The field of artificial intelligence needs to attract new researchers to the field to continue current explorations and look for novel approaches to tomorrow's problems. One approach involves providing students with learning tools that excite their imagination and help them obtain an appreciation for what artificial intelligence can do. The tools described here are used in an undergraduate course at Sam Houston State University. They include heuristic-driven search in a potential game's terrain map, reinforcement learning in a tank battle game, and game tree search techniques in tic-tac-toe.

In this paper, we examine Robot Defense, a computer game that serves as a pedagogical platform for students to explore methods typically covered in an Introductory Artificial Intelligence course. Robot Defense is the synergistic outcome of two NSF funded Course, Curriculum, and Laboratory Improvement (CCLI) projects and was first presented in (Wallace, Russell and Markov 2008). The primary contribution of this paper is to discuss the implementation of the Robot Defense platform and the outcome of its first use in the classroom.

LEGO Mindstorms robots are very popular with colleges and universities for teaching computer concepts and programming.  These robots elicit excitement in students and provide a nontrivial, real-world platform for exploring algorithmic concepts.  We created a simple algorithmic language, called Robolang, and wrote a translator that turns it into Lejos code, a variant of Java that can be run on the RCX version of the LEGO Mindstorms robots.  Seeing that students were eager to explore programming with the RCX robots at home, we wrote a graphical simulator to visualize actions of our penbot, a configuration of the RCX robot that we used in most assignments.  Using an emulator approach, we intercept the ROM calls to the RCX's hardware made by the TinyVM, the stripped-down Java Virtual Machine that runs compiled Java bytecodes.  Our system then forwards these calls to a software model that represents the actual robot hardware.  The software model creates the graphics to mimic the penbot using Java2D. This approach greatly simplified coding by capitalizing on existing software, namely the Java compiler and the JVM.  Students can program either in Robolang or in actual Lejos and use the simulator to visualize the actions of the robot acting as a sort of visual debugger.

Using robots as part of any curriculum requires careful management of the significant complexity that physical embodiment introduces. Students need to be made aware of this complexity without being overwhelmed by it, and navigating students through this complexity is the biggest challenge faced by an instructor.  Achieving this requires a framework that allows complexity to be introduced in stages, as students' abilities improve. Such a framework should also be flexible enough to provide a range of application environments that can grow with student  sophistication, and be able to quickly change between applications.  It should be portable and maintainable, and require a minimum of overhead to manage in a classroom. Finally, the framework should provide repeatability and control for evaluating the students' work, as well as for performing research. In this paper, we discuss the advantages of a mixed reality approach to applying robotics to education in order to accomplish these challenges.  We introduce a framework for managing mixed reality in the classroom, and discuss our experiences with using this framework for teaching robotics and AI.

A reinforcement learning agent involved in life-long learning in a complex and dynamic environment has to have the ability to utilize control knowledge acquired in one situation in novel contexts. As part of this, it is important for the learning agent not only to be able to learn a new skill for a specific instance of a task but also to identify similar tasks, form a reusable skill and representational abstractions for the corresponding ''task type'', and to apply these abstractions in new, previously unseen contexts. This paper presents a new approach to policy generalization that derives an abstract policy for a set of similar tasks (a ''task type'') by constructing a partial policy homomorphism from a set of basic policies learned for previously seen task instances. The resulting generalized policy can then be applied in new contexts to address new instances of similar tasks. As opposed to many recent approaches in lifelong learning systems, this approach allows to identify similar tasks based on the functional characteristics of the corresponding skills and provides a means of transferring the learned knowledge to new situations without the need for complete knowledge of the state space and the system dynamics in the new environment. To illustrate the new policy generalization method and to demonstrate its ability to reuse the gained knowledge in new contexts, it is applied to a set of grid world examples.

Students must be able to competently compose essays in order to succeed in school and progress into the workplace. Current intelligent tutoring systems (ITS) attempt to provide individual training that is lacking in the current educational system. To provide efficient individual training through ITS, the systems must be able to effectively assess writing input from students. Necessary components for computer-based writing tutors are algorithms that mimic human judgments of writing. The current study attempts to establish a connection between paragraph position and human ratings of paragraph type through the use of computational measures provided by Coh-Metrix. We find that expert raters do not easily identify paragraph type and ratings of paragraph type do not map onto paragraph position.

Vincent Aleven Intelligent tutoring systems (ITS) are highly effective in supporting student learning, but are difficult to build. The Cognitive Tutor Authoring Tools (CTAT) project started over 6 years ago with the goals of making it easier for experienced programmers, and possible for non-programmers to create an ITS. CTAT supports tutor building through programming by demonstration, an approach that has been successful in a range of application areas, but that has been applied to only a very limited degree to ITS authoring. Using CTAT, an author creates a tutor by demonstrating correct and incorrect problem solving behaviors, rather than by writing code. The resulting tutors, called exampletracing tutors, evaluate student behavior by flexibly comparing it against the demonstrated problem-solving examples.

Conference and special track committees responsible for the 2009 FLAIRS conference.

List of officers of the FLAIRS society.

This volume contains the papers presented at the 22nd International FLAIRS Conference (FLAIRS-22) held 19-21 May 2009 on Sanibel Island, Florida, USA. The call for papers attracted 158 paper submissions, 40 to the general conference and 118 to the 10 special tracks. Over 80 percent of the papers were reviewed by at least four reviewers, and all papers by at least three. Reviewing was coordinated by the program committees of the general conference and the special tracks. The program committees finally accepted the 85 papers that appear in these proceedings, all as presented papers (21 from the general conference and 64 from the special tracks) and 29 as poster papers (6 from the general conference and 23 from the special tracks).