scholarly journals The 2015 AAAI Fall Symposium Series Reports

AI Magazine ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 85-90 ◽  
Author(s):  
Nisar Ahmed ◽  
Paul Bello ◽  
Selmer Bringsjord ◽  
Micah Clark ◽  
Bradley Hayes ◽  
...  

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.

AI Magazine ◽  
2017 ◽  
Vol 38 (2) ◽  
pp. 86-90
Author(s):  
Patrícia Alves-Oliveira ◽  
Richard G. Freedman ◽  
Dan Grollman ◽  
Laura Herlant ◽  
Laura Humphrey ◽  
...  

The AAAI 2016 Fall Symposium Series was held Thursday through Saturday, November 17–19, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the six symposia were Accelerating Science: A Grand Challenge for AI; Artificial Intelligence for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Cross-Disciplinary Challenges for Autonomous Systems, Privacy and Language Technologies, Shared Autonomy in Research and Practice. The highlights of each (except Acceleration Science) symposium are presented in this report.


AI Magazine ◽  
2018 ◽  
Vol 39 (2) ◽  
pp. 81-86
Author(s):  
Arjuna Flenner ◽  
Marlena R. Fraune ◽  
Laura M. Hiatt ◽  
Tony Kendall ◽  
John E. Laird ◽  
...  

The AAAI 2017 Fall Symposium Series was held Thursday through Saturday, November 9–11, at the Westin Arlington Gateway in Arlington, Virginia, adjacent to Washington, DC. The titles of the six symposia were Artificial Intelligence for Human-Robot Interaction; Cognitive Assistance in Government and Public Sector Applications; Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks; Human-Agent Groups: Studies, Algorithms and Challenges; Natural Communication for Human-Robot Collaboration; and A Standard Model of the Mind. The highlights of each symposium (except the Natural Communication for Human-Robot Collaboration symposium, whose organizers did not submit a report) are presented in this report.


AI Magazine ◽  
2015 ◽  
Vol 36 (3) ◽  
pp. 107-112
Author(s):  
Adam B. Cohen ◽  
Sonia Chernova ◽  
James Giordano ◽  
Frank Guerin ◽  
Kris Hauser ◽  
...  

The AAAI 2014 Fall Symposium Series was held Thursday through Saturday, November 13–15, at the Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC. The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction, Energy Market Prediction, Expanding the Boundaries of Health Informatics Using AI, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences, Natural Language Access to Big Data, and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report.


AI Magazine ◽  
2017 ◽  
Vol 37 (4) ◽  
pp. 83-88
Author(s):  
Christopher Amato ◽  
Ofra Amir ◽  
Joanna Bryson ◽  
Barbara Grosz ◽  
Bipin Indurkhya ◽  
...  

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2016 Spring Symposium Series on Monday through Wednesday, March 21-23, 2016 at Stanford University. The titles of the seven symposia were (1) AI and the Mitigation of Human Error: Anomalies, Team Metrics and Thermodynamics; (2) Challenges and Opportunities in Multiagent Learning for the Real World (3) Enabling Computing Research in Socially Intelligent Human-Robot Interaction: A Community-Driven Modular Research Platform; (4) Ethical and Moral Considerations in Non-Human Agents; (5) Intelligent Systems for Supporting Distributed Human Teamwork; (6) Observational Studies through Social Media and Other Human-Generated Content, and (7) Well-Being Computing: AI Meets Health and Happiness Science.


2019 ◽  
Vol 30 (1) ◽  
pp. 7-8
Author(s):  
Dora Maria Ballesteros

Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8].


Author(s):  
Vedang Naik ◽  
◽  
Rohit Sahoo ◽  
Sameer Mahajan ◽  
Saurabh Singh ◽  
...  

Reinforcement learning is an artificial intelligence paradigm that enables intelligent agents to accrue environmental incentives to get superior results. It is concerned with sequential decision-making problems which offer limited feedback. Reinforcement learning has roots in cybernetics and research in statistics, psychology, neurology, and computer science. It has piqued the interest of the machine learning and artificial intelligence groups in the last five to ten years. It promises that it allows you to train agents using rewards and penalties without explaining how the task will be completed. The RL issue may be described as an agent that must make decisions in a given environment to maximize a specified concept of cumulative rewards. The learner is not taught which actions to perform but must experiment to determine which acts provide the greatest reward. Thus, the learner has to actively choose between exploring its environment or exploiting it based on its knowledge. The exploration-exploitation paradox is one of the most common issues encountered while dealing with Reinforcement Learning algorithms. Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. We describe how to utilize several deep reinforcement learning (RL) algorithms for managing a Cartpole system used to represent episodic environments and Stock Market Trading, which is used to describe continuous environments in this study. We explain and demonstrate the effects of different RL ideas such as Deep Q Networks (DQN), Double DQN, and Dueling DQN on learning performance. We also look at the fundamental distinctions between episodic and continuous activities and how the exploration-exploitation issue is addressed in their context.


AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 93-98 ◽  
Author(s):  
Vita Markman ◽  
Georgi Stojanov ◽  
Bipin Indurkhya ◽  
Takashi Kido ◽  
Keiki Takadama ◽  
...  

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.


Author(s):  
Ning Wang ◽  
David V. Pynadath ◽  
K. V. Unnikrishnan ◽  
Santosh Shankar ◽  
Chirag Merchant

AI Magazine ◽  
2018 ◽  
Vol 39 (4) ◽  
pp. 29-35
Author(s):  
Christopher Amato ◽  
Haitham Bou Ammar ◽  
Elizabeth Churchill ◽  
Erez Karpas ◽  
Takashi Kido ◽  
...  

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University’s Department of Computer Science, presented the 2018 Spring Symposium Series, held Monday through Wednesday, March 26–28, 2018, on the campus of Stanford University. The seven symposia held were AI and Society: Ethics, Safety and Trustworthiness in Intelligent Agents; Artificial Intelligence for the Internet of Everything; Beyond Machine Intelligence: Understanding Cognitive Bias and Humanity for Well-Being AI; Data Efficient Reinforcement Learning; The Design of the User Experience for Artificial Intelligence (the UX of AI); Integrated Representation, Reasoning, and Learning in Robotics; Learning, Inference, and Control of Multi-Agent Systems. This report, compiled from organizers of the symposia, summarizes the research of five of the symposia that took place.


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