bluuebunny/crossref_metadata_2025 huggingface dataset:
Emerging Applications for Intelligent Diabetes Management
Authors: Marling Cindy , Wiley Matthew , Bunescu Razvan , Shubrook Jay , Schwartz Frank
Published: July 2017

Diabetes management is a difficult task for patients, who must monitor and control their blood glucose levels in order to avoid serious diabetic complications. It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task: (1) case‐based decision support for diabetes management; (2) machine‐learning classification of blood glucose plots; and (3) support vector regression for blood glucose prediction. The first application provides decision support by detecting blood glucose control problems and recommending therapeutic adjustments to correct them. The second provides an automated screen for excessive glycemic variability. The third aims to build a hypoglycemia predictor that could alert patients to dangerously low blood glucose levels in time to take preventive action. All are products of the 4 Diabetes Support System project, which uses AI to promote the health and well‐being of people with type 1 diabetes. These emerging applications could potentially benefit 20 million patients who are at risk for devastating complications, thereby improving quality of life and reducing health‐care cost expenditures.
An Overview of Recent Application Trends at the AAMAS Conference: Security, Sustainability, and Safety
Authors: Jain Manish , An Bo , Tambe Milind
Published: July 2017

A key feature of the AAMAS conference is its emphasis on ties to real‐world applications. The focus of this article is to provide a broad overview of application‐focused papers published at the AAMAS 2010 and 2011 conferences. More specifically, recent applications at AAMAS could be broadly categorized as belonging to research areas of security, sustainability and safety. We outline the domains of applications, key research thrusts underlying each such application area, and emerging trends.
I Have a Robot, and I'm Not Afraid to Use It!
Authors: Kaminka Gal A.
Published: July 2017

Robots (and roboticists) increasingly appear at the Autonomous Agents and Multiagent Systems (AAMAS) conferences because the community uses robots both to inspire AAMAS research as well as to conduct it. In this article, I submit that the growing success of robotics at AAMAS is due not only to the nurturing efforts of the AAMAS community, but mainly to the increasing recognition of an important, deeper, truth: it is scientifically useful to roboticists and agent researchers to think of robots as agents.
Introduction to the Special Issue: Ten Years of Autonomous Agents and Multiagent Systems
Authors: Sonenberg Liz , Stone Peter , Tumer Kagan , Yolum Pinar
Published: July 2017

The articles in this special issue have been specifically commissioned to provide a snapshot of current activity in the autonomous agents and multiagent systems communities.
Logics for Multiagent Systems
Authors: van der Hoek Wiebe , Wooldridge Michael
Published: July 2017

We present a brief survey of logics for reasoning about multiagent systems. We focus on two paradigms: logics for cognitive models of agency, and logics used to model the strategic structure of a multiagent system.
AI@NICTA
Authors: Barnes Nick , Baumgartner Peter , Caetano Tiberio , Durrant‐Whyte Hugh , Klein Gerwin , Sanderson Penelope , Sattar Abdul , Stuckey Peter , Thiebaux Sylvie , Van Hentenryck Pascal , Walsh Toby
Published: July 2017

NICTA is Australia's Information and Communications Technology (ICT) Centre of Excellence. It is the largest organization in Australia dedicated to ICT research. While it has close links with local universities, it is in fact an independent but not‐for‐profit company in the business of doing research, commercializing that research and training Ph.D. students to do that research. Much of the work taking place at NICTA involves various topics in artificial intelligence. In this article, we survey some of the AI work being undertaken at NICTA.
McCarthy as Scientist and Engineer, with Personal Recollections
Authors: Feigenbaum Edward
Published: July 2017

John McCarthy, professor emeritus of computer science at Stanford University, died on October 24, 2011. McCarthy, a past president of AAAI and an AAAI Fellow, helped design the foundation of today's internet‐based computing and is widely credited with coining the term, artificial intelligence. This remembrance by Edward Feigenbaum, also a past president of AAAI and a professor emeritus of computer science at Stanford University, was delivered at the celebration of John McCarthy's accomplishments, held at Stanford on 25 March 2012.

