Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

The Semantic Web Paradigm for a Real-Time Agent Control (Part I)

For the Semantic Web point of view, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. Adding logic to the Web, the means to use rules to make inferences, choose courses of action and answer questions, is the actual task for the distributed IT community. The real power of Intelligent Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results with other programs. The first part of this paper is an introductory of Semantic Web properties, and summarises agent characteristics and their actual importance in digital economy. The second part presents the predictability of a multiagent system used in a learning process for a control problem.Semantic Web, agents, fuzzy knowledge, evolutionary computing

The Semantic Web: Apotheosis of annotation, but what are its semantics?

This article discusses what kind of entity the proposed Semantic Web (SW) is, principally by reference to the relationship of natural language structure to knowledge representation (KR). There are three distinct views on this issue. The first is that the SW is basically a renaming of the traditional AI KR task, with all its problems and challenges. The second view is that the SW will be, at a minimum, the World Wide Web with its constituent documents annotated so as to yield their content, or meaning structure, more directly. This view makes natural language processing central as the procedural bridge from texts to KR, usually via some form of automated information extraction. The third view is that the SW is about trusted databases as the foundation of a system of Web processes and services. There's also a fourth view, which is much more difficult to define and discuss: If the SW just keeps moving as an engineering development and is lucky, then real problems won't arise. This article is part of a special issue called Semantic Web Update

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

A commentary on standardization in the Semantic Web, Common Logic and MultiAgent Systems

Given the ubiquity of the Web, the Semantic Web (SW) offers MultiAgent Systems (MAS) a most wide-ranging platform by which they could intercommunicate. It can be argued however that MAS require levels of logic that the current Semantic Web has yet to provide. As ISO Common Logic (CL) ISO/IEC IS 24707:2007 provides a firstorder logic capability for MAS in an interoperable way, it seems natural to investigate how CL may itself integrate with the SW thus providing a more expressive means by which MAS can interoperate effectively across the SW. A commentary is accordingly presented on how this may be achieved. Whilst it notes that certain limitations remain to be addressed, the commentary proposes that standardising the SW with CL provides the vehicle by which MAS can achieve their potential.</p

A Logic-Independent IDE

The author's MMT system provides a framework for defining and implementing logical systems. By combining MMT with the jEdit text editor, we obtain a logic-independent IDE. The IDE functionality includes advanced features such as context-sensitive auto-completion, search, and change management.Comment: In Proceedings UITP 2014, arXiv:1410.785