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 five AI assignments from the 2014 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at modelai.gettysburg.edu.

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.

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.

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.

In this article supervised learning problems are solved using soft rule ensembles. First, we review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. Soft rules are obtained with logistic regression using the corresponding hard rules and training data. Soft rule ensembles work well when both the response and the input variables are continuous because soft rules provide smooth transitions around the boundaries of hard rules. Finally, various examples and simulation results are provided to illustrate and evaluate the performance of soft rule ensembles.

Computer Science researchers have long sought ways to apply the fruits of their labors to education. From the Logo turtles to the latest Cognitive Tutors, the allure of computers that will understand and help humans learn and grow has been a constant thread in Artificial Intelligence research. Now, advances in robotics and our understanding of Human-Robot Interaction make it feasible to develop physically-present robots that are capable of presenting educational material in an engaging manner, adapting online to sensory information from individual students, and building sophisticated, personalized models of a student's mastery over complex educational domains. In this paper, we discuss how using physical robots as platforms for artificially intelligent tutors enables an expanded space of possible educational interactions. We also describe a work-in-progress to (1) extend previous work in personalized user models for robotic tutoring and (2) further explore the differences between interaction with physical robots and onscreen agents. Specifically, we are examining how embedding an tutoring interaction inside a story, game, or activity with an agent may differentially affect learning gains and engagement in interactions with physical robots and screen-based agents.

Socially assistive robotics (SAR) has the potential to combinethe massive replication and standardization of computertechnology with the benefits of learning in a social and tangible(hands-on) context. We are developing HRI methodsfor SAR systems designed to supplement the efforts of humanteachers to personalize education in the classroom. Thisabstract defines and proposes solutions to the computationalchallenges inherent in accomplishing differentiated and personalizededucation utilizing SAR in real-world classrooms. We aim to design robotic systems that are compelling, assistchildren in achieving educational goals, and mitigate developmentalchallenges in a classroom context. To do so, ourapproach must be deeply informed by the needs of our targetusers, children, at all stages of development, and mustadapt to a variety of special needs.

The goal of this research is to enable robots to learn new things from everyday people. For years, the AI and Robotics community has sought to enable robots to efficiently learn new skills from a knowledgeable human trainer, and prior work has focused on several important technical problems. This vast amount of research in the field of robot Learning by Demonstration has by and large only been evaluated with expert humans, typically the system's designer. Thus, neglecting a key point that this interaction takes place within a social structure that can guide and constrain the learning problem.

In this position paper, we propose a game theoretic formulation of the adversarial learning problem called a RepeatedBayesian Stackelberg Game (RBSG) that can be used by aprediction mechanism to make itself robust against adversarial examples.

One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality.