, job, and cause popularity explains the the most important position of those ideas in a large choice of purposes together with: .

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Extra resources for Plan, Activity, and Intent Recognition: Theory and Practice

Sample text

17] El-Nasr M, Drachen A, Canossa A, editors. Game analytics: maximizing the value of player data. Springer; 2013. [18] Fishkin K, Philipose M, Rea A. Hands-on RFID: wireless wearables for detecting use of objects. In: Proceedings of the IEEE international symposium on wearable computers; 2005. p. 38–41. [19] Geib Christopher W. Delaying commitment in probabilistic plan recognition using combinatory categorial grammars. In: Proceedings of the international joint conference on artificial intelligence; 2009.

Artif Intell 1994;66(2):345–74. [13] Cohen Philip R, Raymond Perrault C. Elements of a plan-based theory of speech acts. Cogn Sci 1979;3:177–212. [14] Duchi John C, Jordan Michael I, Wainwright Martin. Privacy aware learning. In: Neural information processing systems; 2012. p. 1439–47. [15] Duong TV, Bui HH, Phung DQ, Venkatesh S. Activity recognition and abnormality detection with the switching Hidden semi-Markov model. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition; 2005.

A probabilistic plan recognition algorithm based on plan tree grammars. Artif Intell 2009;173(11):1101–32. [21] Geib Christopher W, Swetenham Christopher E. Parallelizing plan recognition. In: Workshop on plan activity and intent recognition at the twenty-seventh AAAI conference on artificial intelligence; 2013. p. 10–16. [22] Getoor Lise, Taskar Ben. Introduction to statistical relational learning (Adaptive computation and machine learning). MIT Press; 2007. [23] Gold Kevin. Training goal recognition online from low-level inputs in an action-adventure game.

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