bluuebunny/crossref_metadata_2025 huggingface dataset:
FLAMBAGEM VERTICAL EM TUBULAÇÕES DE DISTRIBUIÇÃO DE GÁS NATURAL
Authors: Almeida Julio Cezar
Published: July 2017

Tubulações de aço são normalmente utilizadas em sistemas de transmissão e também em redes de distribuição de gás natural.  Estas estruturas, quando em operação, são submetidas a cargas externas, que quando atingem valores críticos, podem ocasionar falhas, até mesmo catastróficas.  Os acidentes, nestas circunstâncias, podem resultar em prejuízos muito elevados, danos ambientais e até mesmo problemas de imagem para a empresa envolvida. No caso específico das redes de transmissão e distribuição de gás natural, o projeto e construção seguem rígidos padrões, entre as quais podem se destacar a norma NBR-12712.  A combinação de altas temperaturas e pressões internas aos quais os dutos podem ser submetidos causa expansões, as quais são restringidas pela ação solo, gerando assim forças axiais de compressão. Neste contexto, o tubo pode entrar em colapso através do fenômeno conhecido como flambagem vertical ou flambagem horizontal. No entanto, como essas tubulações são normalmente enterradas, elimina-se a preocupação com a flambagem horizontal permanecendo, entretanto, uma maior preocupação com a flambagem vertical assim existente.  O objetivo do presente trabalho é determinar, de forma conceitual, as temperaturas limites que uma malha de distribuição de gás natural pode ser submetida, sem apresentar problemas de flambagem vertical.  Este estudo é realizado com os dados operacionais da rede de distribuição de gás natural da COMPAGAS - empresa de distribuição de gás natural ou responsável no estado do Paraná.  Para tanto se utilizou equações matemáticas simplificadas para o estudo da flambagem em dutos verticais aquecidos, seguindo o modelo proposto e adaptado por Hobbs.
PRODUÇÃO DE BIODIESEL POR TRANSESTERIFICAÇÃO UTILIZANDO CATALISADOR HETEROGÊNEO (KOH/Al2O3)
Authors: Gondim Amanda Duarte , Santos Maria Fernanda Vicente , Oliveira Tatiane Potiguara , Peixoto Camila Gisele Damasceno , Batista Alyxandra Carla Medeiros , Fernandes Jr. Valter José
Published: July 2017

O biodiesel consiste de um combustível biodegradável derivado de óleos vegetais e gorduras animais, formado por ésteres de ácidos graxos, ésteres de alquila (metila, etila ou propila) de ácidos carboxílicos de cadeia longa. O processo convencional para sua produção ocorre pela transesterificação de óleos vegetais com álcool na presença de um catalisador. O catalisador mais utilizado no processo de transesterificação é o catalisador homogêneo, KOH, que apresenta grande dificuldade de recuperação do mesmo, portanto foi estudado neste trabalho um catalisador heterogêneo (KOH/Al2O3). Deste modo, este trabalho tem como objetivo e obter o catalisador e caracterizar, além de aplicar o catalisador na reação de transesterificação de óleo de algodão em busca das melhores condições de reações. Os produtos obtidos foram avaliados por densidade, viscosidade, análise térmica (TG/DTG) e espectroscopia na região do infravermelho por transformada de Fourier (FTIR). Para a confirmação da obtenção do catalisador foi utilizada a técnica como por difratograma de raio X (DRX),  microscópico eletrônico de varredura (MEV-EDS), FTIR e TG/DTG . Os biodiesel obtido através da reação de transesterificação na presença de KOH/Al2O3 apresentaram resultados de densidade e viscosidade de acordo com Resolução ANP 45/2014. O biodiesel que foi obtido com um tempo reacional de 5h apresentou maior percentual de conversão (94,5%).
INFLUÊNCIA DO TEMPO DE REAÇÃO NA PRODUÇÃO DE BIODIESEL VIA CATÁLISE HETEROGÊNEA
Authors: Cintra Jackeline Sousa Aleixo , Portela Mayara Nunes , Silvany Tila Cachoeira de Carvalho , Pedroza Graziella Aguiar Gomes , Santos Luiz Carlos Lobato dos , Lobato Ana Katerine de Carvalho Lima
Published: July 2017