– AI Magazine
The Answer Set Programming Competition
Authors: Calimeri Francesco , Ianni Giovambattista , Krennwallner Thomas , Ricca Francesco
Published: July 2017

The Answer Set Programming (ASP) Competition is a biannual event for evaluating declarative knowledge representation systems on hard and demanding AI problems. The competition consists of two main tracks: the ASP system track and the model and solve track. The traditional system track compares dedicated answer set solvers on ASP benchmarks, while the model and solve track invites any researcher and developer of declarative knowledge representation systems to participate in an open challenge for solving sophisticated AI problems with their tools of choice. This article provides an overview of the ASP Competition series, reviews its origins and history, giving insights on organizing and running such an elaborate event, and briefly discusses the lessons learned so far.
The Eighth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Authors: Riedl Mark , Sukthankar Gita , Jhala Arnav , Zhu Jichen , Ontañón Santiago , Buro Michael , Churchill David
Published: July 2017

The Eighth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) was held October 8–12, 2012, at Stanford University in Palo Alto, California. The conference included a research and industry track as well as a demonstration program. The conference featured 16 technical papers, 16 posters, and one demonstration, along with invited speakers, the StarCraft AI Competition, a newly introduced Doctoral Consortium, and five workshops. This report summarizes the activities of the conference.
RoboCup Rescue Robot and Simulation Leagues
Authors: Akin H. Levent , Ito Nobuhiro , Jacoff Adam , Kleiner Alexander , Pellenz Johannes , Visser Arnoud
Published: July 2017

The RoboCup Rescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deploying robots after real disasters (for example, the Fukushima Daiichi nuclear disaster). This article provides an overview of these competitions and highlights the state of the art and the lessons learned.
Reports of the 2012 AIIDE Workshops
Authors: Bown Oliver , Eigenfeldt Arne , Hodhod Rania , Pasquier Philippe , Swanson Reid , Ware Stephen G. , Zhu Jichen
Published: July 2017

The 2012 AIIDE Conference included four workshops: Artificial Intelligence in Adversarial Real‐Time Games, Human Computation in Digital Entertainment and AI for Serious Games, Intelligent Narrative Technologies, and Musican Metacreation. The workshops took place October 8–9, 2012, at Stanford University. This report contains summaries of the activities of those four workshops.
A Virtual Archive for the History of AI
Authors: Buchanan Bruce G. , Eckroth Joshua , Smith Reid G.
Published: July 2017

Publications that have influenced the growth of artificial intelligence are often difficult to obtain. We first collected titles of several thousand publications from many well‐known sources and then selected about 2,000 titles considered to be especially influential. We have identified, and in a few cases created, online versions of about half of these “classics in AI.” Searchable text of the documents enables additional analysis of trends and influences. Integration into the rest of the AITopics information portal contextualizes the classic publications.
Artificial Intelligence on Mobile Devices
Authors: Yang Qiang , Zhao Feng
Published: July 2017

This issue of AI Magazine contains several guest‐edited articles devoted to some exemplary works of AI on mobile devices. It includes four works that range from mobile activity recognition and air‐quality detection to machine translation and image compression
Thinking Fast and Slow: An Approach to Energy‐Efficient Human Activity Recognition on Mobile Devices
Authors: Jiang Yifei , Li Du , Lv Qin
Published: July 2017

According to Daniel Kahneman, there are two systems that drive the human decision‐making process: The intuitive system that performs the fast thinking, and the deliberative system that does more logical and slower thinking. Inspired by this model, we propose a framework for implementing human activity recognition on mobile devices. In this area, the mobile app is usually always on and the general challenge is how to balance accuracy and energy consumption. However, among existing approaches, those based on cellular IDs consume little power but are less accurate; those based on GPS/Wi‐Fi sampling are accurate often at the costs of battery drainage; moreover, previous methods in general do not improve over time. To address these challenges, our framework consists of two modes: In the deliberation mode, the system learns cell ID patterns that are trained by existing GPS‐/Wi‐Fi‐based methods; in the intuition mode, only the learned cell ID patterns are used for activity recognition, which is both accurate and energy efficient; system parameters are learned to control the transition from deliberation to intuition, when sufficient confidence is gained, and the transition from intuition to deliberation, when more training is needed. For the scope of this paper, we first elaborate our framework in a subproblem in activity recognition, trip detection, which recognizes significant places and trips between them. For evaluation, we collected real‐life traces of six participants over five months. Our experiments demonstrated consistent results across different participants in terms of accuracy and energy efficiency and, more importantly, its fast improvement on energy efficiency over time due to regularities in human daily activities.
The Annual Computer Poker Competition
Authors: Bard Nolan , Hawkin John , Rubin Jonathan , Zinkevich Martin
Published: July 2017

