A new architectural framework for a metacognitive tutoring system is presented that is aimed to stimulate self-regulatory behavior in the learner.The new framework extends the cognitive architecture of TutorJ that has been already proposed by some of the authors. TutorJ relies mainly on dialogic interaction with the user, and makes use of a statistical dialogue planner implemented through a Partially Observable Markov Decision Process (POMDP). A suitable two-level structure has been designed for the statistical reasoner to cope with measuring and stimulating metacognitive skills in the user. Suitable actions have been designed to this purpose starting from the analysis of the main questionnaires proposed in the literature. Our reasoner has been designed to model the relation between each item in a questionnaire and the related metacognitive skill, so the proper action can be selected by the tutoring agent. The complete framework is detailed, the reasoner structure is discussed, and a simple application scenario is presented.

An experiment was conducted to test the efficacy of a new intelligent hypermedia system, MetaTutor, which is intended to prompt and scaffold the use of self-regulated learning (SRL) processes during learning about a human body system. Sixty-eight (N=68) undergraduate students learned about the human circulatory system under one of three conditions: prompt and feedback (PF), prompt-only (PO), and control (C) condition. The PF condition received timely prompts from animated pedagogical agents to engage in planning processes, monitoring processes, and learning strategies and also received immediate directive feedback from the agents concerning the deployment of the processes. The PO condition received the same timely prompts, but did not receive any feedback following the deployment of the processes. Finally, the control condition learned without any assistance from the agents during the learning session. All participants had two hours to learn using a 41-page hypermedia environment which included texts describing and static diagrams depicting various topics concerning the human circulatory system. Results indicate that the PF condition had significantly higher learning efficiency scores, when compared to the control condition. There were no significant differences between the PF and PO conditions. These results are discussed in the context of development of a fully-adaptive hypermedia learning system intended to scaffold self-regulated learning.

Successful learning with advanced learning technologies is based on the premise that learners adaptively regulate their cognitive and metacognitive behaviors during learning. However, there is abundant empirical evidence that suggests that learners typically do not adaptively modify their behavior, thus suggesting that they engage in what is called dysregulated behavior. Dysregulated learning is a new term that is used to describe a class of behaviors that learners use that lead to minimal learning. Examples of dysregulated learning include failures to: (1) encode contextual demands, (2) deploy effective learning strategies, (3) modify and update internal standards, (4) deal with the dynamic nature of the task, (5) metacognitive monitor the use of strategies and repeatedly make accurate metacognitive judgments, and (6) intelligently adapt behavior during learning so as to maximize learning and understanding of the instructional material. Understanding behaviors associated with dysregulated learning is critical since it has implications for determining what they are, when they occur, how often they occur, and how they can be corrected during learning.

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified.

Concept drift refers to a non stationary learning problem over time. The training and the application data often mismatch in real life problems. In this report we present a context of concept drift problem 1. We focus on the issues relevant to adaptive training set formation. We present the framework and terminology, and formulate a global picture of concept drift learners design. We start with formalizing the framework for the concept drifting data in Section 1. In Section 2 we discuss the adaptivity mechanisms of the concept drift learners. In Section 3 we overview the principle mechanisms of concept drift learners. In this chapter we give a general picture of the available algorithms and categorize them based on their properties. Section 5 discusses the related research fields and Section 5 groups and presents major concept drift applications. This report is intended to give a bird's view of concept drift research field, provide a context of the research and position it within broad spectrum of research fields and applications.

There is growing body of learning problems for which it is natural to organize the parameters into matrix, so as to appropriately regularize the parameters under some matrix norm (in order to impose some more sophisticated prior knowledge). This work describes and analyzes a systematic method for constructing such matrix-based, regularization methods. In particular, we focus on how the underlying statistical properties of a given problem can help us decide which regularization function is appropriate. Our methodology is based on the known duality fact: that a function is strongly convex with respect to some norm if and only if its conjugate function is strongly smooth with respect to the dual norm. This result has already been found to be a key component in deriving and analyzing several learning algorithms. We demonstrate the potential of this framework by deriving novel generalization and regret bounds for multi-task learning, multi-class learning, and kernel learning.

Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning problem, which makes them inadequate for large scale scenarios. We propose a new family of online proximal algorithms for MKL (as well as for group-lasso and variants thereof), which overcomes that drawback. We show regret, convergence, and generalization bounds for the proposed method. Experiments on handwriting recognition and dependency parsing testify for the successfulness of the approach.

So, we have a variety of people here with different interests and backgrounds that I asked to talk about not just the key challenges ahead but potential opportunities and promising pathways, trajectories to solving those problems, and their predictions about how R&D might proceed in terms of the timing of various kinds of development over time. I asked the panelists briefly to frame their comments sharing a little bit about fundamental questions, such as, "What is the research goal?" Not everybody stays up late at night hunched over a computer or a simulation or a robotic system, pondering the foundations of intelligence and human-level AI. We have here today Lise Getoor from the University ipate the liability and insurance industry; and the of Maryland; Devika Subramanian, who other one, that it was a human interface problem, comes to us from Rice University; we have Carlos that people don't necessarily want to go and type Guestrin from Carnegie Mellon University (CMU); a bunch of yes/no questions into a computer to get James Hendler from Rensselaer Polytechnic Institute an answer, even with a rule-based explanation, (RPI); Mike Wellman at the University of that if you'd taken that just a step further and Michigan; Henry Kautz at tjhe University of solved the human problem, it might have worked. Rochester; and Joe Konstan, who comes to us from Related to that, I was remembering a bunch of the Midwest, as our Minneapolis person here on these smart house projects. And I have to admit I the panel. I think everyone Joe Konstan: I was actually surprised when you hates smart spaces. I think of myself at the core there's nobody there, do you warn people and give in human-computer interaction. So I went back them a chance to answer? There's no good answer and started looking at what I knew of artificial to this question. I can tell you if that person is in intelligence to try to see where the path forward bed asleep, the answer is no, don't wake them up was, and I was inspired by the past.

Recent years have seen a growing interest in interactive narrative systems that dynamically adapt story experiences in response to users’ actions, preferences, and goals. However, relatively little empirical work has investigated runtime models of user knowledge for informing interactive narrative adaptations. User knowledge about plot scenarios, story environments, and interaction strategies is critical in a range of interactive narrative contexts, such as mystery and detective genre stories, as well as narrative scenarios for education and training. This paper proposes a dynamic Bayesian network approach for modeling user knowledge in interactive narrative environments. A preliminary version of the model has been implemented for the Crystal Island interactive narrative-centered learning environment. Results from an initial empirical evaluation suggest several future directions for the design and evaluation of user knowledge models for guiding interactive narrative generation and adaptation.

The Prom is a game where the player manages the social life of a group of high school students and creates the situations from which dramatic, thought provoking or at least funny stories can unfold. The setting of The Prom involves a group of alternative high school kids (e.g. Emos, Goths, Geeks, etc.) and their dramatic lives as they prepare for the upcoming school prom. Through creating friendships, making people become enemies, controlling who gets to be in the "in" crowd and much more, the player can shape the social world of the characters. Each character has a distinct personality represented by interests (e.g. what bands they like), needs (e.g. a character may need to demonstrate a certain degree of dominance over others), traits (e.g. being a particularly jealous person), social networks (e.g. to what degree a characters like, are attracted to or respect one another) and social status (e.g. who is dating who).The social artificial intelligence system Comme il Faut ( CiF ) drives this gameplay experience by simulating per character needs and traits, social statuses, social networks, social history and most importantly to gameplay, the outcomes and effects of social games. CiF is a playable computational model of social interactions designed specifically to allow autonomous characters to play social games. By giving player controls to navigate a social, rather than physical, space, The Prom is being created to demonstrate how CiF and social games can create a practically limitless numbers of possibly compelling stories and gameplay.