O biodiesel apresenta muitas vantagens em comparação ao diesel por ser de origem renovável, biodegradável, podendo ser empregado de forma ambientalmente sustentável. A sua produção é comumente realizada através da reação de transesterificação por catálise homogênea. Entretanto,os catalisadoresheterogêneos têm sidoamplamente estudados por serem eficientes e passíveis de reciclagem, além de facilitar a purificação do biodiesel e minimizar a geração de efluentes. Neste trabalho, o óxido de cálcio (CaO) foi escolhido como catalisador básico para a reação de transesterificação por apresentar maior atividade, condições suaves de reação, menor custo e baixa toxidade, quando comparado com outros catalisadores comerciais.Foram avaliados os efeitos do tempo de reação no rendimento do biodiesel produzido com óleo de soja via rota metílica num processo em batelada, fixando a razão molar metanol/óleo em 12:1, temperatura em 65°C e concentração de catalisador em 8%empeso do óleo e variando o tempo do processo em 1h, 1,5h e 2h. Verificou-se que os efeitos relativos à variação do tempo de reação utilizada na síntese debiodieselinfluenciamdiretamente no processo.
ROTA DE ARTE RUPESTRE DO NOROESTE PORTUGUÊS. UM PROJETO PARA O DESENVOLVIMENTO DE UMA PRÁTICA TURÍSTICA SUSTENTÁVEL
Authors: Bettencourt Ana , Sampaio Hugo Aluai , Cardoso Daniela , Sá Sofia , Rodrigues Alda
Published: July 2017

A Rota de Arte Rupestre do Noroeste é um projeto que pretende explorar as potencialidades da arte rupestre do NW de Portugal enquanto recurso  passível de desenvolvimento de uma prática turística sustentável.O NW português é uma área rica em arte rupestre de ar livre de cronologia pós-paleolítica, em especial do estilo designado de Arte Atlântica, específico da área. Além do seu inegável valor científico, o caráter estratégico deste recurso patrimonial deveria integrar programas de desenvolvimento e de organização do território.Para que tal se concretize há que colmatar carências ao nível da investigação, tais como a compilação e sistematização dos dados que promovam o avanço do conhecimento científico e, simultaneamente, propiciem discursos atrativos para o público em geral.O projeto tenciona aprofundar a investigação da arte rupestre segundo perspetivas arqueológicas e antropológicas, contribuir para salvaguardar este património arqueológico e promover a sua valorização na fachada ocidental no NW de Portugal, por forma a que se possa criar o produto turístico sustentável “Rota de Arte Rupestre do Noroeste”. Visa, ainda, rentabilizar o património “intangível” ligado a estes recursos, os recursos humanos das parcerias envolvidas no projeto – e incrementar o desenvolvimento sustentável do território abordado.Serão divulgadas as ações já materializadas e/ou em vias de materialização, bem como os resultados atingidos para diferentes áreas do NW.
Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity
Authors: Cardoso Amílcar , Veale Tony , Wiggins Geraint A.
Published: July 2017

In this article, we survey the history of studies of computational creativity, following the development of the International Conference on Computational Creativity from its beginnings, a decade ago, in two parallel workshop series. We give a brief outline of key issues and a summary of the various different approaches taken by participants in the research field. The outlook is optimistic: a lot has been achieved in 10 years.
Reports of the AAAI 2009 Spring Symposia
Authors: Bao Jie , Bojars Uldis , Choudhury Tanzeem , Ding Li , Greaves Mark , Kapoor Ashish , Louchart Sandy , Mehta Manish , Nebel Bernhard , Nirenburg Sergei , Oates Tim , Roberts David L. , Sanfilippo Antonio , Stojanovic Nenad , Stubbs Kristen , Thomaz Andrea L. , Tsui Katherine , Woelfl Stefan
Published: July 2017

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, was pleased to present the 2009 Spring Symposium Series, held Monday through Wednesday, March 23‐25, 2009, at Stanford University. The titles of the nine symposia were Agents That Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Experimental Design for Real‐World Systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The goal of the Agents That Learn from Human Teachers symposium was to investigate how we can enable software and robotics agents to learn from real‐time interaction with an everyday human partner. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning Systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long‐term research. The Experimental Design symposium discussed the challenges of evaluating AI systems. The Human Behavior Modeling symposium explored reasoning methods for understanding various aspects of human behavior, especially in the context of designing intelligent systems that interact with humans. The Intelligent Event Processing symposium discussed the need for more AI‐based approaches in event processing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies II AAAI symposium discussed innovations, progress, and novel techniques in the research domain. The Learning by Reading and Learning to Read symposium explored two aspects of making natural language texts semantically accessible to, and processable by, machines. The Social Semantic Web symposium focused on the real‐world grand challenges in this area. Finally, the Technosocial Predictive Analytics symposium explored new methods for anticipatory analytical thinking that provide decision advantage through the integration of human and physical models.
Mediating between AI and Highly Specialized Users
Authors: Petrelli Daniela , Dadzie Aba‐Sah , Lanfranchi Vitaveska
Published: July 2017