Now entering its eighth year, the Annual Computer Poker Competition (ACPC) is the premier event within the field of computer poker. With both academic and nonacademic competitors from around the world, the competition provides an open and international venue for benchmarking computer poker agents. We describe the competition's origins and evolution, current events, and winning techniques
The International General Game Playing Competition
Authors: Genesereth Michael , Björnsson Yngvi
Published: July 2017

Games have played a prominent role as a test bed for advancements in the field of artificial intelligence ever since its foundation over half a century ago, resulting in highly specialized world‐class game‐playing systems being developed for various games. The establishment of the International General Game Playing Competition in 2005, however, resulted in a renewed interest in more general problem‐solving approaches to game playing. In general game playing (GGP) the goal is to create game‐playing systems that autonomously learn how to play a wide variety of games skillfully, given only the descriptions of the game rules. In this paper we review the history of the competition, discuss progress made so far, and list outstanding research challenges.
The Mario AI Championship 2009–2012
Authors: Togelius Julian , Shaker Noor , Karakovskiy Sergey , Yannakakis Georgios N.
Published: July 2017

We give a brief overview of the Mario AI Championship, a series of competitions based on an open source clone of the seminal platform game Super Mario Bros. The competition has four tracks. The Gameplay and Learning tracks resemble traditional reinforcement learning competitions, the Level‐Generation track focuses on the generation of entertaining game levels, and the Turing Test track focuses on humanlike game‐playing behavior. We also outline some lessons learned from the competition and its future. The article is written by the four organizers of the competition.
Reports of the 2013 AAAI Spring Symposium Series
Authors: Markman Vita , Stojanov Georgi , Indurkhya Bipin , Kido Takashi , Takadama Keiki , Konidaris George , Eaton Eric , Matsumura Naohiro , Fruchter Renate , Sofge Don , Lawless William F. , Madani Omid , Sukthankar Rahul
Published: July 2017

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25–27, 2013. The titles of the eight symposia were Analyzing Microtext; Creativity and (Early) Cognitive Development; Data‐Driven Wellness: From Self‐Tracking to Behavior Change; Designing Intelligent Robots: Reintegrating AI II; Lifelong Machine Learning; Shikakeology: Designing Triggers for Behavior Change; Trust and Autonomous Systems; and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.
The AAAI‐13 Conference Workshops
Authors: Agrawal Vikas , Archibald Christopher , Bhatt Mehul , Bui Hung , Cook Diane J. , Cortés Juan , Geib Christopher , Gogate Vibhav , Guesgen Hans W. , Jannach Dietmar , Johanson Michael , Kersting Kristian , Konidaris George , Kotthoff Lars , Michalowski Martin , Natarajan Sriraam , O'Sullivan Barry , Pickett Marc , Podobnik Vedran , Poole David , Shastri Lokendra , Shehu Amarda , Sukthankar Gita
Published: July 2017

The AAAI‐13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013, at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context‐Aware System Architectures (WS‐13‐05); Artificial Intelligence and Robotics Methods in Computational Biology (WS‐13‐06); Combining Constraint Solving with Mining and Learning (WS‐13‐07); Computer Poker and Imperfect Information (WS‐13‐08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS‐13‐09); Intelligent Robotic Systems (WS‐13‐10); Intelligent Techniques for Web Personalization and Recommendation (WS‐13‐11); Learning Rich Representations from Low‐Level Sensors (WS‐13‐12); Plan, Activity, and Intent Recognition (WS‐13‐13); Space, Time, and Ambient Intelligence (WS‐13‐14); Trading Agent Design and Analysis (WS‐13‐15); and Statistical Relational Artificial Intelligence (WS‐13‐16).
Innovative Applications of Artificial Intelligence 2013
Authors: Muñoz‐Avila Héctor , Stracuzzi David
Published: July 2017

This issue of AI Magazine features expanded versions of articles that discuss innovative applications from the 2013 AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI‐13).
Using Analogy to Cluster Hand‐Drawn Sketches for Sketch‐Based Educational Software
Authors: Chang Maria D. , Forbus Kenneth D.
Published: July 2017

