This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a six-week user study (Li et al. We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. During a one-year period, the recommender system was used by more than 1100 users. We also present our system usage data and payoff, and provide an in-depth discussion of the challenges and design issues associated with developing and deploying the software command recommender system.

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. 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.

Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. 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.

Park and the Heard Museum, sports and discuss their work during its early AAAI-16 Workshop Program -- Submissions stadiums, restaurants, and shopping.

The AAAI 2015 Spring Symposium Series was held Monday through Wednesday, March 23-25, at Stanford University near Palo Alto, California. The titles of the seven symposia were Ambient Intelligence for Health and Cognitive Enhancement, Applied Computational Game Theory, Foundations of Autonomy and Its (Cyber) Threats: From Individuals to Interdependence, Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, Logical Formalizations of Commonsense Reasoning, Socio-Technical Behavior Mining: From Data to Decisions, Structured Data for Humanitarian Technologies: Perfect Fit or Overkill? and Turn-Taking and Coordination in Human-Machine Interaction.The highlights of each symposium are presented in this report.

The program also included six keynote presentations, a funding panel, a community panel, and multiple breakout sessions. The keynote presentations, given by speakers that have been working on AI for HRI for many years, focused on the larger intellectual picture of this subfield. Each speaker was asked to address, from his or her personal perspective, why HRI is an AI problem and how AI research can bring us closer to the reality of humans interacting with robots on everyday tasks. Speakers included Rodney Brooks (Rethink Robotics), Manuela Veloso (Carnegie Mellon University), Michael Goodrich (Brigham Young University), Benjamin Kuipers (University of Michigan), Maja Mataric (University of Southern California), and Brian Scassellati (Yale University).

The AAAI-15 organizing committee of about 60 researchers arranged many of the traditional AAAI events, including the Innovative Applications of Artificial Intelligence (IAAI) Conference, tutorials, workshops, the video competition, senior member summary talks (on well-developed bodies of research or important new research areas), and What's Hot talks (on research trends observed in other AIrelated conferences and, for the first time, competitions). Innovations of AAAI-15 included software and hardware demonstration programs, a virtual agent exhibition, a computer-game showcase, a funding information session with program directors from different funding agencies, and Blue Sky Idea talks (on visions intended to stimulate new directions in AI research) with awards funded by the CRA Computing Community Consortium. Seven invited talks surveyed AI research in academia and industry and its impact on society. Attendees kept track of the program through a smartphone app as well as social media channels.

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.

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.

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.