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.
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.
Azaria, Amos (Carnegie Mellon University) | Rosenfeld, Ariel (Bar-Ilan University) | Kraus, Sarit (Bar-Ilan University) | Goldman, Claudia V. (Advanced Technical Center, General Motors Israel) | Tsimhoni, Omer (General Motors Warren Technical Center)
Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. The need to save energy becomes even greater when considering an electric car, since heavy use of the climate control system may exhaust the battery. In this article we consider a method for an automated agent to provide advice to drivers which will motivate them to reduce the energy consumption of their climate control unit. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system’s settings, and the other, models human comfort level as a function of the climate control system’s settings. Using these models, the agent provides advice to the driver considering how to set the climate control system. The agent advises settings which try to preserve a high level of comfort while consuming as little energy as possible. We empirically show that drivers equipped with our agent which provides them with advice significantly save energy as compared to drivers not equipped with our agent.
An important research topic in artificial intelligence is automatic sensing and inferencing of contextual information, which is used to build computer models of the user’s activity. One approach to build such activity-aware systems is the notion of activity-based computing (ABC). ABC is a computing paradigm that has been applied in personal information management applications as well as in ubiquitous, multidevice, and interactive surface computing. ABC has emerged as a response to the traditional application- and file-centered computing paradigm, which is oblivious to a notion of a user’s activity context spanning heterogeneous devices, multiple applications, services, and information sources. In this article, we present ABC as an approach to contextualize information, and present our research into designing activity-centric computing technologies.
For humans and automation to effectively collaborate and perform tasks, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. Our goal is to develop an approach that enables humans to interact with automation more intuitively and naturally that is reusable across domains by modeling context and algorithms at a higher-level of abstraction. We first provide an operational definition of context and discuss challenges and opportunities for exploiting context. We then describe our current work towards a general platform that supports developing context-aware applications in a variety of domains. We then explore an example use case illustrating how our framework can facilitate personalized collaboration within an information management and decision support tool. Future work includes evaluating our framework.
Zavala, Laura (Medgar Evers College, City University of New York) | Murukannaiah, Pradeep K. (North Carolina State University) | Poosamani, Nithyananthan (North Carolina State University.) | Finin, Tim (University of Maryland, Baltimore County) | Joshi, Anupam (University of Maryland, Baltimore County) | Rhee, Injong (North Carolina State University, Raleigh) | Singh, Munindar P. (North Carolina State University)
The Platys project focuses on developing a high-level, semantic notion of location called place. A place, unlike a geospatial position, derives its meaning from a user’s actions and interactions in addition to the physical location where they occur. Our aim is to enable the construction of a large variety of applications that take advantage of place to render relevant content and functionality and thus, improve user experience. We consider elements of context that are particularly related to mobile computing. The main problems we have addressed to realize our place-oriented mobile computing vision, are representing places, recognizing places, engineering place-aware applications. We describe the approaches we have developed for addressing these problems and related subproblems. A key element of our work is the use of collaborative information sharing where users’ devices share and integrate knowledge about places. Our place ontology facilitates such collaboration. Declarative privacy policies allow users to specify contextual features under which they prefer to share or not share their information.
In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the Twenty-Second International Conference on Case-Based Reasoning (ICCBR), the premier international meeting on research and applications in case-based reasoning (CBR), was held from Monday September 29 to Wednesday October 1, 2014, in Cork, Ireland. ICCBR is the annual meeting of the CBR community and the leading conference on this topic. Started in 1993 as the European Conference on CBR and 1995 as ICCBR, the two conferences alternated biennially until their merger in 2010.
Knowledge workers perform work on many tasks per day and often switch between tasks. When performing work on a task, a knowledge worker must typically search, navigate and dig through file systems, documents and emails, all of which introduce friction into the flow of work. This friction can be reduced, and productivity improved, by capturing and modeling the context of a knowledge worker’s task based on how the knowledge worker interacts with an information space. Captured task contexts can be used to facilitate switching between tasks, to focus a user interface on just the information needed by a task and to recommend potentially other useful information. We report on the use of task contexts and the effect of context on productivity for a particular kind of knowledge worker, software developers. We also report on qualitative findings of the use of task contexts by a more general population of knowledge workers.
Albrecht, Stefano V. (University of Edinburgh) | Beck, J. Christopher (University of Toronto) | Buckeridge, David L. (McGill University) | Botea, Adi (IBM Research, Dublin) | Caragea, Cornelia (University of North Texas) | Chi, Chi-hung (Commonwealth Scientific and Industrial Research Organisation) | Damoulas, Theodoros (New York University) | Dilkina, Bistra (Georgia Institute of Technology) | Eaton, Eric (University of Pennsylvania) | Fazli, Pooyan (Carnegie Mellon University) | Ganzfried, Sam (Carnegie Mellon University) | Giles, C. Lee (Pennsylvania State University) | Guillet, Sébastian (Université du Québec) | Holte, Robert (University of Alberta) | Hutter, Frank (University of Freiburg) | Koch, Thorsten (TU Berlin) | Leonetti, Matteo (University of Texas at Austin) | Lindauer, Marius (University of Freiburg) | Machado, Marlos C. (University of Alberta) | Malitsky, Yui (IBM Research) | Marcus, Gary (New York University) | Meijer, Sebastiaan (KTH Royal Institute of Technology) | Rossi, Francesca (University of Padova, Italy) | Shaban-Nejad, Arash (University of California, Berkeley) | Thiebaux, Sylvie (Australian National University) | Veloso, Manuela (Carnegie Mellon University) | Walsh, Toby (NICTA) | Wang, Can (Commonwealth Scientific and Industrial Research Organisation) | Zhang, Jie (Nanyang Technological University) | Zheng, Yu (Microsoft Research)
AAAI's 2015 Workshop Program was held Sunday and Monday, January 25–26, 2015 at the Hyatt Regency Austin Hotel in Austion, Texas, USA. The AAAI-15 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included AI and Ethics, AI for Cities, AI for Transportation: Advice, Interactivity and Actor Modeling, Algorithm Configuration, Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Beyond the Turing Test, Computational Sustainability, Computer Poker and Imperfect Information, Incentive and Trust in E-Communities, Multiagent Interaction without Prior Coordination, Planning, Search, and Optimization, Scholarly Big Data: AI Perspectives, Challenges, and Ideas, Trajectory-Based Behaviour Analytics, World Wide Web and Public Health Intelligence, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, and Learning for General Competency in Video Games.