One of the major challenges to building intelligent educational software is determining what kinds of feedback to give learners. Useful feedback makes use of models of domain‐specific knowledge, especially models that are commonly held by potential students. To empirically determine what these models are, student data can be clustered to reveal common misconceptions or common problem‐solving strategies. This article describes how analogical retrieval and generalization can be used to cluster automatically analyzed hand‐drawn sketches incorporating both spatial and conceptual information. We use this approach to cluster a corpus of hand‐drawn student sketches to discover common answers. Common answer clusters can be used for the design of targeted feedback and for assessment.
A History of AI Research and Development in Thailand: Three Periods, Three Directions
Authors: Kawtrakul Asanee , Praneetpolgrang Prasong
Published: July 2017

Thailand, a country of 65 million people, has had an active AI community for almost three decades. With limited research funding (less than 1 percent of GDP), AI researchers have had to maintain a focus on producing concrete results. They have set clear research goals to ensure that smart or intelligent systems are developed and applied to help reduce costs, improve efficiency, and increase productivity in integrated public services.

The guest editor for this column was Eduard Hovey.
ICAIL 2013: The Fourteenth International Conference on Artificial Intelligence and Law
Authors: Verheij Bart , Francesconi Enrico , Gardner Anne
Published: July 2017

The 14th International Conference on AI and Law (ICAIL 2013) was held in Rome, Italy, June 10–14, 2013.
Computational Sustainability
Authors: Eaton Eric , Gomes Carla , Williams Brian
Published: July 2017

Computational sustainability problems, which exist in dynamic environments with high amounts of uncertainty, provide a variety of unique challenges to artificial intelligence research and the opportunity for significant impact upon our collective future. This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the next two special issue articles that will appear in AI Magazine.
Reconstructing Velocities of Migrating Birds from Weather Radar — A Case Study in Computational Sustainability
Authors: Farnsworth Andrew , Sheldon Daniel , Geevarghese Jeffrey , Irvine Jed , Van Doren Benjamin , Webb Kevin , Dietterich Thomas G. , Kelling Steve
Published: July 2017

Bird migration occurs at the largest of global scales, but monitoring such movements can be challenging. In the United States there is an operational network of weather radars providing freely accessible data for monitoring meteorological phenomena in the atmosphere. Individual radars are sensitive enough to detect birds, and can provide insight into migratory behaviors of birds at scales that are not possible using other sensors. Archived data from the WSR‐88D network of U.S. weather radars hold valuable and detailed information about the continent‐scale migratory movements of birds over the last 20 years. However, significant technical challenges must be overcome to understand this information and harness its potential for science and conservation. We describe recent work on an AI system to quantify bird migration using radar data, which is part of the larger BirdCast project to model and forecast bird migration at large scales using radar, weather, and citizen science data.
A Survey of Artificial Intelligence Research at IIIA
Authors: de Mantaras Ramon Lopez
Published: July 2017

IIIA is a public research center, belonging to the Spanish National Research Council (CSIC), dedicated to AI research. We focus our activities on a few well‐defined subdomains of artificial intelligence, positively avoiding dispersion and keeping a good balance between basic research and applications, and paying particular attention to training Ph.D. students and technology transfer. In this article, I survey some of the most relevant results we have obtained during the last 12 years.
Leveraging AI Teaching in the Cloud for AI Teaching on Campus
Authors: Fisher Douglas H.
Published: July 2017

In this column I describe my experience adapting the content and infrastructure from massive, open, online courses (MOOCs) to enhance my courses in the Department of Electrical Engineering and Computer Science at Vanderbilt University. I begin with my informal, early use of MOOC content and then move to two deliberatively designed strategies for adapting MOOCs to campus (that is, wrappers and small private online classes [SPOCs]). I describe student reactions and touch on selected policy and institutional considerations. In the never‐ending search for increasing student bang‐for‐the‐buck, I was motivated to increase the bang, rather than reduce the buck, the latter being well above my pay grade. I close with some vision for teaching on campus with resources from the cloud, notably on how it may strengthen community and collaboration in teaching.
RoboCup Soccer Leagues
Authors: Nardi Daniele , Noda Itsuki , Ribeiro Fernando , Stone Peter , von Stryk Oskar , Veloso Manuela
Published: July 2017

