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Teaching artificial intelligence is effective if the experience is a visual and interactive one, with educational materials that utilize combinations of various content types such as text, math, and code into an integrated experience. Unfortunately, easy-to-use tools for creating such pedagogical resources are not available to the educators, resulting in most courses being taught using a disconnected set of static materials, which is not only ineffective for learning AI, but further, requires repeated and redundant effort for the instructor. In this paper, we introduce Moro, a software tool for easily creating and presenting AI-friendly teaching materials. Moro notebooks integrate content of different types (text, math, code, images), allow real-time interactions via modifiable and executable code blocks, and are viewable in browsers both as long-form pages and as presentations. Creating notebooks is easy and intuitive; the creation tool is also in-browser, is WYSIWYG for quick iterations of editing, and supports a variety of shortcuts and customizations for efficiency. We present three deployed case studies of Moro that widely differ from each other, demonstrating its utility in a variety of scenarios such as in-class teaching and conference tutorials.
Poker Squares is a single-player card game played on a 5 x 5 grid, in which a player attempts to create as many high-scoring Poker hands as possible. As a stochastic single-player game with an extremely large state space, this game offers an interesting area of application for Monte-Carlo Tree Search (MCTS). This paper describes enhancements made to the MCTS algorithm to improve computer play, including pruning in the selection stage and a greedy simulation algorithm. These enhancements make extensive use of domain knowledge in the form of a state evaluation heuristic. Experimental results demonstrate both the general efficacy of these enhancements and their ideal parameter settings.
This paper discusses the Turing Test as an educational activity for undergraduate students. It describes in detail an experiment that we conducted in a first-year non-CS course. We also suggest other pedagogical purposes that the Turing Test could serve.
Recent years have seen increasing interest in AI from outside the AI community. This is partly due to applications based on AI that have been used in real-world domains, for example, the successful deployment of game theory-based decision aids in security domains. This paper describes our teaching approach for introducing the AI concepts underlying security games to diverse audiences. We adapted a game-based research platform that served as a testbed for recent research advances in computational game theory into a set of interactive role-playing games. We guided learners in playing these games as part of our teaching strategy, which also included didactic instruction and interactive exercises on broader AI topics. We describe our experience in applying this teaching approach to diverse audiences, including students of an urban public high school, university undergraduates, and security domain experts who protect wildlife. We evaluate our approach based on results from the games and participant surveys.
We report on a novel use of parallel coordinates as a pedagogical tool for illustrating the non-intuitive properties of high dimensional spaces with special emphasis on the phenomenon of Curse of Dimensionality. Also, we have collated what we believe to be a representative sample of diverse approaches that exist in literature to conceptualize the Curse of Dimensionality. We envisage that the paper will have pedagogical value in structuring the way Curse of Dimensionality is presented in classrooms and associated lab sessions.
We investigated Parameterized Poker Squares to approximate an optimal game playing agent. We organized our inquiry along three dimensions: partial hand representation, search algorithms, and partial hand utility learning. For each dimension we implemented and evaluated several designs, among which we selected the best strategies to use for BeeMo, our final product. BeeMo uses a parallel flat Monte-Carlo search. The search is guided by a heuristic based on hand patterns utilities, which are learned through an iterative improvement method involving Monte-Carlo simulations and optimized greedy search.
The field of AI has changed significantly in the past couple of years and will likely continue to do so. Driven by a desire to expose our students to relevant and modern materials, we conducted two surveys, one of AI instructors and one of AI practitioners. The surveys were aimed at gathering infor-mation about the current state of the art of introducing AI as well as gathering input from practitioners in the field on techniques used in practice. In this paper, we present and briefly discuss the responses to those two surveys.
In Fall 2014 we offered an online course on Knowledge-Based Artificial Intelligence (KBAI) to about 200 students as part of the Georgia Tech Online MS in CS program. By now we have offered the course to more than 1000 students. We describe the design, development and delivery of the online KBAI class in Fall 2014.
We describe the experimental development of an AI player that adapts to different point systems for Parameterized Poker Squares. After introducing the game and research competition challenge, we describe our static board evaluation utilizing learned evaluations of abstract partial Poker hands. Next, we evaluate various time management strategies and search algorithms. Finally, we show experimentally which of our design decisions most signicantly accounted for observed performance.
We have designed an open and modular course for data science and big data analytics using a workflow paradigm that allows students to easily experience big data through a sophisticated yet easy to use instrument that is an intelligent workflow system. A key aspect of this work is the use of semantic workflows to capture and reuse end-to-end analytic methods that experts would use to analyze big data, and the use of an intelligent workflow system to elaborate the workflow and manage its execution and resulting datasets. Through the exposure of big data analytics in a workflow framework, students will be able to get first-hand experiences with a breadth of big data topics, including multi-step data analytic and statistical methods, software reuse and composition, parallel distributed programming, high-end computing. In addition, students learn about a range of topics in AI, including semantic representations and ontologies, machine learning, natural language processing, and image analysis.
Recent progress in logic programming, particularly answer set programming, has enabled us to teach it to undergraduate and high school students. We developed an online answer set programming environment with simple interface and self contained file system. It is expected to make the teaching of answer set programming more effective and help us to reach more students.
This paper discusses the design of an introductory computer science course for high school students using declarative programming. Though not often taught at the K-12 level, declarative programming is a viable paradigm for teaching computer science due to its importance in artificial intelligence and in helping student explore and understand problem spaces. This paper describes the authors' implementation of a declarative programming course for high school students during a 4-week summer session.
Profound knowledge about Artificial Intelligence (AI) will become increasingly important for careers in science and engineering. Therefore an innovative educational project teaching fundamental concepts of AI at high school level will be presented in this paper. We developed an AI-course covering major topics (problem solving, search, planning, graphs, datastructures, automata, agent systems, machine learning) which comprises both theoretical and hands-on components. A pilot project was conducted and empirically evaluated. Results of the evaluation show that the participating pupils have become familiar with those concepts and the various topics addressed. Results and lessons learned from this project form the basis for further projects in different schools which intend to integrate AI in future secondary science education.
Courses in artificial intelligence and related topics often cover methods for reasoning under uncertainty, decision theory, and game theory. However, these methods can seem very abstract when students first encounter them, and they are often taught using simple “toy” problems. Our goal is to help students to operationalize this knowledge by designing sophisticated autonomous agents that must make complex decisions in games that capture their interest. We describe a tournament-based pedagogy that we have used in two different courses with two different games based on current research topics in artificial intelligence to engage students in designing agents that use strategic reasoning. Many students find this structure very engaging, and we find that students develop a deeper understanding of the abstract strategic reasoning concepts introduced in the courses.
While many computer science programs offer courses on research methods, such classes typically tend to be aimed at graduate students. In this paper, we propose a novel means for introducing undergraduate students to research experiences in computer science — via an introductory Artificial Intelligence (A.I.) course. Students explore the content areas typically covered in an upper-level A.I. course (heuristic search, constraint satisfaction, game-playing etc.), while also learning about the mechanics of how empirical research is conducted in this field.
We developed a course in which students train an instance of Watson and develop an application that interacts with the trained instance. Additionally, students learn technical in-formation about the Jeopardy! version of Watson and they discuss a future infused with cognitive assistants. In this poster, we justify this course, characterize major assessment items and provide advice on choosing a domain.
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 2016 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.