We report part of the design experience gained in X‐Media, a system for knowledge management and sharing. Consolidated techniques of interaction design (scenario‐based design) had to be revisited to capture the richness and complexity of intelligent interactive systems. We show that the design of intelligent systems requires methodologies (faceted scenarios) that support the investigation of intelligent features and usability factors simultaneously. Interaction designers become mediators between intelligent technology and users and have to facilitate reciprocal understanding.
The Design and Evaluation of User Interfaces for the RADAR Learning Personal Assistant
Authors: Faulring Andrew , Mohnkern Ken , Steinfeld Aaron , Myers Brad A.
Published: July 2017

The RADAR project developed a large multiagent system with a mixed‐initiative user interface designed to help office workers cope with email overload. Most RADAR agents observe experts performing tasks and then assist other users who are performing similar tasks. The interaction design for RADAR focused on developing user interfaces that allowed the intelligent functionality to improve the user's workflow without frustrating the user when the system's suggestions were either unhelpful or simply incorrect. For example, with regard to autonomy, the RADAR agents were allowed much flexibility in selecting ways to assist the user but were restricted from taking actions that would be visible to other people. This policy ensured that the user remained in control and mitigated the negative effects of mistakes. A large evaluation of RADAR demonstrated that novice users confronted with an e‐mail overload test performed significantly better, achieving a 37 percent better overall score when assisted by RADAR. The evaluation showed that AI technologies can help users accomplish their goals.
User Interface Goals, AI Opportunities
Authors: Lieberman Henry
Published: July 2017

This is an opinion piece about the relationship between the fields of human‐computer interaction (HCI) and artificial intelligence (AI). The ultimate goal of both fields is to make user interfaces more effective and easier for people to use. But historically, researchers have disagreed about whether “intelligence” or “direct manipulation” is the better route to achieving this. There is an unjustified perception in HCI that AI is unreliable. There is an unjustified perception in AI that interfaces are merely cosmetic. This disagreement is counterproductive.

This article argues that AI's goals of intelligent interfaces would benefit enormously by the user‐centered design and testing principles of HCI. It argues that HCI's stated goals of meeting the needs of users and interacting in natural ways would be best served by application of AI. Peace.
Introduction to the Special Issue on “Usable AI”
Authors: Jameson Anthony , Spaulding Aaron , Yorke‐Smith Neil
Published: July 2017

When creating algorithms or systems that are supposed to be used by people, we should be able to adopt a “binocular” view of users' interaction with intelligent systems: a view that regards the design of interaction and the design of intelligent algorithms as interrelated parts of a single design problem. This special issue offers a coherent set of articles on two levels of generality that illustrate the binocular view and help readers to adopt it.
RealScape—Metropolitan Fixed Assets Change Judgment by Pixel‐by‐Pixel Stereo Processing of Aerial Photographs
Authors: Koizumi Hirokazu , Yagyu Hiroyuki , Hashizume Kazuaki , Kamiya Toshiyuki , Kunieda Kazuo , Shimazu Hideo
Published: July 2017

The Tokyo Metropolitan Government, the largest municipality in Japan, routinely conducts building‐change‐identification work. Recently, Tokyo terminated its traditional visual‐identification work, which had been used for 20 years, and shifted to a new automated system. This article introduces the Fixed Assets Change Judgment (FACJ) system and its core tool, RealScape. RealScape automatically detects changes in the height and color of buildings based on three‐dimensional analysis of aerial photographs. It employs a unique pixel‐by‐pixel stereo processing method and enables a foot‐level precision for each building. RealScape detects building changes more accurately than visual judgment operations by humans and reduces the labor costs to one third of the traditional approach and the required judgment duration to about two weeks per 100 km2.
Lessons Learned from Virtual Humans
Authors: Swartout William
Published: July 2017

Over the past decade, we have been engaged in an extensive research effort to build virtual humans and applications that use them. Building a virtual human might be considered the quintessential AI problem, because it brings together many of the key features, such as autonomy, natural communication, and sophisticated reasoning and behavior, that distinguish AI systems. This article describes major virtual human systems we have built and important lessons we have learned along the way.
Reports on the AAAI 2009 Fall Symposia
Authors: Azevedo Roger , Bench‐Capon Trevor , Biswas Gautam , Carmichael Ted , Green Nancy , Hadzikadic Mirsad , Koyejo Oluwasanmi , Kurup Unmesh , Parsons Simon , Pirrone Roberto , Prakken Henry , Samsonovich Alexei , Scott Donia , Souvenir Richard
Published: July 2017