RoboCup was created in 1996 by a group of Japanese, American, and European artificial intelligence and robotics researchers with a formidable, visionary long‐term challenge: By 2050 a team of robot soccer players will beat the human World Cup champion team. In this article, we focus on RoboCup robot soccer, and present its five current leagues, which address complementary scientific challenges through different robot and physical setups. Full details on the status of the RoboCup soccer leagues, including league history and past results, upcoming competitions, and detailed rules and specifications are available from the league homepages and wikis.
Multirobot Coordination for Space Exploration
Authors: Yliniemi Logan , Agogino Adrian K. , Tumer Kagan
Published: July 2017

Teams of artificially intelligent planetary rovers have tremendous potential for space exploration, allowing for reduced cost, increased f1exibility, and increased reliability. However, having these multiple autonomous devices acting simultaneously leads to a problem of coordination: to achieve the best results, they should work together. This is not a simple task. Due to the large distances and harsh environments, a rover must be able to perform a wide variety of tasks with a wide variety of potential teammates in uncertain and unsafe environments. Directly coding all the necessary rules that can reliably handle all of this coordination and uncertainty is problematic. Instead, this article examines tackling this problem through the use of coordinated reinforcement learning: rather than being programmed what to do, the rovers iteratively learn through trial and error to take take actions that lead to high overall system return. To allow for coordination, yet allow each agent to learn and act independently, we employ state‐of‐the‐art reward‐shaping techniques. This article uses visualization techniques to break down complex performance indicators into an accessible form and identifies key future research directions.
Semantics‐Empowered Big Data Processing with Applications
Authors: Thirunarayan Krishnaprasad , Sheth Amit
Published: July 2017

We discuss the nature of big data and address the role of semantics in analyzing and processing big data that arises in the context of physical‐cybersocial systems. To handle volume, we advocate semantic perception that can convert low‐level observational data to higher‐level abstractions more suitable for decision making. To handle variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle velocity, we seek to use continuous semantics capability to dynamically create event‐ or situation‐specific models and recognize relevant new concepts, entities and facts. To handle veracity, we explore trust models and approaches to glean trustworthiness. These four of the five v's of big data are harnessed by the semantics‐empowered analytics to derive value to support applications transcending the physical‐cyber‐social continuum.
Reports of the Workshops Held at the Tenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE)
Authors: Barnes Tiffany , Bown Oliver , Buro Michael , Cook Michael , Eigenfeldt Arne , Muñoz‐Avila Héctor , Ontañón Santiago , Pasquier Philippe , Tomuro Noriko , Young R. Michael , Zook Alexander
Published: July 2017

The AIIDE‐14 Workshop program was held Friday and Saturday, October 3–4, 2014, at North Carolina State University in Raleigh, North Carolina. The workshop program included five workshops covering a wide range of topics. The titles of the workshops held Friday were Artificial Intelligence for Adversarial Real‐Time Games and Games and Natural Language Processing The titles of the workshops held Saturday were Diversity in Games Research, Experimental AI in Games, and Musical Metacreation. This article presents short summaries of those events.
Architectures for Activity Recognition and Context‐Aware Computing
Authors: Geib Christopher , Agrawal Vikas , Sukthankar Gita , Shastri Lokendra , Bui Hung
Published: July 2017

This editorial to the summer 2015 AI Magazine introduces the specialissue articles on architectures for activity recognition and context‐aware computing.
Parallelizing Plan Recognition
Authors: Geib Christopher W. , Swetenham Christopher E.
Published: July 2017

Modern multicore computers provide an opportunity to parallelize plan‐recognition algorithms to decrease run time. Viewing plan recognition as parsing based on a complete breadth first search, makes ELEXIR (engine for lexicalized intent recognition) (Geib 2009, Geib and Goldman 2011) particularly suited for parallelization. This article documents the extension of ELEXIR to utilize such modern computing platforms. We will discuss multiple possible algorithms for distributing work between parallel threads and the associated performance wins. We will show that the best of these algorithms provides close to linear speedup (up to a maximum number of processors), and that features of the problem domain have an impact on the achieved speedup.
Reducing Friction for Knowledge Workers with Task Context
Authors: Kersten Mik , Murphy Gail C.
Published: July 2017

