Concept maps are frequently used in K-12 educational settings. The purpose of this study is to determine whether students’ performance on interactive concept map tasks in Guru, an intelligent tutoring system, is related to immediate and delayed learning outcomes. Guru is a dialogue-based system for high-school biology that intersperses concept map tasks within the tutorial dialogue. Results indicated that when students first attempt to complete concept maps, time spent on the maps may be a good indicator of their understanding, whereas the errors they make on their second attempts with the maps may be an indicator of the knowledge they are lacking.  This pattern of results was observed for one cycle of testing, but not replicated in a second cycle. Differences in the findings for the two testing cycles are most likely due to topic variations.

In this paper, we propose a model for semantic clustering of entities extracted from a text, and we apply it to a Proper Noun classification task.This model is based on a new method to compute the similarity between the entities.Indeed, the classical way of calculating similarity is to build a feature vector or Bag-of-Features for each entity and then use classical similarity functions like Cosine.In practice, the features are contextual, such as words around the different occurrences of each entity. Here, we propose to use an alternative representation for entities, called Bag-of-Vectors, or Bag-of-Bags-of-Features.In this new model, each entity is not defined as a unique vector but as a set of vectors, in which each vector is built based on the contextual features of one occurrence of the entity.In order to use Bag-of-Vectors for clustering, we introduce new versions of classical similarity functions such as Cosine and Scalar Products. Experimentally, we show that the Bag-of-Vectors representation always improve the clustering results compared to classical Bag-of-Features representations.

Analyzing international political behavior based on similar precedent circumstances is one of the basic techniques that policymakers use to monitor and assess current situations. Our goal is to investigate how to analyze geopolitical conflicts as sequences of events and to determine what probabilistic models are suitable to perform these analyses. In this paper, we evaluate the performance of N-grams models on the problem of forecasting political conflicts from sequences of events. For the current phase of the project, we focused on event data collected from the Balkans war in the 1990's. Our experimental results indicate that N-gram models have impressive results when applied to this data set, with accuracies above 90\% for most configurations.

In the Silver Anniversary year of FLAIRS, in an effort to promote discussion of emerging ideas and work in order to encourage and help guide researchers, especially new researchers, the program committee added the poster abstract submission category. This allows researchers to present a full poster in the conference poster session and receive that critical, work-shaping feedback that helps guide good work into great work.

In this paper we describe an improvement to the popular IDA* search algorithm that emphasizes a different space-for-time trade-off than previously suggested. In particular, our algorithm, called Iterative-Expansion A* (IEA*), focuses on reducing redundant node expansions within individual depth-first search DFS iterations of IDA* by employing a relatively small amount of available memory—bounded by the error in the heuristic—to store selected nodes. The additional memory required is exponential not in the solution depth, but only in the difference between the solution depth and the estimated solution depth. A constant-time hash set lookup can then be used to prune entire subtrees as DFS proceeds. Overall, we show 2- to 26-fold time speedups vs. an optimized version of IDA* across several domains, and compare IEA* with several other competing approaches. We also sketch proofs of optimality and completeness for IEA*, and note that IEA* is particularly efficient for solving implicitly-defined general graph search problems.

Calliope is an open source mobile robot designed in the Tekkotsu Lab at Carnegie Mellon University in collaboration with RoPro Design, Inc. The Calliope5SP model features an iRobot Create base, an ASUS netbook, a 5-degree of freedom arm with a gripper with two independently controllable fingers, and a Sony PlayStation Eye camera and Robotis AX-S1 IR rangefinder on a pan/tilt mount. We use chess as a test of Calliope’s abilities. Since Calliope is a mobile platform we consider how problems in vision and localization directly impact the performance of manipulation. Calliope’s arm is too short to reach across the entire chessboard. The robot must therefore navigate to a location that provides the best position to access the pieces it wants to move. The robot proved capable of performing small-scale manipulation tasks that require careful positioning.

Intelligent tutoring systems research aims to produce systems that meet or exceed the effectiveness of one-on-one expert human tutoring. Theory and empirical study suggest that affective states of the learner must be addressed to achieve this goal. While many affective measures can be utilized, posture offers the advantages of non-intrusiveness and ease of interpretation. This paper presents an accurate posture estimation algorithm applied to a computer-mediated tutoring corpus of depth recordings. Analyses of posture and session-level student reports of engagement and cognitive load identified significant patterns. The results indicate that disengagement and frustration may coincide with closer postural positions and more movement, while focused attention and less frustration occur with more distant, stable postural positions. It is hoped that this work will lead to intelligent tutoring systems that recognize a greater breadth of affective expression through channels of posture and gesture.

We discuss the design and implementation of the pro- totype of an avatar-based health system aimed at pro- viding people access to an effective behavior change intervention which can help them to find and cultivate motivation to change unhealthy lifestyles. An empathic Embodied Conversational Agent (ECA) delivers the in- tervention. The health dialog is directed by a compu- tational model of Motivational Interviewing, a novel effective face-to-face patient-centered counseling style which respects an individual’s pace toward behavior change. Although conducted on a small sample size, re- sults of a preliminary user study to asses users’ accep- tance of the avatar counselor indicate that the current early version of the system prototype is well accepted by 75% of users.

A possible approach to extend classical logics to probabilistic logics is to consider a probability distribution over the classical interpretations that satisfies some constraints and maximizes entropy. Over the past years miscellaneous languages and semantics have been considered often based on similar ideas. In this paper a hierarchy of general probabilistic semantics is developed. It incorporates some interesting specific semantics and a family of standard semantics that can be used to extend arbitrary languages with finite interpretation sets to probabilistic languages. We use the hierarchy to generalize an approach reducing the complexity of the whole entailment process and sketch the importance for further theoretical and practical applications.

Special tracks are a vital part of the FLAIRS Thanks go to the authors of both accepted and rejected conferences, with 11 held at FLAIRS-25. Over 90 papers; the special track coordinator Chutima percent of the papers were reviewed by four or Boonthum-Denecke and all the special track organizers; more reviewers, and all papers were reviewed by at the program committees and their reviewers; least three. These were coordinated by the program the invited speakers; Chad Lane for organizing committees of the general conference and the special the conference; Jean Gerber for administering the tracks. The accepted submissions include 74 conference; the Florida Artificial International Research full papers (19 from the general conference and 55 Society for maintaining the conference series; from the special tracks), 27 short papers presented the Association for the Advancement of Artificial as posters (6 from the general conference and 21 Intelligence for its cooperation with the conference; from the special tracks), and 20 poster abstracts Mike Hamilton for organizing the publication that appear in these proceedings. of the proceedings; and EasyChair for hosting the review process. The program included five invited talks: Bill Swartout, the Director of Technology and Research Professor at the University of Southern California's