The Association for the Advancement of Artificial Intelligence was pleased to present the 2009 Fall Symposium Series, held Thursday through Saturday, November 5‐7, at the Westin Arlington Gateway in Arlington, Virginia. The Symposium Series was preceded on Wednesday, November 4, by a one‐day AI funding seminar. The titles of the seven symposia were as follows: (1) Biologically Inspired Cognitive Architectures, (2) Cognitive and Metacognitive Educational Systems, (3) Complex Adaptive Systems and the Threshold Effect: Views from the Natural and Social Sciences, (4) Manifold Learning and Its Applications, (5) Multirepresentational Architectures for Human‐Level Intelligence, (6) The Uses of Computational Argumentation, and (7) Virtual Healthcare Interaction.
Recommendation as Collaboration in Web Search
Authors: Smyth Barry , Freyne Jill , Briggs Peter , Coyle Maurice
Published: July 2017

Recommender systems now play an important role in online information discovery, complementing traditional approaches such as search and navigation with a more proactive approach to discovery that is informed by the users' interests and preferences. To date recommender systems have been deployed within a variety of e‐commerce domains, covering a range of products such as books, music, and movies, and have proven to be a successful way to convert browsers into buyers. Recommendation technologies have a potentially much greater role to play in information discovery, however, and in this article we consider recent research that takes a fresh look at web search as a fertile platform for recommender systems research as users demand a new generation of search engines that are less susceptible to manipulation and more responsive to searcher needs and preferences.
Context‐Aware Recommender Systems
Authors: Adomavicius Gediminas , Mobasher Bamshad , Ricci Francesco , Tuzhilin Alex
Published: July 2017

Context‐aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in the recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context‐aware recommender systems.
A Brief Overview of Artificial Intelligence in South Africa
Authors: Ferrein Alexander , Meyer Thomas
Published: July 2017

One of the consequences of the growth in AI research in South Africa in recent years is the establishment of a number of research hubs involved in AI activities ranging from mobile robotics and computational intelligence to knowledge representation and reasoning and human language technologies. In this survey we take the reader through a quick tour of the research being conducted at these hubs and touch on an initiative to maintain and extend the current level of interest in AI research in the country.
Reports on the 2011 AAAI Fourth Artificial Intelligence for Interactive Digital Entertainment Conference Workshops
Authors: Elson David , Rowe Jonathan , Smith Adam M. , Smith Gillian , Tomai Emmett
Published: July 2017

The Seventh Artificial Intelligence for Interactive Digital Entertainment Conference (AIIDE‐11) was held October 11–14, 2011 at Stanford University, Stanford, California. Two one‐day workshops were held on October 11: Intelligent Narrative Technologies, and Artificial Intelligence in the Game Design Process. The highlights of each workshop are presented in this report.
The AAAI 2011 Robot Exhibition
Authors: Chernova Sonia , Dodds Zachary , Stilman Mike , Touretzky David S. , Thomaz Andrea L.
Published: July 2017

In this article we report on the exhibits and challenges shown at the AAAI 2011 Robotics Program in San Francisco. The event included a broad demonstration of innovative research at the intersection of robotics and artificial intelligence. Through these multiyear challenge events, our goal has been to focus the research community's energy toward common platforms and common problems to work toward the greater goal of embodied AI.
NewsFinder: Automating an AI News Service
Authors: Eckroth Joshua , Dong Liang , Smith Reid G. , Buchanan Bruce G.
Published: July 2017

NewsFinder automates the steps involved in finding, selecting, categorizing, and publishing news stories that meet relevance criteria for the artificial intelligence community. The software combines a broad search of online news sources with topic‐specific trained models and heuristics. Since August 2010, the program has been used to operate the AI in the News service that is part of the AAAI AITopics website.
Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure
Authors: Vyas Nisarg , Farringdon Jonathan , Andre David , Stivoric John (Ivo)
Published: July 2017

In this article we provide insight into the BodyMedia FIT armband system—a wearable multisensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine‐learning and data‐modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight loss. We describe several challenges that arise in applying machine‐learning techniques to the health‐care domain and present various solutions utilized in the armband system. We demonstrate how machine‐learning and multisensor data‐fusion techniques are critical to the system's success.
Learning by Demonstration for a Collaborative Planning Environment
Authors: Myers Karen , Kolojejchick Jake , Angiolillo Carl , Cummings Tim , Garvey Tom , Gaston Matt , Gervasio Melinda , Haines Will , Jones Chris , Keifer Kellie , Knittel Janette , Morley David , Ommert William , Potter Scott
Published: July 2017

