Total: 14
The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2017 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.
There are many benefits to introducing students to the idea of doing projects where the outcome is unknown or unsure. Some have proposed that engaging students in research can help with retention of underrepresented groups. In this paper, we report on a particular approach we have used to introduce high school students to open-ended robotics projects in a three-week summer program. We describe the structure of our summer program, how we ramp the students up to speed, and we summarize the five open-ended "research" projects that the students work on. These projects can be adopted for open-ended work elsewhere by high school students or undergraduates.
In this paper we present the game of cornhole as a compelling, accessible, and adaptable AI robotics task. Cornhole is a fun and social game with simple rules, but involves strategy and physical training for humans to play competitively; thus, developing a robot that can play at the level of even the average human player presents a multitude of opportunities for curricular integration at a variety of levels. We characterize the AI tasks involved with the game, and present results and resources gained from preliminary offerings.
The Grace Hopper conference has many lectures/activities for participants. Tech Node presentations at this conference are two hours and focus on encouraging open discussion around a topic. This "not so grand" challenge, originally created for this conference, requires participants to brainstorm a robot creation that could somehow improve society in one of four societal areas: Elder Care (non- medical), Search and Rescue, Environment, and Affordable Home Health Care. This project format also can be used as an unplugged activity for a CS0/CS1 class or as a more advanced project that employs image processing and AI techniques such as machine learning.
The Robotics Program at Oregon State University has beenrunning an NSF-funded summer Research Experiences forUndergraduates (REU) site since 2014. Over twenty studentsper year (on average) have participated in the site, spendingten weeks embedded in the OSU Robotics Program. Our mainfocus with this REU Site is to give the participants a com-plete research experience, from problem definition to the fi-nal presentation of results, "in miniature". Our secondary ed-ucational objectives are: 1) Teach basic non-technical skillsneeded for graduate work, such as time management and lit-erature review, 2) Provide details on how to apply to gradu-ate school and for funding, 3) Clarify what we look for in agraduate student, and 4) Detail what to expect from the grad-uate student experience. In this paper, we describe the over-all structure of the participants’ summer experience, outlinesome of the training materials that we use, describe the moti-vations for our approach, and discuss the lessons that we havelearned after running the program for a number of years.
I present a robotics localization challenge based on the inexpensive Neato XV robotic vacuum cleaner platform. The challenge teaches skills such as computational modeling, probabilistic inference, efficiency vs. accuracy tradeoffs, debugging, parameter tuning, and benchmarking of algorithmic performance. Rather than allowing students to pursue any localization algorithm of their choosing, here, I propose a challenge structured around the particle filter family of algorithms. This additional scaffolding allows students at all levels to successfully implement one approach to the challenge, while providing enough flexibility and richness to enable students to pursue their own creative ideas. Additionally, I provide infrastructure for automatic evaluation of systems through the collection of ground truth robot location data via ceiling-mounted location tags that are automatically scanned using an upward facing camera attached to the robot. The robot and supporting hardware can be purchased for under $400 dollars, and the challenge can even be run without any robots at all using a set of recorded sensor traces.
Prerequisite relations among concepts play an important role in many educational applications such as intelligent tutoring system and curriculum planning. With the increasing amount of educational data available, automatic discovery of concept prerequisite relations has become both an emerging research opportunity and an open challenge. Here, we investigate how to recover concept prerequisite relations from course dependencies and propose an optimization based framework to address the problem. We create the first real dataset for empirically studying this problem, which consists of the listings of computer science courses from 11 U.S. universities and their concept pairs with prerequisite labels. Experiment results on a synthetic dataset and the real course dataset both show that our method outperforms existing baselines.
Visual sensing can be difficult to incorporate into undergraduate robotics and AI assignments. Images, after all, do not provide a direct estimate of the geometric conditions within the field of view. Yet vision is increasingly compelling as a part of undergraduate AI and robotics, given the centrality of pixels in our students' interactions with technology and each other. This paper shares a small-footprint framework designed to make visual sensing as easy to incorporate into AI projects and assignments, e.g., as a source of evidence for localization algorithms, as range sensors. The framework leverages (hand-built) circular panoramas and the image-matching capabilities provided by OpenCV's python library. An example localization project highlights its pedagogical accessibility and ease of deployment atop low-cost hardware and alongside other sensors.
ARTY is a week-long program for middle school students to teach them programming of robots and allow them to express themselves artistically. It was started in 2013 and ran its fourth edition in 2016. We describe the ideas behind the inception of this program, its curriculum, our experiences during the 2016 workshop and challenges/future directions for the program. Our primary intent in this paper is to convey the program curriculum and its design, including the way in which robots can be viewed as vehicles for artistic expression. Some results from a brief attitudinal survey that was administered before and after the workshop are also included along with a discussion of outcomes assessment and issues.
Monte Carlo Localization (MCL) is a sampling-based algorithm for mobile robot localization. In this paper we describe an MCL assignment and its required hardware and software. The Neato vacuum robot and a Raspberry Pi serve as the core of the robot model. The Robot Operating System (ROS) is used as the robot programming environment. Students are expected to learn the localization problem, implement the MCL algorithm, and better understand the kidnapped robot problem and the limitations of MCL by observing the performance of the algorithm in real-time application.
We developed a method to draw and animate using SPARC, a logic programming system, and an online environment to support this method.Particularly, we introduce two predicates: one for drawing and one for animation. By our method, programmers will write a SPARC program, using our introduced predicates, to specify their drawing or animation. The drawing or animation will then be rendered upon executing the program with our system. In fact, our online system provides an environment where the programmers can easily edit and execute their programs.
Capstone senior design projects provide students with a collaborative software design and development experience to reinforce learned material while allowing students latitude in developing real-world applications. Our two-semester capstone classes are required for all computer science majors. Students must have completed a software engineering course — capstone classes are typically taken during their last two semesters. Project proposals come from a variety of sources, including industry, WSU faculty (from our own and other departments), local agencies, and entrepreneurs. We have recently targeted projects in AI — although students typically have little background, they find the ideas and methods compelling. This paper outlines our instructional approach and reports our experiences with three projects.
The purpose of this project is to provide instructions for teaching the Artificial Intelligence topic of supervised machine learning for the task of Optical Character Recognition (OCR) at various levels of a student’s undergraduate curriculum, such as basic knowledge, novice, and intermediate. The levels vary from beginner with a slight background in computing and computer science to intermediate with a better understanding of computer science fundamentals and algorithms.
This demonstration showcases the different use cases of Artificial Intelligence (AI) in education by introducing students to applications of the Scribbler robot with the Fluke board in order to cultivate an interest in programming, robotics, and AI. The targeted audience for this is students aged eight through twelve. This demonstration uses three Scribbler robots to introduce students to common tools in AI (OpenCV and Tesseract), and teach them the basics of coding in an interactive, unintimidating way; by physically describing the goals of simple shape-building algorithms and implementing them using cards with both visual and written representations of the instructions.