PROGRAM FOR MONDAY, JULY 28TH

ABSTRACT. We report on Shallow Blue (SB), an autonomous chess agent constructed by a small group of faculty and undergraduate students at Canisius College. In addition to pushing the limits of consumer grade components at low cost, SB is a focal point for interdisciplinary student projects spanning computer science, engineering, and physics. We demonstrate that undergraduate students can engage in rich, long-term robotic design and applied Artificial Intelligence (AI) from both hardware and software perspectives. Student outcomes of SB include senior theses, conference presentations, peer-reviewed publications, and admission to graduate programs. Students who participated also report substantial development in skills and knowledge applicable to their post-undergraduate education and careers.

ABSTRACT. Dorothy is an integrated 3D/robotics educational tool created by augmenting the Alice programming environment for teaching core computing skills to students without prior programming experience. The tool provides a drag and drop interface to create graphical routines in virtual worlds; these routines are automatically translated into code to provide a real-time or offline enactment on mobile robots in the real world. This paper summarizes the key capabilities of Dorothy, and describes the contributions made to: (a) enhance the bidirectional communication between the virtual interface and robots; and (b) support multirobot collaboration. Specifically, we describe the ability to automatically revise the virtual world based on sensor data obtained from robots, creating or deleting objects in the virtual world based on their observed presence or absence in the real world. Furthermore, we describe the use of visually observed behavior of teammates for collaboration between robots when they cannot communicate with each other. Dorothy thus helps illustrate sophisticated algorithms for fundamental challenges in robotics and AI to teach advanced computing concepts, and to emphasize the importance of computing in real world applications, to beginning programmers.

ABSTRACT. In this paper, we describe how we integrated the materials from the 2013 IBM The Great Minds Challenge (TGMC) - Watson Technical Edition into our Introductory Artificial Intelligence course. We describe the variety of materials made available by IBM, as well as the nature of the competition and the datasets that are at the heart of it. We detail how, where and in what form we integrated the materials into our course. We describe assignments that are based on the materials from the competition as well as additional materials we incorporated into our course. We finish by evaluating our experience in teaching with the materials as well as summarize relevant student feedback. We make recommendations for those who wish to adopt the materials.

ABSTRACT. Monte Carlo (MC) techniques have become important and pervasive in the work of AI practitioners. In general, they provide a relatively easy means of providing deep understanding of complex systems as long as important events are not infrequent.

ABSTRACT. We present open-ended freshman projects where students design and implement their own player strategies for a commercial board game. The games chosen in previous terms are Quoridor (Gigamic), San Francisco Cable Cars (Queen Games), Gobblet (Blue Orange Games), and The aMAZEing Labyrinth (Ravensburger).

ABSTRACT. This assignment is mainly a programming project, where undergraduate students in an introductory AI class will be asked to write a complete solver for the Strimko puzzle. This project will give students hands-on experience with using propositional logic for knowledge representation, logical inference via resolution, and the use of backtracking search (implementing resolution) for completeness of the solver.

ABSTRACT. Text classification is a classic problem for the use of Naive Bayes with many real-world applications related to spam detection, article categorization, and sentiment analysis. The assignment has students extending their understanding of the basic Naive Bayes classifier, learning how it may be applied to the problem of text classification specifically using a multinomial and Bernoulli model.

ABSTRACT. In an introductory AI course students learn about many informed search strategies as alternatives to the woefully inefficient brute-force search, which represents the extreme lower bound of efficiency.

ABSTRACT. We report on Jim, an inexpensive student designed platform for embodied affective AI. The project brings together students from backgrounds in computer science, physics, engineering, and Digital Media Arts (DMA) in an informal educational setting. The platform will be used in AI courses and autism treatment studies.

ABSTRACT. One of the more important aims of graduate artificial intelligence courses is to prepare graduate students to critically evaluate the current literature. The established approaches for this include either asking a student to present a paper in class, or to have the entire class read and discuss a paper. However, neither of these approaches presents incentives for student participation beyond the posting of a single summary or review. In this paper, we describe a class project that uses the popular Easychair conference management system as a pedagogical tool to enable engagement in the peer review process. We report on the deployment of this project in a medium- sized graduate AI class, and present the results of this deployment. We hope that the success of this project in engaging students in the peer review process can be used better train and bolster the future corps of AI reviewers.

ABSTRACT. Siri is the virtual personal assistant resident inside hundreds of millions of Apple devices. Ask Siri to buy you a movie ticket, make a restaurant reservation, send a message or a tweet, or get the score of the big game and Siri will help you get the job done quickly and easily, through a conversational interaction. People often ask me, "What technology is really behind Siri" and "What's next for Siri?" As a former Apple employee, I'm not at liberty to talk about either of these questions. However, without saying anything related to Apple's system or roadmaps, I can describe the past, explaining what got left "on the cutting room floor" as Siri moved forward from research to commercialization up to an eventual acquisition by Apple. In this talk, I will present the technology and features behind a lineage of systems leading towards Apple's Siri: OAA, Vanguard, CALO, Active, the startup Siri. We will do it in reverse: the farther we go back in time, the more futuristic each version gets, with fantastic capabilities not available in any later version. As Steve Jobs famously said, "You can't connect the dots looking forward, you can only connect them looking backwards..."