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.

This paper focuses on a sleep cycle, and improves the problem which an estimation accuracy of Real-Time Sleep Stage Estimation Method(RSSE) when it estimates a sleep stage on real time. Concretely, the proposed method re-estimates the sleep stage immediately after first sleep cycle since going to bed for the problem which decreases the correct rate of the sleep stage estimated by RSSE as time passes since going to bed. From the human subject experiments, the following implications have been revealed: (1) the correct rate improved by re-estimation in 8 cases out of 9 cases. (2) when the sleep cycle is long, it is possible to calculate the sleep cycle from the same subject's past sleeping information and if it is used, the estimation accuracy is improved for all cases.

This paper focuses on care support knowledge (especially focuses on the sleep related knowledge) and tackles its cognitive bias and humanity aspects from machine learning perspective through discussion of whether machine learning can correct commonly accepted knowledge and provide understandable knowledge in care support domain. For this purpose, this paper starts by introducing our data mining method (based on association rule learning) that can provide only necessary number of understandable knowledge without probabilities even if its accuracy slightly becomes worse, and shows its effectiveness in care plans support systems for aged persons as one of healthcare systems. The experimental result indicates that (1) our method can extract a few simple knowledge as understandable knowledge that clarifies what kinds of activities (e.g., rehabilitation, bathing) in care house contribute to having a deep sleep, but (2) the apriori algorithm as one of major association rule learning methods is hard to provide such knowledge because it needs calculate all combinations of activities executed by aged persons.

In this AAAI Spring symposium 2018, we discuss cognitive bias and humanity in the context of well-being AI. We define “well-being AI” as an AI research paradigm for promoting psychological well-being and maximizing human potential. The goals of well-being AI are (1) to understand how our digital experience affects our health and our quality of life and (2) to design well-being systems that put humans at the center. The important challenges of this research are how to quantify subjective things such as happiness, personal impressions, and personal values, and how to transform them into scientific representations with corresponding computational methods. One of the important keywords in understanding machine intelligence in human health and wellness is cognitive bias. Advances in big data and machine learning should not overlook some new threats to enlightened thought, such as the recent trend of social media platforms and commercial recommendation systems being used to manipulate people's inherent cognitive bias. The second important keyword is humanity. Rational thinking, on which early AI researchers had been focused their efforts, is recently and rapidly replacing human thinking by machines. Many people might have begun to believe that irrational thinking is the root of humanity. Empirical and philosophical discussions on AI and humanity would be welcome. This paper describes the detailed motivation, technical, and philosophical challenges of this symposium proposal.

Reports on the 2017 AAAI Spring Symposium Series

It is also important to remember that having a very sharp distinction of AI A rise in real-world applications of AI has stimulated for social good research is not always feasible, and significant interest from the public, media, and policy often unnecessary. While there has been significant makers. Along with this increasing attention has progress, there still exist many major challenges facing come a media-fueled concern about purported negative the design of effective AIbased approaches to deal consequences of AI, which often overlooks the with the difficulties in real-world domains. One of the societal benefits that AI is delivering and can deliver challenges is interpretability since most algorithms for in the near future. To address these concerns, the AI for social good problems need to be used by human symposium on Artificial Intelligence for the Social end users. Second, the lack of access to valuable data Good (AISOC-17) highlighted the benefits that AI can that could be crucial to the development of appropriate bring to society right now. It brought together AI algorithms is yet another challenge. Third, the researchers and researchers, practitioners, experts, data that we get from the real world is often noisy and and policy makers from a wide variety of domains.

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.

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.

This paper proposes a novel method to estimate sleep stage in real-time with a non-contact device. The proposed method employs the trigonometric function regression model to estimate prospective heart rate from the partially obtained heart rate and calculates the sleep stage from the estimated heart rate. This paper conducts the subject experiment and it is revealed that the proposed method enables to estimate the sleep stage in real-time, in particular the proposed method has the equivalent estimation accuracy as the previous method that estimates the sleep stage according to the entire heart rate during sleeping.

This paper proposes the concept of Well-being computing which is an information technology for improving not only our health as physical aspect but also our happiness as psy-chological aspect, and shows its potential from the sleep perspective. Concretely, this paper introduces “our personal-ized sleep monitoring system” as the well-being computing technologies and shows the following implications as its ef-fectiveness: (1) from the viewpoint of the service based on the real-time sleep, (1-a) good health is provided through a stable sleep of aged person in care house by reducing their sleep disturbance which may be occurred in diaper exchange, while happiness is provided by the smooth diaper exchange when aged person have a deep/light sleep; (1-b) good health is provided through a sufficient sleep time acquired by a fast falling asleep, while happiness is provided by releasing from anxiety of the insufficient sleep such as insomnia; and (2) from the viewpoint of the service based on the long-term sleep, (2-a) good health is provided through a deep sleep by continuing the daytime activities (such as a walking) which contribute to deriving a deep sleep, while happiness is provided by achieving a deep sleep through a change of life style; (2-b) good health is provided through a good sleep by keeping good bed condition (e.g., a change of a pillow or mattress when cotton/spring is deteriorated), while happiness is provided through a discovery of suitable bedding (such as suitable pillow or bed).