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.
A General Context‐Aware Framework for Improved Human‐System Interactions
Authors: Pfautz Stacy Lovell , Ganberg Gabriel , Fouse Adam , Schurr Nathan
Published: July 2017

For humans and automation to collaborate and perform tasks effectively, 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 more intuitively and naturally with automation 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 toward 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.
A Semantic Infrastructure for Personalizable Context‐Aware Environments
Authors: Scerri Simon , Debattista Jeremy , Attard Judie , Rivera Ismael
Published: July 2017

Although a number of initiatives provide personalized context‐aware guidance for niche use cases, a standard framework for context awareness remains lacking. This article explains how semantic technology has been exploited to generate a centralized repository of personal activity context. This data drives advanced features such as personal situation recognition and customizable rules for the context‐sensitive management of personal devices and data sharing. As a proof of concept, we demonstrate how an innovative context‐aware system has successfully adopted such an infrastructure.
Report on the Twenty‐Second International Conference on Case‐Based Reasoning
Authors: Bridge Derek , Lamontagne Luc , Plaza Enric
Published: July 2017

The Twenty‐Second International Conference on Case‐Based Reasoning was held from September 29 to October 1, 2014. This report summarizes the conference highlights.
A Deployed People‐to‐People Recommender System in Online Dating
Authors: Wobcke Wayne , Krzywicki Alfred , Kim Yang Sok , Cai Xiongcai , Bain Michael , Compton Paul , Mahidadia Ashesh
Published: July 2017

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.
Deploying CommunityCommands: A Software Command Recommender System Case Study
Authors: Li Wei , Matejka Justin , Grossman Tovi , Fitzmaurice George
Published: July 2017

In 2009 we presented the idea of using collaborative filtering within a complex software application to help users learn new and relevant commands (Matejka et al. 2009). 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. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. CommunityCommands was a publically available plug‐in for Autodesk's flagship software application AutoCAD. During a one‐year period, the recommender system was used by more than 1100 users. In this article, we discuss how our practical system architecture was designed to leverage Autodesk's existing customer involvement program (CIP) data to deliver in‐product contextual recommendations to end 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. Our work sets important groundwork for the future development of recommender systems within the domain of end user software learning assistance.
Advice Provision for Energy Saving in an Automobile Climate‐Control System
Authors: Azaria Amos , Rosenfeld Ariel , Kraus Sarit , Goldman Claudia V. , Tsimhoni Omer
Published: July 2017

Reducing energy consumption of climate‐control systems is important in order to reduce the 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 drivers with advice that 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 have built two models, one for assessing the energy consumption of the climate‐control system as a function of the system's settings, and the other for modeling the 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 that 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.
Reports on the 2014 AAAI Fall Symposium Series
Authors: Cohen Adam B. , Chernova Sonia , Giordano James , Guerin Frank , Hauser Kris , Indurkhya Bipin , Leonetti Matteo , Medsker Larry , Michalowski Martin , Sonntag Daniel , Stojanov Georgi , Tecuci Dan G. , Thomaz Andrea , Veale Tony , Waltinger Ulli
Published: July 2017

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.
Cognition as a Service: An Industry Perspective
Authors: Spohrer Jim , Banavar Guruduth
Published: July 2017

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 reimage work practices, augmenting and scaling expertise to transform professions, industries, and regions.
Fifteenth International Conference on Artificial Intelligence and Law (ICAIL 2015)
Authors: Atkinson Katie , Conrad Jack G. , Gardner Anne , Sichelman Ted
Published: July 2017

The 15th International Conference on AI and Law (ICAIL 2015) was held in San Diego, California, USA, June 8–12, 2015, at the University of San Diego, at the Kroc Institute, under the auspices of the International Association for Artificial Intelligence and Law (IAAIL), an organization devoted to promoting research and development in the field of AI and law with members throughout the world. The conference is held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and with ACM SIGAI (the Special Interest Group on Artificial Intelligence of the Association for Computing Machinery).
How to Write Science Questions That Are Easy for People and Hard for Computers
Authors: Davis Ernest
Published: July 2017