Learning by demonstration technology has long held the promise to empower nonprogrammers to customize and extend software. We describe the deployment of a learning by demonstration capability to support user creation of automated procedures in a collaborative planning environment that is used widely by the U.S. Army. This technology, which has been in operational use since the summer of 2010, has helped to reduce user work loads by automating repetitive and time‐consuming tasks. The technology has also provided the unexpected benefit of enabling standardization of products and processes.
Introduction to the Special Issue: Innovative Applications of Artificial Intelligence 2011
Authors: Shapiro Daniel , Fromherz Markus
Published: July 2017

This special issue of AI Magazine contains expanded versions of five papers that describe deployed applications and two papers that discuss emergent applications from IAAI‐11 (the article by Warrick and colleagues is from IAAI‐10). We selected them for significance, novelty, and (in several cases) common task focus.
Negotiating Agents
Authors: Jonker Catholijn M. , Hindriks Koen V. , Wiggers Pascal , Broekens Joost
Published: July 2017

Negotiation is a complex emotional decision‐making process aiming to reach an agreement to exchange goods or services. From an agent technological perspective creating negotiating agents that can support humans with their negotiations is an interesting challenge. After more than a decade of research, negotiating agents can outperform human beings (in terms of deal optimality) if the negotiation space is well understood. However, the inherent semantic problem and the emotional issues involved mean that negotiation cannot be handled by artificial intelligence alone, and a human‐machine collaborative system is required. This article presents research goals, challenges, and an approach to create the next generation of negotiation support agents.
The Multi‐Agent Programming Contest
Authors: Behrens Tristan , Dastani Mehdi , Dix Jürgen , Hübner Jomi , Köster Michael , Novák Peter , Schlesinger Federico
Published: July 2017

The International Multi‐Agent Programming Contest (MAPC), is a community‐serving effort to facilitate advances in programming multiagent systems (MASs) by developing benchmark problems, enabling head‐to‐head comparison of MASs, and supporting educational efforts in the design and implementation of MASs.
Reports of the AAAI 2012 Conference Workshops
Authors: Agrawal Vikas , Baier Jorge , Bekris Kostas , Chen Yiling , Garcez Artur S. d'Avila , Hitzler Pascal , Haslum Patrik , Jannach Dietmar , Law Edith , Lecue Freddy , Lamb Luis C. , Matuszek Cynthia , Palacios Hector , Srivastava Biplav , Shastri Lokendra , Sturtevant Nathan , Stern Roni , Tellex Stefanie , Vassos Stavros
Published: July 2017

The AAAI‐12 Workshop program was held Sunday and Monday, July 22–23, 2012, at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI‐12 workshop program included nine workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, Cognitive Robotics, Grounding Language for Physical Systems, Human Computation, Intelligent Techniques for Web Personalization and Recommendation Systems, Multiagent Pathfinding, Neural‐Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, and Semantic Cities. This article presents short summaries of those events.
Introduction to the IAAI Articles in This Issue: Emerging Innovative Applications of Artificial Intelligence 2012
Authors: Fromherz Markus , Muñoz‐Avila Héctor
Published: July 2017

This issue of AI Magazine contains the Robert S. Engelmore Memorial Lecture paper and expanded versions of articles that discuss emerging applications from IAAI‐12.
eBird: A Human / Computer Learning Network to Improve Biodiversity Conservation and Research
Authors: Kelling Steve , Lagoze Carl , Wong Weng‐Keen , Yu Jun , Damoulas Theodoros , Gerbracht Jeff , Fink Daniel , Gomes Carla
Published: July 2017

eBird is a citizen‐science project that takes advantage of the human observational capacity to identify birds to species, and uses these observations to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a human/computer learning network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both and thereby continually improves the effectiveness of the net‐ work as a whole. In this article we explore how human/computer learning networks can leverage the contributions of human observers and process their contributed data with artificial intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.
Statistical Anomaly Detection for Train Fleets
Authors: Holst Anders , Bohlin Markus , Ekman Jan , Sellin Ola , Lindström Björn , Larsen Stefan
Published: July 2017

