Automated tutoring for a database skills training environment

The emergence of educational technology and the growth of the Internet, coupled with the rise in the number of students entering third level education, has led to a surge of online courses offered by universities. These online courses may be part of a traditional classroom based course, or they may act as an entire course by themselves. Student engagement, assessment, feedback and guidance are important parts of any course, but have an added importance for one that is presented online. Together, in the absence of a human tutor, they can greatly aid the student in the learning process. We present an automated skills training system for a database programming environment that will promote procedural knowledge acquisition and skills training. An SQL (Structured Query Language) select statement tutoring tool is an integral part of this. Targeted at students with a prior knowledge of database theory, and as part of a blended learning strategy, the system allows the student to practice SQL querying at his own time and pace. This is achieved by providing pedagogical actions that would be offered by a human tutor. Specifically, we refer to synchronous feedback and guidance based on a personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. A high-level of interaction and engagement exists between the student and the system. Students assume control of their learning experience

Explaining and Refining Decision-Theoretic Choices

As the need to make complex choices among competing alternative actions is ubiquitous, the reasoning machinery of many intelligent systems will include an explicit model for making choices. Decision analysis is particularly useful for modelling such choices, and its potential use in intelligent systems motivates the construction of facilities for automatically explaining decision-theoretic choices and for helping users to incrementally refine the knowledge underlying them. The proposed thesis addresses the problem of providing such facilities. Specifically, we propose the construction of a domain-independent facility called UTIL, for explaining and refining a restricted but widely applicable decision-theoretic model called the additive multi-attribute value model. In this proposal we motivate the task, address the related issues, and present preliminary solutions in the context of examples from the domain of intelligent process control

A Knowledge-Based Expert Systems Primer and Catalog

For more than 20 years, artificial intelligence techniques have been applied to the development of computer programs that solve difficult problems. Although several expert systems are well known, it is all too easy to circumscribe the field based on these few examples. The purpose of this paper is to present the fundamentals of the field (the Primer), and to give a broad overview via concise descriptions of many rule-based expert systems and knowledge engineering frameworks (the Catalog)

Beyond rules: development and evaluation of knowledge acquisition systems for educational knowledge-based modelling

The technology of knowledge-based systems undoubtedly offers potential for educational modelling, yet its practical impact on today's school classrooms is very limited. To an extent this is because the tools presently used in schools are EMYCIN -type expert system shells. The main argument of this thesis is that these shells make knowledge-based modelling unnecessarily difficult and that tools which exploit knowledge acquisition technologies empower learners to build better models. We describe how such tools can be designed. To evaluate their usability a model-building course was conducted in five secondary schools. During the course pupils built hundreds of models in a common range of domains. Some of the models were built with an EMYCIN -type shell whilst others were built with a variety of knowledge acquisition systems. The knowledge acquisition systems emerged as superior in important respects. We offer some explanations for these results and argue that although problems remain, such as in teacher education, design of classroom practice, and assessment of learning outcomes, it is clear that knowledge acquisition systems offer considerable potential to develop improved forms of educational knowledge-based modelling

Knowledge-based trend detection and diagnosis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (p. 167-181) and index.by Ira Joseph Haimowitz.Ph.D

The 1989 Goddard Conference on Space Applications of Artificial Intelligence

The following topics are addressed: mission operations support; planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; and modeling and simulation

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies

The Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992)

This document contains papers presented at the Space Operations, Applications, and Research Symposium (SOAR) hosted by the U.S. Air Force (USAF) on 4-6 Aug. 1992 and held at the JSC Gilruth Recreation Center. The symposium was cosponsored by the Air Force Material Command and by NASA/JSC. Key technical areas covered during the symposium were robotic and telepresence, automation and intelligent systems, human factors, life sciences, and space maintenance and servicing. The SOAR differed from most other conferences in that it was concerned with Government-sponsored research and development relevant to aerospace operations. The symposium's proceedings include papers covering various disciplines presented by experts from NASA, the USAF, universities, and industry

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration