This tutorial for CS0/1/2 instructors will explore how to use the Vizard IDE from Worldviz to teach CS topics via virtual reality projects in Python. Participants will work with free versions of the Vizard IDE to learn how to motivate concepts ranging from CS-0-1-2 topics such as iteration, data structures, and object orientation, to topics in virtual reality and 3D data visualization. This tutorial assumes no background in virtual reality or game design, but does assume knowledge of Python. Participants must bring their own laptops. Vizard runs on Windows platforms only. It also runs well on common Windows emulators on MacOS laptops. Headsets are optional. Three laptops with a headset will be made available for participants to preview sample projects. Participants will receive a set of sample programs discussed in the tutorial.

This workshop will conduct an exploration of the newly released Lego Mindstorms EV3 robot platform and its applicability to the college computer science curriculum. Participants will learn about the EV3 through handouts and hands-on programming exercises. The first part of the workshop will focus on demonstrating EV3 robots as well as the STEM concepts and computing concepts they illustrate. The second part of the workshop will focus in on the new capabilities of the EV3. This workshop will be more detailed than the vendor led workshop. It will be of benefit to participants new to Mindstorms robotics as well as those with NXT/RCX experience who want to see the evolution of the platform and new components featured in the EV3. These include a revision of the controller brick hardware and software, new color and gyroscopic sensors, and increased processing and memory capabilities. The organizers have a combined 20 years of experience using Mindstorms in CS courses (including courses in introductory programming, systems, and artificial intelligence) with Lego and third party programming environments. Participants must bring a Bluetooth-capable laptop (Mac or Windows), and will have a robot, software, and kit to use for the workshop. Participants will receive a promotion code to purchase a 10% discounted Mindstorms kit.

This paper examines the applicability of LEGO Mindstorms to the intermediate-level computer science curriculum, which traditionally includes courses such as Computer Organization and Operating Systems. We describe three open-laboratory projects that employ Mindstorms in exploring systems-level programming topics and present results from student evaluations of the projects over the last two years. An interesting feature of the projects is that they do not involve robotics or robot construction but instead focus on the Mindstorms programmable brick as a platform in its own right.

No abstract available.

This paper presents the RCXLisp library, an extension to Common Lisp that allows students to investigate a broad range of artificial intelligence and computer science topics using the LEGO MindStorms® platform. The library has two features that distinguish it from other third-party packages and languages designed by academics and hobbyists for programming the MindStorms platform. The first is that it supports both remote control and on-board programming of MindStorms robots. The second is that it supports targeted communication between multiple LEGO robots and command-center desktops. The package is also the only one to be integrated into most popular Common Lisp programming environments. This paper also summarizes student experiences with the package over the years 2000-2003 in an Artificial Intelligence course.

This paper examines LEGO Mindstorms'™ suitability as a hardware platform for integrating robotics into an Artificial Intelligence course organized around the agent paradigm popularized by Russell and Norvig. This evaluation discusses how kits and projects based on Mindstorms supported students' exploration of the issues behind the design of agents from three classes in Russell and Norvig's intelligent agent taxonomy. The paper's investigation also examines several popularly-perceived limitations of the Mindstorms package for college-level robotics projects and shows that most of these "limitations" are not serious impediments to Mindstorms' use, while certain other of these "limitations" do indeed present challenges to the platform's use.

Using the robot as a metaphor for assisting students in understanding problem solving in general, the algorithmic process, and the relationship between algorithms and computing agents is not new. While simulated robot environments have existed for many years (e.g. Karel the Robot[3]) it is only recently that the technology for inexpensively supplying undergraduates with real robots has become available. Lego Mindstorms, MIT Handyboards, the Rug Warrior, and others are examples of such systems. Programmable in familiar languages, including C, Ada, and Java, these systems allow for the creative exploration of important computer science concepts. Representing a variety of institution types the panelists will discuss their experiences in using hands-on robot-based projects for illustrating various important computer science concepts.

The Integrated Processing and Understanding of Signals (IPUS) architecture is presented as a framework that exploits formal signal processing models to structure the bidirectional interaction between front-end signal processing and signal understanding processes. This architecture is appropriate for complex environments, which are characterized by variable signal-to-noise ratios, unpredictable source behaviors, and the simultaneous occurrence of objects whose signal signatures can distort each other. A key aspect of this architecture is that front-end signal processing is dynamically modifiable in response to scenario changes and to the need to reanalyze ambiguous or distorted data. The architecture tightly integrates the search for the appropriate front-end signal processing configuration with the search for plausible interpretations. In our opinion, this dual search, informed by formal signal processing theory, is a necessary component of perceptual systems that must interact with complex environments. To explain this architecture in detail, we discuss examples of its use in an implemented system for acoustic signal interpretation.