We have developed a method for statistical anomaly detection that has been deployed in a tool for condition monitoring of train fleets. The tool is currently used by several railway operators across the world to inspect and visualize the occurrence of “event messages” generated on the trains. The anomaly detection component helps the operators quickly to find significant deviations from normal behavior and to detect early indications for possible problems. The method used is based on Bayesian principal anomaly, which is a framework for parametric anomaly detection using Bayesian statistics. The savings in maintenance costs of using the tool comes mainly from avoiding costly breakdowns and have been estimated to be several million Euros per year for the tool. In the long run, it is expected that maintenance costs can be reduced by between 5 and 10 percent with the help of the tool.
Deployed Innovative Applications of Artificial Intelligence 2012
Authors: Fromherz Markus , Muñoz‐Avila Héctor
Published: July 2017

This issue of AI Magazine features expanded versions of articles that discuss deployed applications from the 2012 AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI‐12).
User‐Centric Indoor Air‐Quality Monitoring on Mobile Devices
Authors: Jiang Yifei , Li Kun , Piedrahita Ricardo , Xiang Yun , Tian Lei , Mansata Omkar , Lv Qin , Dick Robert P. , Hannigan Michael , Shang Li
Published: July 2017

Because people spend a majority of their time indoors, indoor air quality (IAQ) can have a significant impact on human health, safety, productivity, and comfort. Because of the diversity and dynamics of people's indoor activities, it is important to monitor IAQ for each individual. Most existing airquality sensing systems are stationary or focus on outdoor air quality. In contrast,we propose MAQS, a user‐centered mobile sensing system for IAQ monitoring. MAQS users carry portable, indoor location tracking and IAQ sensing devices that provide personalized IAQ information in real time. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n‐gram augmented Bayesian room localization method that requires few Wi‐Fi fingerprints; (2) an air‐exchange‐rate‐ based IAQ sensing method, which measures general IAQ using only CO2 sensors; and (3) a zone‐based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated through a real‐world user study. This evaluation demonstrates that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency. We also found that study participants frequently experienced poor IAQ
Intelligent Learning Technologies: Applications of Artificial Intelligence to Contemporary and Emerging Educational Challenges
Authors: Chaudhri Vinay K. , Gunning Dave , Lane H. Chad , Roschelle Jeremy
Published: July 2017

The fall and winter issues of AI Magazine present articles on some of the most interesting projects at the intersection of AI and education. Included are articles on integrated systems such as virtual humans, an intelligent textbook, and a game‐based learning environment as well as technology‐focused components such as student models and data mining. The winter issue will conclude with an article summarizing the contemporary and emerging challenges at the intersection of AI and education.
Serious Games Get Smart: Intelligent Game‐Based Learning Environments
Authors: Lester James C. , Ha Eun Y. , Lee Seung Y. , Mott Bradford W. , Rowe Jonathan P. , Sabourin Jennifer L.
Published: July 2017

Intelligent game‐based learning environments integrate commercial game technologies with AI methods from intelligent tutoring systems and intelligent narrative technologies. This article introduces the Crystal Island intelligent game‐based learning environment, which has been under development in the authors' laboratory for the past seven years. After presenting Crystal Island, the principal technical problems of intelligent game‐based learning environments are discussed: narrative‐centered tutorial planning, student affect recognition, student knowledge modeling, and student goal recognition. Solutions to these problems are illustrated with research conducted with the Crystal Island learning environment
DynaLearn — An Intelligent Learning Environment for Learning Conceptual Knowledge
Authors: Bredeweg Bert , Liem Jochem , Beek Wouter , Linnebank Floris , Gracia Jorge , Lozano Esther , Wiβner Michael , Bühling René , Salles Paulo , Noble Richard , Zitek Andreas , Borisova Petya , Mioduser David
Published: July 2017

Articulating thought in computer‐based media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an intelligent learning environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components that are capable of generating knowledge‐based feedback and virtual characters that enhance the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed. This article presents an overview of the DynaLearn system.
Report on the 2013 Affective Computing and Intelligent Interaction Conference (ACII 2013)
Authors: Pun Thierry , Nijholt Anton
Published: July 2017

The 2013 Affective Computing and Intelligent Interaction Conference (ACII 2013) was held in Geneva, Switzerland, September 2–5, 2013. This report summarizes the major events of the conference.
An Antimicrobial Prescription Surveillance System That Learns from Experience
Authors: Beaudoin Mathieu , Kabanza Froduald , Nault Vincent , Valiquette Louis
Published: July 2017

