In the last years, knowledge technologies have been exploited for self-regulation functionalities inside e-learning systems. The definition of integrated system suitably scaffolding learners to improve their experi- ence is still lacking though. In this work, we propose an innovative Web-based educational environment that sustains metacognitive self-regulated learning processes upon Semantic Web and Social Web methods and technologies.

Our learning by teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is structured so that successfully instructing their teachable agent requires the students to learn and understand science topics for themselves. This process is supported by adaptive scaffolding and feedback from the system. This feedback is instantiated through the interactions with the teachable agent and a mentor agent, named Mr. Davis. This paper provides an overview of two studies that were conducted with 5th grade science students and a description of the analysis techniques that we have developed for interpreting students’ activities in this learning environment.

Developing the capabilities of children to comprehend written texts is key to their development as young adults. Text comprehension skills develop enormously from the age of 7- 8 until the age of 11. Nowadays, several young children (˜5% – 10% of novice readers) turn out to be poor (text) comprehenders: they demonstrate text comprehension difficulties, related to inference-making skills, despite proficiency in lowlevel cognitive skills like word decoding. Though there are several pencil-and-paper reading interventions for improving inference-making skills on text, and addressed to poor comprehenders, the design and evaluation of Adaptive Learning Systems (ALSs) are lagging behind. The use of more intelligent ALSs to custom-tailor such interventions in the form of games for poor comprehenders has tremendous potential. Our system embodies that potential. This paper presents the design of our ALS by focusing on its intelligent adaptive engine and the related conceptual models, and by presenting the visual interfaces for story telling and gaming.

A method is presented for training an Input-Output Hidden Markov Model (IOHMM) to identify a player's current goal in an action-adventure game. The goals were Explore, Fight, or Return to Town, which served as the hidden states of the IOHMM. The observation model was trained by directing the player to achieve particular goals and counting actions. When trained on first-time players, training to the specific players did not appear to provide any benefits over a model trained to the experimenter. However, models trained on these players' subsequent trials were significantly better than the models trained to the specific players the first time, and also outperformed the model trained to the experimenter. This suggests that game goal recognition systems are best trained after the players have some time to develop a style of play. Systems for probabilistic reasoning over time could help game designers make games more responsive to players' individual styles and approaches.

The second Future of Humanity Institute on AGI and keynote was by Tecnalia neuroscientist Randal possible paths to technological singularity. Koene, who also gave a tutorial on the connection While the community of AGI researchers is between reinforcement learning models in AI and nowhere near a consensus on the best approach to in computational neuroscience. Koene's keynote the original, grand goal of the AI field, it's clear focused on technologies enabling detailed brain that the pursuit of the goal is alive and well, and imaging and whole-brain emulation and on the yielding interesting discoveries and discussions.

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.

Wikis are being increasingly used as a tool for conducting colla-borative writing assignments in today’s classrooms. However, Wikis in general (1) do not provide group formation methods to more specifically facilitate collaborative learning of the students and (2) suffer from typical problems of collaborative learning like detection of free-riding (earning credit without contribution). To improve the state of the art of the use of Wikis as a collaborative writing tool, we have designed and implemented ClassroomWiki - a Web-based collaborative Wiki that utilizes a set of learner pedagogy theories to provide multiagent-based tracking, modeling, and group formation functionalities. For the students, ClassroomWiki provides a Web interface for writing and revising their group’s Wiki and a topic-based forum for discussing their ideas during collaboration. When the students collaborate, ClassroomWiki’s agents track all student activities to learn a model of the students and use a Bayesian Network to learn a probabilistic mapping that describes the ability of a group of students with a specific set of models to work together. For the teacher, Clas-sroomWiki provides a framework that uses the learned student models and the mapping to form student groups to improve the collaborative learning of students. ClassroomWiki was deployed in three university-level courses and the results suggest that ClassroomWiki can (1) form better student groups that improve stu-dent learning and collaboration and (2) alleviate free-riding and allow the instructor to provide scaffolding by its multiagent-based tracking and modeling.

NPCEditor is a system for building a natural language processing component for virtual humans capable of engaging a user in spoken dialog on a limited domain. It uses a statistical language classification technology for mapping from user's text input to system responses. NPCEditor provides a user-friendly editor for creating effective virtual humans quickly. It has been deployed as a part of various virtual human systems in several applications.

This My thesis work combines AI, programming language design, incompleteness of perception and dynamism in the environment and software engineering. I am integrating reinforcement creates a strong need for adaptivity. Programming this learning (RL) into a programming language so adaptivity by hand in a language that does not provide builtin that the language achieves three primary goals: accessibility, support for adaptivity is very cumbersome. As I demonstrated adaptivity, and modularity. If I am successful, my or designer specifies the structure of certain parts work will enable a discipline of modular large-scale agent of a program while leaving other portions unspecified, such software engineering while making advanced agent modeling that a learning system can learn how to perform them.

The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the Pac- Man environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.