Rehabilitation robots physically support and guide a patient's limb during motor therapy, but require sophisticated control algorithms and artificial intelligence to do so. This article provides an overview of the state of the art in this area. It begins with the dominant paradigm of assistive control, from impedance-based cooperative controller through electromyography and intention estimation. It then covers challenge-based algorithms, which provide more difficult and complex tasks for the patient to perform through resistive control and error augmentation. Furthermore, it describes exercise adaptation algorithms that change the overall exercise intensity based on the patient's performance or physiological responses, as well as socially assistive robots that provide only verbal and visual guidance. The article concludes with a discussion of the current challenges in rehabilitation robot software: evaluating existing control strategies in a clinical setting as well as increasing the robot's autonomy using entirely new artificial intelligence techniques.
Active problem solving has been shown to be one of the most effective ways to acquire complex skills. Whether one is learning a programming language by implementing a computer program, or learning calculus by solving problems, context sensitive feedback and guidance are crucial to keeping problem solving efforts fruitful and efficient. This article reviews AI-based algorithms that can diagnose student difficulties during active problem solving and serve as the basis for providing context-sensitive and individualized guidance. The article also describes the crucial role sensor based estimates of cognitive resources such as working memory capacity and attention can play in enhancing the diagnostic capabilities of intelligent instructional systems.
This article is aimed at helping AI researchers and practitioners imagine roles intelligent technologies might play in the many different and varied ecosystems in which people learn. My observations are based on learning sciences research of the past several decades, the possibilities of new technologies of the past few years, and my experience as program officer for the National Science Foundation’s Cyberlearning and Future Learning Technologies program. My thesis is that new technologies have potential to transform possibilities for fostering learning in both formal and informal learning environments by making it possible and manageable for learners to engage in the kinds of project work that professionals engage in and learn important content, skills, practices, habits, and dispositions from those experiences. The expertise of AI researchers and practitioners is critical to that vision, but it will require teaming up with others — for example, technology imagineers, educators, and learning scientists.
Ford, Kenneth M. (Florida Institute for Human and Machine Cognition (IHMC)) | Hayes, Patrick J. (Florida Institute for Human and Machine Cognition (IHMC)) | Glymour, Clark (Florida Institute for Human and Machine Cognition (IHMC)) | Allen, James (Florida Institute for Human and Machine Cognition (IHMC))
This introduction focuses on how human-centered computing (HCC) is changing the way that people think about information technology. The AI perspective views this HCC framework as embodying a systems view, in which human thought and action are linked and equally important in terms of analysis, design, and evaluation. This emerging technology provides a new research outlook for AI applications, with new research goals and agendas.
Recent advances in cognitive computing componentry combined with other factors are leading to commercially viable cognitive systems. From chips to smart phones to public and private clouds, industrial strength “cognition as a service” is beginning to appear at all scales in business and society. Furthermore, in the age of zettabytes on the way to yottabytes, the designers, engineers, and managers of future smart systems will depend on cognition as a service. Cognition as a service can help unlock the mysteries of big data and ultimately boost the creativity and productivity of professionals and their teams, the productive output of industries and organizations, as well as the GDP (gross domestic product) of regions and nations. In this and the next decade, cognition as a service will allow us to re-image work practices, augmenting and scaling expertise to transform professions, industries, and regions.
Garcia, David (ETH Zurich) | Halegoua, Germaine (University of Kansas) | Mejova, Yelena (Qatar Computing Research Institute.) | Perra, Nicola (Northeastern University) | Pfeffer, Jürgen (Carnegie Mellon University) | Ruths, Derek (McGill University) | Weber, Ingmar (Qatar Computing Research Institute) | West, Robert (Stanford University) | Zia, Leila (Wikimedia Foundation)
The 2015 workshops at the International AAAI Conference on Web and Social Media were held on May 26 in Oxford, UK. The workshop program included seven workshops, including Auditing Algorithms From the Outside: Methods and Implications, Digital Placemaking: Augmenting Physical Places with Contextual Social Data, Modeling and Mining Temporal Interactions Religion on Social Media, Standards and Practices in Large-Scale Social Media Research, Wikipedia, a Social Pedia: Research Challenges and Opportunities, and The ICWSM Science Slam. This article contains the written reports of 5 of the workshops
Baarslag, Tim (University of Southampton) | Aydoğan, Reyhan (Delft University of Technology) | Hindriks, Koen V. (Delft University of Technology) | Fujita, Katsuhide (Tokyo University of Agriculture and Technology) | Ito, Takayuki (Nagoya Institute of Technology) | Jonker, Catholijn M. (Delft University of Technology)
The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state-of-the-art in automated multi-issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, bilateral and multilateral protocols. Two of the challenges that remain are: How to develop argumentation-based negotiation agents that next to bids, can inform and argue to obtain an acceptable agreement for both parties, and how to create agents that can negotiate in a human fashion.
Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.