Researchers have established the value of hands-on learning with tangible artifacts in mathematics and related fields. Inspired by this work, an assignment was developed for an undergraduate/graduate Artificial Intelligence course to introduce students to the formal representation of search. Students analyzed a familiar board game — e.g., Rush Hour or peg solitaire — using the standard approach to modeling an uninformed search process. The assignment was well-received by students, and analysis of their work yielded unexpected insights into the challenges students face in understanding how the formal problem model interacts with search algorithms. This paper introduces the theoretical motivations for the work, analyzes student work products, and makes recommendations for future extensions.

We describe a robot system that autonomously acquires skills through interaction with its environment. The robot learns to sequence the execution of a set of innate controllers to solve a task, extracts and retains components of that solution as portable skills, and then transfers those skills to reduce the time required to learn to solve a second task.

We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called "selection diagrams" for expressing knowledge about differences and commonalities between environments and, using this representation, we derive procedures for deciding whether effects in the target environment can be inferred from experiments conducted elsewhere. When the answer is affirmative, the procedures identify the set of experiments and observations that need be conducted to license the transport. We further discuss how transportability analysis can guide the transfer of knowledge in non-experimental learning to minimize re-measurement cost and improve prediction power.

A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number ofclusters in mixture models or the number of factors in factor analysis. In this tutorial we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.

Cluedo) serves as a fun when it comes to designing an optimal (or even practicable) focus problem for this introduction to propositional knowledge solution. The potential solutions also touch on many representation and reasoning. After covering fundamentals areas of AI, so the students can be creative in applying and of propositional logic, students first solve basic synthesizing what they've learned to a new problem. The logic problems with and without the aid of a satisfiability three challenges give the students the opportunity to choose solver (e.g.

Integrating games into the computer science curriculum has been gaining acceptance in recent years, particularly when used to improve student engagement in introductory courses. This paper argues that games can also be useful in upper level courses, such as general artificial intelligence and machine learning. We provide a case study of using a Mario game in a machine learning class to provide one successful data point where both content-specific and general learning outcomes were successfully achieved.

We introduce a Java graphical gaming framework that enables students in an introductory artificial intelligence (AI) course to immediately apply and visualize the topics from class. We have used this framework in teaching a mixed undergraduate/graduate AI course for six years. We believe that the use of games motivates students. The graphical nature of each game enables students to quickly see how well their algorithm works. Because the topics in an introductory AI course vary widely, students apply their algorithms to multiple game environments. A final challenging environment enables them to tie together the concepts for the entire semester.

The undergraduate computer science curriculum is generally focused on skills and tools;  most students are not exposed to much  research in the field, and do not learn how to navigate the research literature.  We describe how science fiction reviews were used as a gateway to research reviews.  Students learn a little about current or recent research on a topic that stirs their imagination, and learn how to search for, read critically, and compare technical papers on a topic related their chosen science fiction book, movie, or TV show.

The initial idea of using Lego Mindstorms Robots for student courses had soon to be expanded to a simulation environment as the user base in students grew larger and the need for parallel development and testing arose. An easy to use and easy to set up means of providing positioning data led to the creation of an indoor positioning system so that new users can adapt quickly and successfully, as sensors on the actual robots are difficult to configure and hard to interpret in an environmental context. A global positioning system shared among robots can make local sensors obsolete and still deliver more precise information than currently available sensors, also providing the base necessary for the robots to effectively work on shared tasks as a group. Further more, a simulator for robots programmed with Fujaba and Java which was developed along the way can be used by many developers simultaneously and lets them evaluate their code in a simple way, while close to real-world results.

That drone and its basic capabilities are summarized in Figure 1. The flexibility and controllability of quadrotor helicopters have made them a recent focus of interest among robotics and AI research groups. At the same time, their popularity has led to a wide range of commercially available platforms, some at prices accessible for undergraduate educational use. This project evaluates the ARDrone quadrotor helicopter as a basis for use in undergraduate classes such as robotics, computer vision, or embodied AI. We have encountered both successes and frustrations in using the ARDrone to date. Looking forward, the quadrotor's capabilities do seem a promising basis for future curricular offerings.