The New York Public Library is participating in the Chronicling America initiative to develop an online searchable database of historically significant newspaper articles. Microfilm copies of the papers are scanned and high resolution OCR software is run on them. The text from the OCR provides a wealth of data and opinion for researchers and historians. However, the categorization of articles provided by the OCR engine is rudimentary and a large number of the articles are labeled ``editorial" without further categorization. To provide a more refined grouping of articles, unsupervised machine learning algorithms (such as K-Means) are being investigated. The K-Means algorithm requires tuning of parameters such as the number of clusters and mechanism of seeding to ensure that the search is not prone to being caught in a local minima. We designed a pilot study to observe whether humans are adept at finding sub-categories. The subjective labels provided by humans are used as a guide to compare performance of the automated clustering techniques. In addition, seeds provided by annotators are carefully incorporated into a semi-supervised K-Means algorithm (Seeded K-Means); empirical results indicate that this helps to improve performance and provides an intuitive sub-categorization of the articles labeled ``editorial" by the OCR engine.

Community mining is applied in order to identify groups of users which share, e.g., common interests or expertise. This paper presents an approach for mining descriptive patterns in order to characterize communities in terms of their distinctive features: For an efficient discovery approach, we introduce optimistic estimates for obtaining an upper bound for the community quality. We present an evaluation using data from the real-world social bookmarking system BibSonomy.

In this paper, we show how to make a cognitive tutoring agent capable of precise causal reasoning by integrating constraints with data mining algorithms. Putting constraints on recorded interactions between the agent and learners during learning activities allows data mining algorithms to extract the causes of the learners’ problems. Subsequently, the agent uses this information to provide useful and customized explanations to learners.

We have experimented with the notion of — conceptualization, and contextualization from situated cognition and psychic reflection from activity theory for identifying context into a method called the activity states framework (ASF). The purpose of the ASF is to provide a method of analysis for identifying collaborators activity during situated context − specific to Web-mediated collaboration. This paper introduces the ASF.

In this paper, we describe a framework dedicated to studies and experimentations upon the nature of the relationships between the rational reasoning process of an artificial agent and its psychological counterpart, namely its behavioral reasoning process. This study is focused on the domain of Conversational Assistant Agents, which are software tools providing various kinds of assistance to people of the general public interacting with computer-based applications or services. In this context, we show on some examples the need for the agents to be able to exhibit both a rational reasoning about the system functioning and a human-like believable dialogical interaction with the users.

We discuss and evaluate metrics for difficulty rating of Sudoku puzzles. The correlation coefficient with human performance for our best metric is 0.95. The data on human performance were obtained from three web portals and they comprise thousands of hours of human solving over 2000 problems. We provide a simple computational model of human solving activity and evaluate it over collected data. Using the model we show that there are two sources of problem difficulty: complexity of individual steps (logic operations) and structure of dependency among steps. Beside providing a very good Sudoku-tuned metric, we also discuss a metric with few Sudoku-specific details, which still provides good results (correlation coefficient is 0.88). Hence we believe that the approach should be applicable to difficulty rating of other constraint satisfaction problems.

What determines difficulty of solving a problem? Although this question has been studied before, we found examples which show large differences in problem difficulty which are not explained by concepts identified in previous research. This differences are caused mainly by the structure of a problems' state spaces and cannot be easily captured by static metrics like size of the state space or the length of a solution. To address these unexplained differences, we propose a computational model of human problem solving behaviour. We provide evaluation of the model over large scale dataset (hundreds of hours of problem solving, more than 100 problem instances) for three transport puzzles (Sokoban, Rush hour, and Replacement puzzle).

We used a stimulus and response method in cognition to consider agents as situated in their specific (Binti Abdullah et al, 2010) to uncover correlation patterns context as it was realized that people are strongly affected of the physical artefact-communication during specific by, and possibly dependent on their environment contexts of communications. We found preliminary empirical (Susi & Ziemke, 2001). With this shift of focus, new interactive evidence that the physical artefacts influence the theories of cognition have emerged. These interactive communication process in a mutually constraining relationship theories such as situated cognition (Clancey, 1997), with the contexts. In which the context is made up and distributed cognition (Hutchins, 1999), are noted for of the teams' practice that includes how they collaborate, their emphasis on the relationship between cognition, and the physical setting, situations, and participation role.

In this paper we introduce our recommender Happy Movie, a Facebook application for movie recommendation to groups. This system exploits information about the social relationships and behaviour of the users to provide better recommendations. Our previous works have shown that social factors improve the recommendation results. However it required many questionnaires to be filled for obtaining the social information, so we have moved to a social network environment where this information is easily available.

If given manually-crafted goal selection knowledge, goal reasoning agents can dynamically determine which goals they should achieve in complex environments. These agents should instead learn goal selection knowledge through expert interaction. We describe T-ARTUE, a goal reasoning agent that performs case-based active and interactive learning to discover goal selection knowledge. We also report tests of its performance in a complex environment. We found that, under some conditions, T-ARTUE can quickly learn goal selection knowledge.