Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

ITS Teaching ASP Dot Net

Abstract: ASP dot net is one of the most widely used languages in web developing of its many advantages, so there are many lessons that explain its basics, so it should be an intelligent tutoring system that offers lessons and exercises for this language.why tutoring system? Simply because it is one-one teacher, adapts with all the individual differences of students, begins gradually with students from easier to harder level, save time for teacher and student, the student is not ashamed to make mistakes, and more. Therefore, in this paper, we describe the design of an Intelligent Tutoring System for teaching ASP dot net to help students learn ASP dot net easily and smoothly. Tutor provides beginner level in ASP dot net. Finally, we evaluated our tutor and the results were excellent by students and teacher

Performance evaluation of a distributed integrative architecture for robotics

The eld of robotics employs a vast amount of coupled sub-systems. These need to interact cooperatively and concurrently in order to yield the desired results. Some hybrid algorithms also require intensive cooperative interactions internally. The architecture proposed lends it- self amenable to problem domains that require rigorous calculations that are usually impeded by the capacity of a single machine, and incompatibility issues between software computing elements. Implementations are abstracted away from the physical hardware for ease of de- velopment and competition in simulation leagues. Monolithic developments are complex, and the desire for decoupled architectures arises. Decoupling also lowers the threshold for using distributed and parallel resources. The ability to re-use and re-combine components on de- mand, therefore is essential, while maintaining the necessary degree of interaction. For this reason we propose to build software components on top of a Service Oriented Architecture (SOA) using Web Services. An additional bene t is platform independence regarding both the operating system and the implementation language. The robot soccer platform as well as the associated simulation leagues are the target domain for the development. Furthermore are machine vision and remote process control related portions of the architecture currently in development and testing for industrial environments. We provide numerical data based on the Python frameworks ZSI and SOAPpy undermining the suitability of this approach for the eld of robotics. Response times of signi cantly less than 50 ms even for fully interpreted, dynamic languages provides hard information showing the feasibility of Web Services based SOAs even in time critical robotic applications

Reviews

John Bowden and Ference Marton, The University of Learning: Beyond Quality and Competence in Higher Education, London: Kogan Page, 1998. ISBN: 0–7494–2292–0. Hardback, x310 pages, £35.00

EconPort: Creating and Maintaining a Knowledge Commons

null

Evaluating Go Game Records for Prediction of Player Attributes

We propose a way of extracting and aggregating per-move evaluations from sets of Go game records. The evaluations capture different aspects of the games such as played patterns or statistic of sente/gote sequences. Using machine learning algorithms, the evaluations can be utilized to predict different relevant target variables. We apply this methodology to predict the strength and playing style of the player (e.g. territoriality or aggressivity) with good accuracy. We propose a number of possible applications including aiding in Go study, seeding real-work ranks of internet players or tuning of Go-playing programs

Natural User Interface for Education in Virtual Environments

Education and self-improvement are key features of human behavior. However, learning in the physical world is not always desirable or achievable. That is how simulators came to be. There are domains where purely virtual simulators can be created in contrast to physical ones. In this research we present a novel environment for learning, using a natural user interface. We, humans, are not designed to operate and manipulate objects via keyboard, mouse or a controller. The natural way of interaction and communication is achieved through our actuators (hands and feet) and our sensors (hearing, vision, touch, smell and taste). That is the reason why it makes more sense to use sensors that can track our skeletal movements, are able to estimate our pose, and interpret our gestures. After acquiring and processing the desired – natural input, a system can analyze and translate those gestures into movement signals