As a challenge problem for AI systems, I propose the use of hand‐constructed multiple‐choice tests, with problems that are easy for people but hard for computers. Specifically, I discuss techniques for constructing such problems at the level of a fourth‐grade child and at the level of a high school student. For the fourth‐grade‐level questions, I argue that questions that require the understanding of time, of impossible or pointless scenarios, of causality, of the human body, or of sets of objects, and questions that require combining facts or require simple inductive arguments of indeterminate length can be chosen to be easy for people, and are likely to be hard for AI programs, in the current state of the art. For the high school level, I argue that questions that relate the formal science to the realia of laboratory experiments or of real‐world observations are likely to be easy for people and hard for AI programs. I argue that these are more useful benchmarks than existing standardized tests such as the SATs or New York Regents tests. Since the questions in standardized tests are designed to be hard for people, they often leave many aspects of what is hard for computers but easy for people untested.
The Social‐Emotional Turing Challenge
Authors: Jarrold William , Yeh Peter Z.
Published: July 2017

Social‐emotional intelligence is an essential part of being a competent human and is thus required for human‐level AI. When considering alternatives to the Turing test it is therefore a capacity that is important to test. We characterize this capacity as affective theory of mind and describe some unique challenges associated with its interpretive or generative nature. Mindful of these challenges we describe a five‐step method along with preliminary investigations into its application. We also describe certain characteristics of the approach such as its incremental nature, and countermeasures that make it difficult to game or cheat.
I‐athlon: Toward a Multidimensional Turing Test
Authors: Adams Sam S. , Banavar Guruduth , Campbell Murray
Published: July 2017

While the Turing test is a well‐known method for evaluating machine intelligence, it has a number of drawbacks that make it problematic as a rigorous and practical test for assessing progress in general‐purpose AI. For example, the Turing test is deception based, subjectively evaluated, and narrowly focused on language use. We suggest that a test would benefit from including the following requirements: focus on rational behavior, test several dimensions of intelligence, automate as much as possible, score as objectively as possible, and allow incremental progress to be measured. In this article we propose a methodology for designing a test that consists of a series of events, analogous to the Olympic Decathlon, which complies with these requirements. The approach, which we call the I‐athlon, is intended ultimately to enable the community to evaluate progress toward machine intelligence in a practical and repeatable way.
Toward a Comprehension Challenge, Using Crowdsourcing as a Tool
Authors: Paritosh Praveen , Marcus Gary
Published: July 2017

Human readers comprehend vastly more, and in vastly different ways, than any existing comprehension test would suggest. An ideal comprehension test for a story should cover the full range of questions and answers that humans would expect other humans to reasonably learn or infer from a given story. As a step toward these goals we propose a novel test, the crowdsourced comprehension challenge (C3), which is constructed by repeated runs of a three‐person game, the Iterative Crowdsourced Comprehension Game (ICCG). ICCG uses structured crowdsourcing to comprehensively generate relevant questions and supported answers for arbitrary stories, whether fiction or nonfiction, presented across a variety of media such as videos, podcasts, and still images.
Activity Planning for a Lunar Orbital Mission
Authors: Bresina John L.
Published: July 2017

This article describes a challenging, real‐world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer). I present the approach taken to reduce the complexity of the activity‐planning task in order to perform it effectively under the time pressures imposed by the mission requirements. One key aspect of this approach is the design of the activity‐planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed‐initiative system developed for this task, called LASS (LADEE Activity Scheduling System). The primary challenge for LASS was representing and managing the science constraints that were tied to key points in the spacecraft's orbit, given their dynamic nature due to the continually updated orbit determination solution.
Amazon Picking Challenge 2015
Authors: Wurman Peter R. , Romano Joseph M.
Published: July 2017

At the Amazon Picking Challenge, 26 teams competed on their ability to pick items out of warehouse shelves. While the first year was largely focused on basic competencies, there are clear ways AI techniques can help make these systems more capable and robust.
The AIIDE 2015 Workshop Program
Authors: Barot Camille , Buro Michael , Cook Michael , Eladhari Mirjam , Johansson Magnus , Li Boyang , Liapis Antonios , McCoy Josh , Ontañón Santiago , Rowe Jonathan , Tomai Emmett , Verhagen Harko , Zook Alexander
Published: July 2017

The workshop program at the 11th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment was held November 14–15, 2015, at the University of California, Santa Cruz, USA. The program included four workshops (one of which was a joint workshop): Artificial Intelligence in Adversarial Real‐Time Games, Experimental AI in Games, Intelligent Narrative Technologies and Social Believability in Games, and Player Modeling. This article contains the reports of three of the four workshops.
The 2015 AAAI Fall Symposium Series Reports
Authors: Ahmed Nisar , Bello Paul , Bringsjord Selmer , Clark Micah , Hayes Bradley , Kolobov Andrey , Miller Christopher , Oliehoek Frans , Stein Frank , Spaan Matthijs
Published: July 2017

This article contains the reports of the AI for Human‐Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter‐Deceptive Machines, Self‐Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents symposia, which were held November 12–14, 2015 in Arlington, Virginia.
The Israeli AI Community
Authors: Felner Ariel
Published: July 2017

This column provides an encounter with the artificial intelligence research community in the state of Israel. The first section introduces this community and its special attributes. The second section provides an overview of some recent research projects done in Israel. The author serves as the chair of the Israeli Association for Artificial Intelligence.
Report on the 29th International Florida Artificial Intelligence Research Society Conference (FLAIRS‐29)
Authors: Markov Zdravko , Russell Ingrid , Eberle Bill
Published: July 2017

The 29th International Florida Artificial Intelligence Research Society Conference (FLAIRS‐29) was held May 16–18, 2016, at the Hilton Key Largo Resort in Key Largo, Florida, USA. The conference events included invited speakers, special tracks, and presentations of papers, posters, and awards. The conference chair was Bill Eberle from Tennessee Technological University. The program cochairs were Zdravko Markov from Central Connecticut State University and Ingrid Russell from the University of Hartford. The special tracks were coordinated by Vasile Rus from University of Memphis.
Reports of the Workshops Held at the 2016 International AAAI Conference on Web and Social Media
Authors: An Jisun , Crandall David J. , Fedorov Roman , Fiesler Casey , Giglietto Fabio , Heravi Bahareh , Pater Jessica , Pelechrinis Konstantinos , Quercia Daniele , Weller Katrin , Zubiaga Arkaitz
Published: July 2017

The workshop program of the Association for the Advancement of Artificial Intelligence's International Conference on Web and Social Media (AAAI‐16) was held in Cologne, Germany. There were eight workshops in the program: CityLab, Challenges and Futures for Ethical Social Media Research, Social Media and Demographic Research, Wiki, #Fail: Things That Didn't Work Out in Social Media Research — And What We Can Learn from Them, News and Public Opinion, Social Media in the Newsroom, and Social Web for Environmental and Ecological Monitoring. Workshops were held on the first day of the conference, Tuesday, May 17, 2016. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers, and users on topics of current interest. Of the eight workshops held at the conference; organizers from only five included papers in the AAAI Technical Reports series, and organizers from six workshops submitted reports.
Innovative Applications of Artificial Intelligence 2016
Authors: Yeh Peter Z. , Crawford James
Published: July 2017

This issue features expanded versions of articles selected from the 2016 AAAI Conference on Innovative Applications of Artificial Intelligence held in Phoenix, Arizona. We present a selection of three articles that describe deployed applications, two articles that discuss work on emerging applications, and an article based on the 2016 Robert S. Engelmore Memorial Lecture.
Deploying nEmesis: Preventing Foodborne Illness by Data Mining Social Media
Authors: Sadilek Adam , Kautz Henry , DiPrete Lauren , Labus Brian , Portman Eric , Teitel Jack , Silenzio Vincent
Published: July 2017

Foodborne illness afflicts 48 million people annually in the US alone. More than 128,000 are hospitalized and 3000 die from the infection. While preventable with proper food safety practices, the traditional restaurant inspection process has limited impact given the predictability and low frequency of inspections, and the dynamic nature of the kitchen environment. Despite this reality, the inspection process has remained largely unchanged for decades. CDC has even identified food safety as one of seven “winnable battles”; however, progress to date has been limited. In this work, we demonstrate significant improvements in food safety by marrying AI and the standard inspection process. We apply machine learning to Twitter data, develop a system that automatically detects venues likely to pose a public health hazard, and demonstrate its efficacy in the Las Vegas metropolitan area in a double‐blind experiment conducted over three months in collaboration with Nevada's health department. By contrast, previous research in this domain has been limited to indirect correlative validation using only aggregate statistics. We show that the adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent more than 9000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.