Detecting and correcting errors in ruled-based expert systems : an integration of empirical and explanation-based learning

In this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quinlan's FOIL program, can learn in spite of an incorrect domain theory (e.g., a knowledge base of an expert system that contains some erroneous rules). A prototype knowledge acquisition tool, KR-FOCL, has been constructed that can utilize a trace of FOCL to suggest revisions to a rule base

A framework for the adoption and diffusion of Personal Learning Environments in commercial organisations: an exploratory study in the learning and development sector in the UK

This study presents an exploratory approach to identify the main factors of Personal Learning Environment (PLE) adoption and diffusion within commercial organisations. Utilising an inductive investigative approach via the use of Grounded Theory methodology, relevant adoption factors were identified and their resulting influence during various stages of the innovation diffusion process were proposed. Data was collected using semi-structured interviews followed by systematic analysis using a three-staged coding process. The results revealed 10 factors affecting the adoption of PLEs influencing the innovation diffusion process at various stages. Informed by the Technology Acceptance Model and Innovation Diffusion Theory, the proposed model could have important implications for key decision makers within commercial organisations, while adopting, rejecting and assimilating new technological innovations (e.g. PLE) for learning delivery

Introduction to the 26th International Conference on Logic Programming Special Issue

This is the preface to the 26th International Conference on Logic Programming Special IssueComment: 6 page

Editorial: Composition in the English/literacy classroom

The act of writing is a complex task. About that, there is almost complete agreement, whether you are a psychologist, a linguist, a socio-cultural theorist, a teacher, or a student battling with an assignment deadline and a blank page. For the emergent writer in the infant classroom, the challenge of communicating in writing is compounded by the sheer effort of transcription – remembering to put spaces between words, shaping upper and lower case letters, marking sentence boundaries with full stops, and representing words in your head as accurately spelled sequences of letters on the page. For the older writer, the complexity persists, though the challenges change. Although transcribing text onto paper or screen may be less effortful, understanding the expectations of the writing task and imagining the needs of the (implied) reader create different obstacles to effortless composition

What Can Artificial Intelligence Do for Scientific Realism?

The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling