In this paper we describe the development and evaluation of AITK, the Artificial Intelligence Toolkit. This open-source project contains both Python libraries and computational essays (Jupyter notebooks) that together are designed to allow a diverse audience with little or no background in AI to interact with a variety AI tools, exploring in more depth how they function, visualizing their outcomes, and gaining a better understanding of their ethical implications. These notebooks have been piloted at multiple institutions in a variety of humanities courses centered on the theme of responsible AI. In addition, we conducted usability testing of AITK. Our pilot studies and usability testing results indicate that AITK is easy to navigate and effective at helping diverse users gain a better understanding of AI and its ethical implications. Our goal, in this time of rapid innovations in AI, is for AITK to provide an accessible resource for faculty from any discipline looking to incorporate AI topics into their courses and for anyone eager to learn more about AI on their own.

This workshop is a hands-on exploration of Deep Learning techniques and topics for use in the classrooms of Computer Science and related fields. Deep Learning denotes the latest in a series of advances in neural network training algorithms and hardware that allow Artificial Neural Networks (ANNs) to learn quickly and effectively, even with many, stacked layers. These types of networks can be applied to almost any learning problem, such as driving a car, describing images, controlling a robot, or understanding language. This workshop will start with the mathematical and algorithmic foundations of Deep Learning, and introduce an accessible Python-based library, called "conx," which is based on the Keras library and was developed by the workshop instructors. The workshop will demonstrate ideas through animation and visualizations, examine the path to advanced topics, and explore ideas for incorporating Deep Learning topics into the classroom. The workshop is designed to allow participants to gain a foothold with these complex topics, and to help them develop their own materials for teaching. Workshop materials will be made freely available before the workshop as Jupyter notebooks.

In line with institutions across the United States, the Computer Science Department at Swarthmore College has faced the challenge of maintaining a demographic composition of students that matches the student body as a whole. To combat this trend, our department has made a concerted effort to revamp our introductory course sequence to both attract and retain more women and minority students.

Developmental robotics is a broad, new discipline that lies at the intersections of psychology, biology, artificial intelligence (AI) and robotics. This new field was inspired by the fact that most...

We propose an intrinsic developmental algorithm that is designed to allow a mobile robot to incrementally progress through levels of increasingly sophisticated behavior. We believe that the core ingredients for such a developmental algorithm are abstractions, anticipations, and self-motivations. We describe a multilevel, cascaded discovery and control architecture that includes these core ingredients. As a first step toward implementing the proposed architecture, we explore two novel mechanisms: a governor for automatically regulating the training of a neural network and a path-planning neural network driven by patterns of “mental states” that represent protogoals.

In this article we describe a programming framework called Pyro, which provides a set of abstractions that allows students to write platform-independent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a top-down approach. We describe the background of the project, its novel abstractions, its library of objects, and the many learning modules that have been created from which curricula for different types of courses can be drawn. Finally, we explore Pyro from the students' perspective in a case study.

Male and female students often hold different views of the culture within the same computer science department. These differences may, in part, account for why women are underrepresented in computer science. We found that surveying students about their views of our departments' environments was an important first step in evaluating the cultures of our own departments, in determining what issues needed to be addressed, and in determining how to address them. Our survey results revealed some problems in our classroom and lab environments, and showed that there are gender differences in students' perceptions of our departments. We describe a set of changes that were implemented in response to our findings. These solutions are specifically designed to address problems that we discovered through our student survey, but they are not all original to us. The contribution of our work is in demonstrating how surveying is critical to identifying and understanding problems in our departments. We argue that a process of continually surveying students is vital to the maintenance and evolution of a healthy computer science program.

This paper describes Pyro, a robotics programming environment designed to allow inexperienced undergraduates to explore topics in advanced robotics. Pyro, which stands for Python Robotics, runs on a number of advanced robotics platforms. In addition, programs in Pyro can abstract away low-level details such that individual programs can work unchanged across very different robotics hardware. Results of using Pyro in an undergraduate course are discussed.

