scholarly journals Benchmark of public intent recognition services

2021 ◽  
Author(s):  
Petr Lorenc ◽  
Petr Marek ◽  
Jan Pichl ◽  
Jakub Konrád ◽  
Jan Šedivý
Keyword(s):  
Intent Recognition

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.

A Vision-Based Architecture for Intent Recognition

2007 ◽  
pp. 173-182 ◽  
Author(s):  
Alireza Tavakkoli ◽  
Richard Kelley ◽  
Christopher King ◽  
Mircea Nicolescu ◽  
Monica Nicolescu ◽  
...  
Keyword(s):  
Intent Recognition

Design of an Intent Recognition System for Dynamic, Rapid Motions in Unstructured Environments

2021 ◽  
pp. 1-10
Author(s):  
Pooja R Moolchandani ◽  
Anirban Mazumdar ◽  
Aaron Young
Keyword(s):  
Estimation Error ◽  
Recognition System ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Degree Reduction ◽  
Wearable Robot ◽  
Intent Recognition ◽  
Physical Systems ◽  
Start Up ◽  
Increasing Demand

Abstract In this study, we developed an offline, hierarchical intent recognition system for inferring the timing and direction of motion intent of a human operator when operating in an unstructured environment. There has been an increasing demand for robot agents to assist in these dynamic, rapid motions that are constantly evolving and require quick, accurate estimation of a user's direction of travel.An experiment was conducted in a motion capture space with six subjects performing threat-evasion in 8 directions, and their mechanical and neuromuscular signals were recorded for use in our intent recognition system (XGBoost). Investigated against current, analytical methods, our system demonstrated superior performance with quicker direction of travel estimation occurring 140 ms earlier in the movement and a 11.6 degree reduction of error. The results showed that we could even predict movement start 100 ms prior to the actual, thus allowing any physical systems to start up. Our direction estimation had an optimal performance of 8.8 degrees, or 2.4% of the 360 degrees range of travel, using 3-axis kinetic data. The performance of other sensors and their combinations indicate that there are additional possibilities to obtain low estimation error. These findings are promising as they can be used to inform the design of a wearable robot aimed at assisting users in dynamic motions, while in environments with oncoming threats.

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