Inappropriate prescribing of antimicrobials is a major clinical concern that affects as many as 50 percent of prescriptions. One of the difficulties of antimicrobial prescribing lies in the necessity to sequentially adjust the treatment of a patient as new clinical data become available. The lack of specialized health‐care resources and the overwhelming amount of information to process make manual surveillance unsustainable. To solve this problem, we have developed and deployed an automated antimicrobial prescription surveillance system that assists hospital pharmacists in identifying and reporting inappropriate prescriptions. Since its deployment, the system has improved antimicrobial prescribing and decreased antimicrobial use. However, the highly sensitive knowledge base used by the system leads to many false alerts. As a remedy, we are developing a machine‐learning algorithm that combines instance‐based learning and rule induction techniques to discover new rules for detecting inappropriate prescriptions from previous false alerts. In this article, we describe the system, point to results and lessons learned so far, and provide insight into the machine‐learning capability.
The Diagnostic Competitions
Authors: Feldman Alexander , de Kleer Johan , Kurtoglu Tolga , Narasimhan Sriram , Poll Scott , Garcia David , Kuhn Lukas , van Gemund Arjan J. C.
Published: July 2017

The international diagnostic competitions provide a set of diagnostic benchmarks to evaluate diagnostic algorithms. This article describes a common diagnostic framework used to evaluate these algorithms. These competitions, started in 2009, have significantly helped shape subsequent diagnostic algorithms.
Crowdsourcing Meets Ecology: Hemispherewide Spatiotemporal Species Distribution Models
Authors: Fink Daniel , Damoulas Theodoros , Bruns Nicholas E. , La Sorte Frank A. , Hochachka Wesley M. , Gomes Carla P. , Kelling Steve
Published: July 2017

Ecological systems are inherently complex. The processes that affect the distributions of animals and plants operate at multiple spatial and temporal scales, presenting a unique challenge for the development and coordination of effective conservation strategies, particularly for wide‐ranging species. In order to study ecological systems across scales, data must be collected at fine resolutions across broad spatial and temporal extents. Crowdsourcing has emerged as an efficient way to gather these data by engaging large numbers of people to record observations. However, data gathered by crowdsourced projects are often biased due to the opportunistic approach of data collection. In this article, we propose a general class of models called AdaSTEM (for adaptive spatiotemporal exploratory models) that are designed to meet these challenges by adapting to multiple scales while exploiting variation in data density common with crowdsourced data. To illustrate the use of AdaSTEM, we produce intraseasonal distribution estimates of long‐distance migrations across the Western Hemisphere using data from eBird, a citizen science project that utilizes volunteers to collect observations of birds. Subsequently, model diagnostics are used to quantify and visualize the scale and quality of distribution estimates. This analysis shows how AdaSTEM can automatically adapt to complex spatiotemporal processes across a range of scales, thus providing essential information for full‐life cycle conservation planning of broadly distributed species, communities, and ecosystems.
Workshops Held at the Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE): A Report
Authors: Liapis Antonios , Cook Michael , Smith Adam M. , Smith Gillian , Zook Alexander , Cavazza Marc , Si Mei , Pasquier Philippe
Published: July 2017

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) was held October 14–18, 2013, at Northeastern University in Boston, Massachusetts. Workshops were held on the two days prior to the start of the main conference, giving attendees a chance to hold in‐depth discussions on topics that complement the themes of the main conference program. This year the workshops included the First Workshop on AI and Game Aesthetics (1 day), the Second Workshop on AI in the Game Design Process (1 day), the Second International Workshop on Musical Metacreation (2 day), and the Sixth Workshop on Intelligent Narrative Technologies (2 day).
The MiniZinc Challenge 2008–2013
Authors: Stuckey Peter J. , Feydy Thibaut , Schutt Andreas , Tack Guido , Fischer Julien
Published: July 2017

MiniZinc is a solver‐agnostic modeling language for defining and solving combinatorial satisfaction and optimization problems. MiniZinc provides a solver‐independent modeling language that is now supported by constraint‐programming solvers, mixed integer programming solvers, SAT and SAT modulo theory solvers, and hybrid solvers. Every year since 2008 we have run the MiniZinc Challenge, which compares and contrasts the different strengths of different solvers and solving technologies on a set of MiniZinc models. Here we report on what we have learned from running the competition for 6 years.
Energy and Uncertainty: Models and Algorithms for Complex Energy Systems
Authors: Powell Warren B.
Published: July 2017

The problem of controlling energy systems (generation, transmission, storage, investment) introduces a number of optimization problems that need to be solved in the presence of different types of uncertainty. I highlight several of these applications, using a simple energy storage problem as a case application. Using this setting, I describe a modeling framework that is based on five fundamental dimensions and that is more natural than the standard canonical form widely used in the reinforcement learning community. The framework focuses on finding the best policy, where I identify four fundamental classes of policies consisting of policy function approximations (PFAs), cost function approximations (CFAs), policies based on value function approximations (VFAs), and look‐ahead policies. This organization unifies a number of competing strategies under a common umbrella.
The Dialog State Tracking Challenge Series
Authors: Williams Jason D. , Henderson Matthew , Raux Antoine , Thomson Blaise , Black Alan , Ramachandran Deepak
Published: July 2017

