Cohen, Adam B. (Independent Consultant) | Chernova, Sonia (Worcester Polytechnic Institute) | Giordano, James (Georgetown University Medical Center) | Guerin, Frank (University of Aberdeen) | Hauser, Kris (Duke University) | Indurkhya, Bipin (AGH University of Science and Technology) | Leonetti, Matteo (University of Texas at Austin) | Medsker, Larry (Siena College) | Michalowski, Martin (Adventium Labs) | Sonntag, Daniel (German Research Center for Artificial Intelligence) | Stojanov, Georgi (American University of Paris) | Tecuci, Dan G. (IBM Watson, Austin) | Thomaz, Andrea (Georgia Institute of Technology) | Veale, Tony (University College Dublin) | Waltinger, Ulli (Siemens Corporate Technology)
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.
Wobcke, Wayne (University of New South Wales) | Krzywicki, Alfred (University of New South Wales) | Kim, Yang Sok (Keimyung University) | Cai, Xiongcai (University of New South Wales) | Bain, Michael (University of New South Wales) | Compton, Paul (University of New South Wales) | Mahidadia, Ashesh (smartAcademic)
Online dating is a prime application area for recommender systems, as users face an abundance of choice, must act on limited information, and are participating in a competitive matching market. This article reports on the successful deployment of a people-to-people recommender system on a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that the recommender system delivered its projected benefits.
Vallati, Mauro (University of Huddersfield) | Chrpa, Lukas (University of Huddersfield) | Grześ, Marek (University of Kent) | McCluskey, Thomas Leo (University of Huddersfield) | Roberts, Mark (Naval Research Laboratory) | Sanner, Scott (NICTA) | Editor, Managing (AAAI)
We review the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part evaluated planning systems in ways that pushed the edge of existing planner performance by introducing new challenges, novel tasks, or both. The competition surpassed again the number of competitors than its predecessor, highlighting the competition’s central role in shaping the landscape of ongoing developments in evaluating planning systems.
Yeh, Peter Z. (Nuance Communications) | Ramachandran, Deepak (Nuance Communications) | Douglas, Benjamin (Nuance Communications) | Ratnaparkhi, Adwait (Nuance Communications) | Jarrold, William (Nuance Communications) | Provine, Ronald (Nuance Communications) | Patel-Schneider, Peter F. (Nuance Communications) | Laverty, Stephen (Nuance Communications) | Tikku, Nirvana (Nuance Communications) | Brown, Sean (Nuance Communications) | Mendel, Jeremy (Nuance Communications) | Emfield, Adam (Nuance Communications)
In this article, we report on a multiphase R&D effort to develop a conversational second screen application for TV program discovery. Our goal is to share with the community the breadth of artificial intelligence (AI) and natural language (NL) technologies required to develop such an application along with learnings from target end-users. We first give an overview of our application from the perspective of the end-user. We then present the architecture of our application along with the main AI and NL components, which were developed over multiple phases. The first phase focuses on enabling core functionality such as effectively finding programs matching the user’s intent. The second phase focuses on enabling dialog with the user. Finally, we present two user studies, corresponding to these two phases. The results from both studies demonstrate the effectiveness of our application in the target domain.
Wu, Jian (Pennsylvania State University) | Williams, Kyle Mark (Pennsylvania State University) | Chen, Hung-Hsuan (Industrial Technology Research Institute) | Khabsa, Madian (Pennsylvania State University) | Caragea, Cornelia (University of North Texas) | Tuarob, Suppawong (Pennsylvania State University) | Ororbia, Alexander G. (Pennsylvania State University) | Jordan, Douglas (Pennsylvania State University) | Mitra, Prasenjit (Pennsylvania State University) | Giles, C. Lee (Pennsylvania State University)
CiteSeerX is a digital library search engine providing access to more than five million scholarly documents with nearly a million users and millions of hits per day. We present key AI technologies used in the following components: document classification and de-duplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5–6 years. We show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. We also present AI technologies implemented in table and algorithm search, which are special search modes in CiteSeerX. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.
Davis, Randall (Massachusetts Institute of Technology) | Libon, David (Drexel University College of Medicine) | Au, Roda (Boston University School of Medicine) | Pitman, David (Kytheram) | Penney, Dana (Lahey Hospital and Medical Center)
The digital clock drawing test is a fielded application that provides a major advance over existing neuropsychological testing technology. It captures and analyzes high precision information about both outcome and process, opening up the possibility of detecting subtle cognitive impairment even when test results appear superficially normal. We describe the design and development of the test, document the role of AI in its capabilities, and report on its use over the past seven years. We outline its potential implications for earlier detection and treatment of neurological disorders. We set the work in the larger context of the THink project, which is exploring multiple approaches to determining cognitive status through the detection and analysis of subtle behaviors.
Morris, Robert (NASA) | Bonet, Blai (Universidad Simón Bolívar) | Cavazza, Marc (Teesside University) | desJardins, Marie (University of Maryland, Baltimore County) | Felner, Ariel (BenGurion University) | Hawes, Nick (University of Birmingham) | Knox, Brad (Massachusetts Institute of Technology) | Koenig, Sven (University of Southern California) | Konidaris, George (Massachusetts Institute of Technology,) | Lang, Jérôme ((Université ParisDauphine) | López, Carlos Linares (Universidad Carlos III de Madrid) | Magazzeni, Daniele (King's College London) | McGovern, Amy (University of Oklahoma) | Natarajan, Sriraam (Indiana University) | Sturtevant, Nathan R. (University of Denver,) | Thielscher, Michael (University New South Wales) | Yeoh, William (New Mexico State University) | Sardina, Sebastian (RMIT University) | Wagstaff, Kiri (Jet Propulsion Laboratory)
The Twenty-Ninth AAAI Conference on Artificial Intelligence, (AAAI-15) was held in January 2015 in Austin, Texas (USA) The conference program was cochaired by Sven Koenig and Blai Bonet. This report contains reflective summaries of the main conference, the robotics program, the AI and robotics workshop, the virtual agent exhibition, the what's hot track, the competition panel, the senior member track, student and outreach activities, the student abstract and poster program, the doctoral consortium, the women's mentoring event, and the demonstrations program.
This issue features expanded versions of articles selected from the 2014 AAAI Conference on Innovative Applications of Artificial Intelligence held in Quebec City, Canada. We present a selection of four articles describing deployed applications plus two more articles that discuss work on emerging applications.