1. Making Research Tools Accessible for All AI Students Zach Dodds, Christine Alvarado, and Sara Sood Though a compelling area of research with many applications, emotional reasoning lacks well-documented open-source tools. As a result, undergraduate research tasks involving emotional classification quickly become overwhelmingly complex. To mitigate this problem, we make our Reasoning through Search (RTS) system, with training data and documentation, available to students both for research projects and AI courses. Emotional Reasoning through Search Getting Set with OpenCV Shared Results A Foundation in Pen-based Computing (2) Pushing development details to HW made more classroom time available for course content. (1) Students could complete more ambitious projects at term’s end: early exposure to these tools fostered comfort and creativity. Briefly, RTS uses a combination of Bayesian, case-based Reasoning, and information retrieval approaches to classify text with an accuracy of 78%. Trained on 106,000 movie and product reviews, RTS quantifies the emotional classification of input text as a valence score between -1 and +1. WPF’s environment is unfamiliar to many students: it runs in the.NET platform and uses both XAML and C#. Students also need time to learn the data structures and functions for storing and manipulating ink. To provide this time, our first HW asks students to build a simple drawing application similar to Windows Journal. Creative extensions are encouraged: some students included complex interface controls (above right); others had recognition correction interfaces (above left). Yet all students found WPF a powerful basis for the open-ended projects that were their capstone work in pen-based computing. Starting from Set, students have extended their code to recognize landmarks (left) and to segment paths from surrounding green areas (below), routines used to guide indoor and outdoor robots. OpenCV is a powerful C++-based library of visual routines. Its role in Stanley’s vision systems is one of its claims to fame. Because it is researchware, however, OpenCV is not easy for students to simply “pick up” on their own. Thus, to motivate several facets of the library, we ask students to implement an agent that plays Set, the game of visual perception. Set motivates in part because it highlights the differences between artificial and human intelligence: human players find it hard to ID appropriate triples (sets), but the primary computational challenge lies in correctly identifying the four attributes of each card: it requires color segmentation, connected components, and shape identification. (3) Students interacted with large, real-world data sets from the very beginning of their AI experiences. With the rise of pen-based computing (PBC), fundamental algorithms for working with ink and for recognizing hand-drawn input, such as text, gestures or sketches, have found their way into many courses. For students to focus on the algorithms, they need a platform for handling mundane tasks, such as collecting and displaying users’ hand- drawn strokes. To this end, we have used WPF, the Windows Presentation Foundation. In all three of these cases, A Pomona College Senior leveraged the RTS system in a project that examines blogs, tracks their topics and bloggers’ sentiments, and displays this information. Above is EmoMeters, a visualization that uses the RTS system to analyze a blogger’s sentiment toward different topics – topics that the system discovered on its own. A second display, EmoCloud (right), leveraged RTS’s underlying data to create a tag cloud in which highly emotional terms, judged by the ratio of negative and positive document appearances, orbit their topic. FastSLAM 1.0 Naïve mapping matching pixelsoriginal image overlay