In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. The Dialog State Tracking Challenge is a research community challenge task that has run for three rounds. The challenge has given rise to a host of new methods for dialog state tracking and also to deeper understanding about the problem itself, including methods for evaluation.
Semantics for Big Data
Authors: van Harmelen Frank , Hendler James A. , Hitzler Pascal , Janowicz Krzysztof
Published: July 2017

This editorial introduction summarizes the seven guest‐edited contributions to AI Magazine that explore opportunities and challenges arising from transferring and adapting sema.ntic web technologies to the big data quest.
Reports on the 2015 AAAI Workshop Series
Authors: Albrecht Stefano V. , Beck J. Christopher , Buckeridge David L. , Botea Adi , Caragea Cornelia , Chi Chi‐hung , Damoulas Theodoros , Dilkina Bistra , Eaton Eric , Fazli Pooyan , Ganzfried Sam , Giles C. Lee , Guillet Sébastien , Holte Robert , Hutter Frank , Koch Thorsten , Leonetti Matteo , Lindauer Marius , Machado Marlos C. , Malitsky Yuri , Marcus Gary , Meijer Sebastiaan , Rossi Francesca , Shaban‐Nejad Arash , Thiebaux Sylvie , Veloso Manuela , Walsh Toby , Wang Can , Zhang Jie , Zheng Yu
Published: July 2017

AAAI's 2015 Workshop Program was held Sunday and Monday, January 25–26, 2015, at the Hyatt Regency Austin Hotel in Austin, Texas, USA. The AAAI‐15 workshop program included 16 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included Algorithm Configuration; Artificial Intelligence and Ethics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Artificial Intelligence for Cities; Artificial Intelligence for Transportation: Advice, Interactivity, and Actor Modeling; Beyond the Turing Test; Computational Sustainability; Computer Poker and Imperfect Information; Incentive and Trust in E‐Communities; Knowledge, Skill, and Behavior Transfer in Autonomous Robots; Learning for General Competency in Video Games; Multiagent Interaction without Prior Coordination; Planning, Search, and Optimization; Scholarly Big Data: AI Perspectives, Challenges, and Ideas; Trajectory‐Based Behaviour Analytics; and World Wide Web and Public Health Intelligence.
CiteSeerX: AI in a Digital Library Search Engine
Authors: Wu Jian , William Kyle , Chen Hung‐Hsuan , Khabsa Madian , Caragea Cornelia , Tuarob Suppawong , Ororbia Alexander , Jordan Douglas , Mitra Prasenjit , Giles C. Lee
Published: July 2017

CiteSeerX is a digital library search engine that provides access to more than 5 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 deduplication, 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, that 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 search engines.
THink: Inferring Cognitive Status from Subtle Behaviors
Authors: Davis Randall , Libon David J. , Au Rhoda , Pitman David , Penney Dana L.
Published: July 2017

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.
Reports on the 2015 AAAI Spring Symposium Series
Authors: Agarwal Nitin , Andrist Sean , Bohus Dan , Fang Fei , Kagal Lalana , Kido Takashi , Kiekintveld Christopher , Lawless W. F. , McCallum Andrew , Purohit Hemant , Seneviratne Oshani , Takadama Keiki , Taylor Gavin
Published: July 2017

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.
Control Strategies and Artificial Intelligence in Rehabilitation Robotics
Authors: Novak Domen , Riener Robert
Published: July 2017

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.
Cognitive Prosthetics for Fostering Learning: A View from the Learning Sciences
Authors: Kolodner Janet L.
Published: July 2017

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.
Reports of the 2015 Workshops Held at the International AAAI Conference on Web and Social Media
Authors: Garcia David , Halegoua Germaine , Mejova Yelena , Perra Nicola , Pfeffer Jürgen , Ruths Derek , Weber Ingmar , West Robert , Zia Leila
Published: July 2017

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; the ICWSM Science Slam; and Wikipedia, a Social Pedia: Research Challenges and Opportunities. This article contains the written reports of six of the workshops.
Cognitive Orthoses: Toward Human‐Centered AI
Authors: Ford Kenneth M. , Hayes Patrick J. , Glymour Clark , Allen James
Published: July 2017

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.