Using natural language as a way for students to interact with an ITS has many advantages. However, creating the intelligence with which the tutor evaluates a student’s natural language input is challenging. We describe a system, ConceptGrid, that allows non-programmers to create the instruction for checking natural language input. Three tutor authors used the system to develop answer templates for conceptual-based questions in statistics. Results indicate ConceptGrid is a viable system for non-programmers to use to allow students to use natural language to interact with a tutor.

The current paradigm in student modeling has continued to show the power of its simplifying assumption of knowledge as a binary and monotonically increasing construct, the value of which directly causes the outcome of student answers to questions. Recent efforts have focused on optimizing the prediction accuracy of responses to questions using student models. Incorporating individual student parameter interactions has been an interpretable and principled approach which has improved the performance of this task, as demonstrated by its application in the 2010 KDD Cup challenge on Educational Data. Performance prediction, however, can have limited practical utility. The greatest utility of such student models can be their ability to model the tutor and the attributes of the tutor which are causing learning. Harnessing the same simplifying assumption of learning used in student modeling, we can turn this model on its head to effectively tease out the tutor attributes causing learning and begin to optimize the tutor model to benefit the student model.

Intelligent tutoring systems (ITS) is a multidisciplinary field of study that draws upon artificial intelligence, computer science, and cognitive science to create computerized tutoring systems that offer immediate feedback and individualized instruction. Broadly construed, most intelligent tutoring systems can be characterized as having two loops: an outer loop and an inner loop. In general, the goal of the track is to bring together an international group of scientists to present current research, design, and empirical evaluations of their tutoring systems. is track is meant to inform researchers on the recent developments in both the design of tutoring systems, as well as their evaluation. Topics included game-based, narrative-based and virtual learning environments; NLP and dialogue in tutoring systems; modeling and shaping affective state; metacognition; gaming the system; ill-defined domains; educational data mining; authoring tools for nonexperts; adaptive educational hypermedia; collaborative and group learning; open learner modeling; ontology engineering for educational purposes; novel interfaces; human computer interaction in educational settings; design decisions to increase engagement; and assistive technologies for learners with special needs.

This work presents an algorithm to provide a better represen- tation of space to artificially intelligent characters (i.e., agents or bots) in game and simulation environments by providing a more accurate breakdown of the traversable space present in the game environment. Such representations are generally constructed by decomposing the walkable space present in a game environment into a series of convex regions to form a data structure called a navigation mesh. We extend the basic concept of a navigation mesh by the introduction of an understanding of the textures that are attached to the underlying geometry creating what we refer to as a texture-aware navigation mesh. This does result in a more complex navigation mesh (more regions and a larger search space). However, since the textures of walkable geometry can be used to determine the appropriate traversal method for that terrain, a game character can determine valid paths for their traversal methods using just the navigation mesh (e.g., characters in cars can generate paths containing just roads or walking characters can create paths containing just sidewalks). We also present a use case that shows how such a system of texture aware naviga- tion meshes might benefit character path planning and search in virtual environments. In this use case, we examine a Real Time Strategy game style game environment, which shows it is possible to generate a navigation mesh such that each region is composed of a single terrain type.

We model the pursuit problem as a set of distributed agents communicating over a network subject to latency. Latency has serious deleterious effects on solving the pursuit problem. In this paper, we present a simple, yet effective way of dealing with latency that yields very good performance. Our method disperses predators within a region in which the prey may move that accounts for network latency.

This paper deals with planning domains that appear in computer games, especially when modeling intelligent virtual agents. Some of these domains contain only actions with no negative effects and are thus treated as easy from the planning perspective. We propose two new techniques to solve the problems in these planning domains, a heuristic search algorithm ANA* and a constraint-based planner RelaxPlan, and we compare them with the state-of-the-art planners, that were successful in IPC, using planning domains motivated by computer games.

Digital games and entertainment are a modern area of enormous economic potential and can have a serious social impact. Digital entertainment has become a constant in our society. Exposure to it begins at a young age, sometimes even before children have started to walk. is exposure continues as they age and does not stop at adulthood, in fact the majority of game sales occur to those over the age of 18. is field represents a large and growing portion of the entertainment sector of the economy. Games are becoming the highest and most advanced form of escapism as they provide people of all ages the opportunity to see and do things that would not otherwise be possible. Effectively, the nonplayer controlled characters of twenty years ago are the same as the ones in current use, they just look better.

Animal behavior analysis has, in the past, taken a very low tech approach, with direct observer surveillance and automated video surveillance as the norm. These methods are insufficient when one wants to study interactions between large numbers of animals in their housing environment. In this paper we use a housing environment that has been equipped with a system of RFID sensors. RFID transponders were implanted into the study animal, the naked mole rat. The resulting data was analyzed using principal component analysis and frequent pattern mining. Results showed that these methods can identify time periods of high behavioral activity from that of low activity, along with which groups of animals interacted with one another

In this paper, we investigate the application of data mining to existing techniques for quality control/anomaly detection on weather sensor observations. Specifically we adapt the popular Barnes Spatial interpolation method to use time-series distance rather than spatial distance to develop an online algorithm that uses readings from similar stations based on current and historical observations for interpolation and we demonstrate that this new algorithm exhibits less model error than the Barnes Spatial interpolation-based method. We focus on interpolation, which is a basis for this popular quality control method and other related methods, and examine a dataset of over 233 million temperature observations from California and surrounding areas. Our approach shows improved performance as indicated by mean squared error reduced by approximately one half for predicted values versus reported values.

Protecting our nation’s ports is a critical challenge for homeland security and requires the research, development and deployment of new technologies that will allow for the efficient securing of shipments entering this country. Most approaches look only at statistical irregularities in the attributes of the cargo, and not at the relationships of this cargo to others. However, anomalies detected in these relationships could add to the suspicion of the cargo, and therefore improve the accuracy with which we detect suspicious cargo. This paper proposes an improvement in our ability to detect suspicious cargo bound for the U.S. through a graph-based anomaly detection approach. Using anonymized data received from the Department of Homeland Security, we demonstrate the effectiveness of our approach and its usefulness to a homeland security analyst who is tasked with uncovering illegal and potentially dangerous cargo shipments.