In this article we describe a programming framework called Pyro, which provides a set of abstractions that allows students to write platform-independent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a top-down approach. We describe the background of the project, its novel abstractions, its library of objects, and the many learning modules that have been created from which curricula for different types of courses can be drawn. Finally, we explore Pyro from the students' perspective in a case study.

We present our experience using a large, real-world application as a course project for the second half of the semester of a CS2 course. Our primary goal for the project was to create an engaging application that incorporated most of the key data structures and algorithms introduced in the course. Specifically, the project uses binary search trees, priority queues, hash tables, and graphs. The project consisted of four parts combined to build an integrated web browser and search engine in Java. A key benefit of an incremental, long-term project of this type is that students quickly learn that their initial design and implementation decisions have a significant impact on the eventual extensibility and performance of their software. This provides numerous opportunities for students to recognize the importance of software engineering techniques and complexity analysis in the development of a successful application. We present students' responses to the project which show that they overwhelmingly enjoyed the project and felt that it helped them to see how the data structures and algorithms discussed in the course are used in real software.

No abstract available.

There is a growing consensus among computer science faculty that it is quite difficult to teach the introductory course on Artificial Intelligence well [4, 6]. In part this is because AI lacks a unified methodology, overlaps with many other disciplines, involves a wide range of skills from very applied to quite formal. In the funded project described here we have addressed these problems Offering a unifying theme that draws together the disparate topics of AI; Focusing the course syllabus on the role AI plays in the core computer science curriculum; and Motivating the students to learn by using concrete, hands-on laboratory exercises.Our approach is to conceive of topics in AI as robotics tasks. In the laboratory, students build their own robots program them to accomplish the tasks. By constructing a physical entity in conjunction with the code to control it, students have a unique opportunity to directly tackle many central issues of computer science including the interaction between hardware software, space complexity in terms of the memory limitations of the robot's controller, time complexity in terms of the speed of the robot's action decisions. More importantly, the robot theme provides a strong incentive towards learning because students want to see their inventions succeed.This robot-centered approach is an extension of the agent-centered approach adopted by Russell Norvig in their recent text book [11]. Taking the agent perspective, the problem of AI is seen as describing building agents that receive perceptions as input then output appropriate actions based on them. As a result the study of AI centers around how best to implement this mapping from perceptions to actions. The robot perspective takes this approach one step further; rather than studying software agents in a simulated environment, we embed physical agents in the real world. This adds a dimension of complexity as well as excitement to the AI course. The complexity has to do with additional demands of learning robot building techniques but can be overcome by the introduction of kits that are easy to assemble. Additionally, they are lightweight, inexpensive to maintain, programmable through the standard interfaces provided on most computers, yet, offer sufficient extensibility to create experiment with a wide range of agent behaviors. At the same time, using robots also leads the students to an important conclusion about scalability: the real world is very different from a simulated world, which has been a long standing criticism of many well-known AI techniques.We proposed a plan to develop identical robot building laboratories at both Bryn Mawr Swarthmore Colleges that would allow us to integrate the construction of robots into our introductory AI courses. Furthermore, we hoped that these laboratories would encourage our undergraduate students to pursue honors theses research projects dealing with the building of physical agents.

By beginning with simple reactive behaviors and gradually building up to more memory-dependent behaviors, it may be possible for connectionist systems to eventually achieve the level of planning. This paper focuses on an intermediate step in this incremental process, where the appropriate means of providing guidance to adapting controllers is explored. A local and a global method of reinforcement learning are contrasted-a special form of back-propagation and an evolutionary algorithm. These methods are applied to a neural network controller for a simple robot. A number of experiments are described where the presence of explicit goals and the immediacy of reinforcement are varied. These experiments reveal how various types of guidance can affect the final control behavior. The results show that the respective advantages and disadvantages of these two adaptation methods are complementary, suggesting that some hybrid of the two may be the most effective method. Concluding remarks discuss the next incremental steps toward more complex control behaviors.