Plan, Activity, and Intent Recognition

2014 ◽  
pp. i
Keyword(s):  
Intent Recognition ◽  
Plan Activity

The AAAI-13 Conference Workshops

AI Magazine ◽  
2013 ◽  
Vol 34 (4) ◽  
pp. 108-115
Author(s):  
Vikas Agrawal ◽  
Christopher Archibald ◽  
Mehul Bhatt ◽  
Hung Bui ◽  
Diane J. Cook ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Informatics ◽  
Imperfect Information ◽  
Intent Recognition ◽  
System Architectures ◽  
Wide Range ◽  
Trading Agent ◽  
Context Aware System ◽  
Plan Activity ◽  
And Robotics

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16).

scholarly journals Reports of the AAAI 2010 Conference Workshops

AI Magazine ◽  
2010 ◽  
Vol 31 (4) ◽  
pp. 95
Author(s):  
David W. Aha ◽  
Mark Boddy ◽  
Vadim Bulitko ◽  
Artur S. D'Avila Garcez ◽  
Prashant Doshi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Game Theory ◽  
Social Systems ◽  
Visual Representations ◽  
Intent Recognition ◽  
Systems Model ◽  
Symbolic Learning ◽  
Wide Range ◽  
Intelligent Security ◽  
Plan Activity

The AAAI-10 Workshop program was held Sunday and Monday, July 11–12, 2010 at the Westin Peachtree Plaza in Atlanta, Georgia. The AAAI-10 workshop program included 13 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Fun, Bridging the Gap between Task and Motion Planning, Collaboratively-Built Knowledge Sources and Artificial Intelligence, Goal-Directed Autonomy, Intelligent Security, Interactive Decision Theory and Game Theory, Metacognition for Robust Social Systems, Model Checking and Artificial Intelligence, Neural-Symbolic Learning and Reasoning, Plan, Activity, and Intent Recognition, Statistical Relational AI, Visual Representations and Reasoning, and Abstraction, Reformulation, and Approximation. This article presents short summaries of those events.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Question Answering ◽  
Intent Recognition ◽  
Affective Content ◽  
Learning Plan ◽  
Dialog System ◽  
Affective Content Analysis ◽  
Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

scholarly journals CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy

2021 ◽  
pp. 1-1
Author(s):  
Xin Huang ◽  
Stephen Mcgill ◽  
Jonathan Decastro ◽  
Luke Fletcher ◽  
John Leonard ◽  
...  
Keyword(s):  
Intent Recognition

MyrrorBot : A Digital Assistant Based on Holistic User Models for Personalized Access to Online Services

2021 ◽  
Vol 39 (4) ◽  
pp. 1-34
Author(s):  
Cataldo Musto ◽  
Fedelucio Narducci ◽  
Marco Polignano ◽  
Marco De Gemmis ◽  
Pasquale Lops ◽  
...  
Keyword(s):  
Natural Language ◽  
Information Needs ◽  
Effective Interaction ◽  
Personal Digital Assistant ◽  
User Profiling ◽  
Online Services ◽  
Intent Recognition ◽  
User Models ◽  
Time Required ◽  
The Way

In this article, we present MyrrorBot , a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, and food recommendation s, in a personalized way, by exploiting a strategy for implicit user modeling called holistic user profiling ; (ii) query their own user models, to inspect the features encoded in their profiles and to increase their awareness of the personalization process. Basically, the system allows the users to formulate natural language requests related to their information needs. Such needs are roughly classified in two groups: quantified self-related needs (e.g., Did I sleep enough? Am I extrovert? ) and personalized access to online services (e.g., Play a song I like ). The intent recognition strategy implemented in the platform automatically identifies the intent expressed by the user and forwards the request to specific services and modules that generate an appropriate answer that fulfills the query. In the experimental evaluation, we evaluated both qualitative (users’ acceptance of the system, usability) as well as quantitative (time required to complete basic tasks, effectiveness of the personalization strategy) aspects of the system, and the results showed that MyrrorBot can improve the way people access online services and applications. This leads to a more effective interaction and paves the way for further development of our system.

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