Plan, Activity, and Intent Recognition

Theory and Practice

Edited by

  • Gita Sukthankar, Assistant Professor, University of Central Florida
  • Christopher Geib, Associate Professor, Drexel University
  • Hung Hai Bui, Principal Scientist, Laboratory for Natural Language Understanding, Nuance
  • David Pynadath, Research Scientist, Institute for Creative Technologies, USC
  • Robert Goldman, Staff Scientist, Smart Information Flow Technologies LLC

Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning.

Plan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of applications including:

  • personal agent assistants
  • computer and network security
  • opponent modeling in games and simulation systems
  • coordination in robots and software agents
  • web e-commerce and collaborative filtering
  • dialog modeling
  • video surveillance
  • smart homes

In this book, follow the history of this research area and witness exciting new developments in the field made possible by improved sensors, increased computational power, and new application areas.

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Academic researchers and industrial researchers in specific application areas such as user interface design and video surveillance systems.


Book information

  • Published: February 2014
  • ISBN: 978-0-12-398532-3


"Plan recognition, activity recognition, and intent recognition all involve making inferences about other actors from observations of their behavior. These inferences are crucial in a wide range of applications including intelligent assistants, computer security, and dialogue management systems. This volume, edited by leading researchers, provides a timely snapshot of some of the key formulations, techniques, and applications that have been developed in this rich and rapidly evolving field."

-Dr. Hector Geffner, ICREA & Universitat Pompeu Fabra, Barcelona

"This book collects some of the top senior people in the field of plan recognition with some of the newest researchers. It offers a comprehensive review of plan recognition from multiple viewpoints, encompassing both logical and probabilistic formalisms and covering mathematical theory, computer science applications, and human cognitive models."

-Dr. Peter Norvig, Director of Research at Google Inc.

"Plan, Activity, and Intent Recognition is an indispensable resource for creating systems that infer peoples’ goals and plans on the basis of their behavior. Researchers in security, natural language dialog systems, smart spaces and pervasive computing, and other areas will find a comprehensive and up to date survey of methods, applications, and open research challenges."

-Dr. Henry Kautz, University of Rochester, Past President of AAAI (Association for the Advancement of Artificial Intelligence)

Table of Contents


Plan and Goal Recognition
1) Hierarchical Goal Recognition
Nate Blaylock and James Allen
2) Weighted Abduction for Discourse Processing Based on Integer Linear Programming
Naoya Inoue, Ekaterina Ovchinnikova, Kentaro Inui and Jerry Hobbs.
3) Plan Recognition using Statistical Relational Models
Sindhu Raghavan, Parag Singla, and Raymond J. Mooney
4) Keyhole Adversarial Plan Recognition for Recognition of Suspicious and Anomalous Behavior
Dorit Avrahami and Gal Kaminka

Activity Discovery and Recognition
5) Scaling Activity Recognition
Parisa Rashidi
6) Extraction of Latent Patterns and Contexts from Social Honest Signals Using Hierarchical Dirichlet Processes
Thuong Nguyen, Dinh Phung, Sunil Gupta and Svetha Venkatesh

Modeling Human Cognition
7) Modeling Human Plan Recognition using Bayesian Theory of Mind
Chris Baker and Joshua Tenenbaum
8) DecisionTheoretic Planning in Multiagent Settings with Application to Modeling Human Strategic Behavior
Prashant Doshi, Xia Qu, and Adam Goode

Multiagent Systems
9) MultiAgent Plan Recognition from Partially Observed Team Traces
Hankz Zhuo
10) Role-based Ad Hoc Teamwork
Katie Genter, Noa Agmon and Peter Stone

11) Probabilistic plan recognition for proactive assistant agents
Jean Oh, Felipe Meneguzzi and Katia Sycara
12) Recognizing Player Goals in OpenEnded Digital Games with Markov Logic Networks
Eun Ha, Jonathan Rowe, Bradford Mott, and James Lester 
13)Using Opponent Modeling to Adapt Team Play in American Football
Kennard Laviers and Gita Sukthankar
14) Intent Recognition for HumanRobot Interaction
Richard Kelley, Alireza Tavakkoli, Christopher King, Amol Ambardekar,
Liesl Wigand, Monica Nicolescu and Mircea Nicolescu