We investigate the impact of a discussion snippet's overall sentiment on a user's willingness to read more of a discussion. Using sentiment analysis, we constructed positive, neutral, and negative discussion snippets using the discussion topic and a sample comment from discussions taking place around content on an enterprise social networking site. We computed personalized snippet recommendations for a subset of users and conducted a survey to test how these recommendations were perceived. Our experimental results show that snippets with high sentiments are better discussion "teasers."

This study examined young people’s trust of Wikipedia as an information resource. A large-scale probability-based survey with embedded quasi-experiments was conducted with 2,747 children in the U.S. ranging from 11 to 18 years old. Results show that young people find Wikipedia to be fairly credible, but also exhibit an awareness of potential problems with non-expert, user-generated content in anonymous environments. Children tend to evaluate the credibility of online encyclopedia information with this in mind, at times with what appears to be an unwarranted devaluation of this information.

As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs that the current Twitter interface fails to support, and argue for better representations of content for solving these challenges. We present a scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.

The American Football betting market provides a particularly attractive domain to study the nexus between public sentiment and the wisdom of crowds. In this paper, we present the first substantial study of the relationship between the NFL betting line and public opinion expressed in blogs and microblogs (Twitter). We perform a large-scale study of four distinct text streams: LiveJournal blogs, RSS blog feeds captured by Spinn3r, Twitter, and traditional news media. Our results show interesting disparities between the first and second halves of each season. We present evidence showing usefulness of sentiment on NFL betting. We demonstrate that a strategy betting roughly 30 games per year identified winner roughly 60% of the time from 2006 to 2009, well beyond what is needed to overcome the bookie's typical commission(53%).

Given a set of reviews of products or merchants from a wide range of authors and several reviews websites, how can we measure the true quality of the product or merchant?  How do we remove the bias of individual authors or sources?  How do we compare reviews obtained from different websites, where ratings may be on different scales (1-5 stars, A/B/C, etc.)?  How do we filter out unreliable reviews to use only the ones with ``star quality''?  Taking into account these considerations, we analyze data sets from a variety of different reviews sites (the first paper, to our knowledge, to do this). These data sets include 8 million product reviews and 1.5 million merchant reviews. We explore statistic- and heuristic- based models for estimating the true quality of a product or merchant, and compare the performance of these estimators on the task of ranking pairs of objects.  We also apply the same models to the task of using Netflix ratings data to rank pairs of movies, and discover that the performance of the different models is surprisingly similar on this data set.

Series, held Thursday through Saturday, November 5-7, at he Association for the Advancement of Artificial Intelligence the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia were as follows: (1) Biologically Inspired Cognitive Biologically Inspired Cognitive Architectures Architectures, (2) Cognitive and Metacognitive Cognitive and Metacognitive Educational Systems Educational Systems, (3) Complex Adaptive Complex Adaptive Systems and the Threshold Effect: Views from the Natural Systems and the Threshold Effect: Views and Social Sciences from the Natural and Social Sciences, (4) Manifold Manifold Learning and Its Applications Learning and Its Applications, (5) Multirepresentational Architectures for Human-Level Multirepresentational Architectures for Human-Level Intelligence Intelligence, (6) The Uses of Computational The Uses of Computational Argumentation Argumentation, and (7) Virtual Healthcare Virtual Healthcare Interaction Interaction. An informal reception was held on Thursday, November 5. A general plenary session, in which the highlights of each symposium were presented, was held on Friday, November 6. The challenge of creating a real-life computational equivalent of the human mind requires that we better understand at a computational level how natural intelligent systems develop their cognitive and learning functions. They will behave, variety of disjoined communities and schools of learn, communicate, and "think" as conscious thought that used to speak different languages and beings in general, in addition to being able to perform ignore each other.

The poker AAAI membership is required for eligibility.

We discuss an interaction-based approach to study the coevolution between socio-technical networks, individual behaviors, and contagion processes on these networks. We use epidemics in human population as an example of this phenomenon. The methods consist of developing synthetic yet realistic national-scale networks using a first principles approach. Unlike simple random graph techniques, these methods combine real world data sources with behavioral and social theories to synthesize detailed social contact (proximity) networks. Individual-based models of within-host disease progression and inter-host transmission are then used to model the contagion process. Finally, models of individual behaviors are composed with disease progression models to develop a realistic representation of the complex system in which individual behaviors and the social network adapt to the contagion. These methods are embodied within Simdemics – a general purpose modeling environment to support pandemic planning and response. Simdemics is designed specifically to be scalable to networks with 300 million agents – the underlying algorithms and methods in Simdemics are all high-performance computing oriented methods. New advances in network science, machine learning, high performance computing, data mining and behavioral modeling were necessary to develop Simdemics. Simdemics is combined with two other environments, Simfrastructure and Didactic, to form an integrated cyberenvironment. The integrated cyber-environment provides the end-user flexible and seamless Internet based access to Simdemics. Service-oriented architectures play a critical role in delivering the desired services to the end user. Simdemics, in conjunction with the integrated cyber-environment, has been used in over a dozen user defined case studies. These case studies were done to support specific policy questions that arose in the context of planning the response to pandemics (e.g., H1N1, H5N1) and human initiated bio-terrorism events. These studies played a crucial role in the continual development and improvement of the cyber-environment.

Preventive maintenance schedules occurring in industry are often suboptimal with regard to maintenance coal-location, loss-of-production costs and availability. We describe the implementation and deployment of a software decision support tool for the maintenance planning of gas turbines, with the goal of reducing the direct maintenance costs and the often costly production losses during maintenance downtime. The optimization problem is formally defined, and we argue that the feasibility version is NP-complete. We outline a heuristic algorithm that can quickly solve the problem for practical purposes and validate the approach on a real-world scenario based on an oil production facility. We also compare the performance of our algorithm with results from using integer programming, and discuss the deployment of the application. The experimental results indicate that downtime reductions up to 65% can be achieved, compared to traditional preventive maintenance. In addition, the use of our tool is expected to improve availability with up to 1% and reduce the number of planned maintenance days by 12%. Compared to a integer programming approach, our algorithm is not optimal, but is much faster and produces results which are useful in practice. Our test results and SIT AB’s estimates based< on operational use both indicate that significant savings can be achieved by using our software tool, compared to maintenance plans with fixed intervals.

We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece in September 2009. We give an overview of how the competition has developed since its first run in 2005, and its relationship with the AI planning field. This run of the competition focused on translators that when input with some formal description in an application-area-specific language, output solver-ready domain models. Despite a fairly narrow focus within knowledge engineering, seven teams took part in what turned out to be a very interesting and successful competition.