Possibilistic Answer Set Programming Revisited

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP programs in terms of possibilistic logic where an ASP program specifies a set of constraints on possibility distributions. This characterization is then naturally generalized to define answer sets of PASP programs. We furthermore provide a syntactic counterpart, leading to a possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we show how our framework can readily be implemented using standard ASP solvers.

Towards Possibilistic Fuzzy Answer Set Programming

Fuzzy answer set programming (FASP) is a generalization of answer set programming to continuous domains. As it can not readily take uncertainty into account, however, FASP is not suitable as a basis for approximate reasoning and cannot easily be used to derive conclusions from imprecise information. To cope with this, we propose an extension of FASP based on possibility theory. The resulting framework allows us to reason about uncertain information in continuous domains, and thus also about information that is imprecise or vague. We propose a syntactic procedure, based on an immediate consequence operator, and provide a characterization in terms of minimal models, which allows us to straightforwardly implement our framework using existing FASP solvers.

Approximating Credal Network Inferences by Linear Programming

An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to very accurate inferences. The approach can also be specialized to classification with credal networks based on the maximality criterion. A complexity analysis for both the problem and the algorithm is reported together with numerical experiments, which confirm the good performance of the method. While the inner approximation produced by the algorithm gives rise to a classifier which might return a subset of the optimal class set, preliminary empirical results suggest that the accuracy of the optimal class set is seldom affected by the approximate probabilities

Think, code, succeed: The Programming Studio as a games-based collaboration:Paper published in the Proceedings of the Association for Teacher Education in Europe conference: Teacher Education through Partnerships and Collaborative Learning Communities, Glasgow, UK. ATEE, 2015

This paper investigates the profile of teachers in the island of Ireland who declared themselves willing to undertake professional development activities in programming, in particular to master programming by taking on-line courses involving the design of computer games. Using the Technology Acceptance Model (TAM), it compares scores for teachers “willing” to undertake the courses with scores for those who declined, and examines other differences between the groups of respondents. Findings reflect the perceived difficulties of programming and the current low status accorded to the subject in Ireland. The paper also reviews the use of games-based learning as a “hook” to engage learners in programming and discusses the role of gamification as a tool for motivating learners in an on-line course. The on-line course focusing on games design was met with enthusiasm, and there was general consensus that gamification was appropriate for motivating learners in structured courses such as those provided.

The Impact of Multimedia Education on Uptake of Comprehensive Eye Examinations in Rural China: A Randomized, Controlled Trial

PURPOSE: To study the effect of multimedia education on acceptance of comprehensive eye examinations (CEEs), critical for detecting glaucoma and diabetic eye disease, among rural Chinese patients using a randomized, controlled design.
METHODS: Patients aged ≥40 years were recruited from 52 routine clinic sessions (26 intervention, 26 control) conducted at seven rural hospitals in Guangdong, China. Subjects answered demographic questionnaires, were tested on knowledge about CEEs and chronic eye disease, and were told the cost of examination (range US$0-8). At intervention sessions, subjects were cluster-randomized to view a 10-minute video on the value of CEEs and retested. Control subjects were not retested. Trial outcomes were acceptance of CEEs (primary outcome) and final knowledge scores (secondary outcome).
RESULTS: At baseline, >70% (p = 0.70) of both intervention (n = 241, 61.2 ± 12.3 years) and control (n = 218, 58.4 ± 11.7 years) subjects answered no knowledge questions correctly, but mean scores on the test (maximum 5 points) increased by 1.39 (standard deviation 0.12) points (p < 0.001) after viewing the video. Intervention (73.0%) and control (72.9%) subjects did not differ in acceptance of CEEs (p > 0.50). In mixed-effect logistic regression models, acceptance of CEEs was associated with availability of free CEEs (odds ratio 18.3, 95% confidence interval 1.32-253.0), but not group assignment or knowledge score. Acceptance was 97.5% (79/81) when free exams were offered.
CONCLUSIONS: Education increased knowledge about but not acceptance of CEEs, which was generally high. Making CEEs free could further increase acceptance.

Teaching Programming - Understanding Lecture Capture YouTube Analytics

ABSTRACT
The proliferation in the use of video lecture capture in universities worldwide presents an opportunity to analyse video watching patterns in an attempt to quantify and qualify how students engage and learn with the videos. It also presents an opportunity to investigate if there are similar student learning patterns during the equivalent physical lecture. The goal of this action based research project was to capture and quantitatively analyse the viewing behaviours and patterns of a series of video lecture captures across several university Java programming modules. It sought to study if a quantitative analysis of viewing behaviours of Lecture Capture videos coupled with a qualitative evaluation from the students and lecturers could be correlated to provide generalised patterns that could then be used to understand the learning experience of students during videos and potentially face to face lectures and, thereby, present opportunities to reflectively enhance lecturer performance and the students’ overall learning experience. The report establishes a baseline understanding of the analytics of videos of several commonly used pedagogical teaching methods used in the delivery of programming courses. It reflects on possible concurrences within live lecture delivery with the potential to inform and improve lecturing performance.

The supporting role of emerging multimedia technologies in higher education

In this paper we give an overview of current investigations into the incorporation of cutting edge technologies within the Higher Education teaching domain. In particular, the role of audio is discussed through a number of case studies. The paper then concludes with a discussion of the authors' plans to incorporate audio and video content as supplementary course material for a technical undergraduate module

Multilinear and Integer Programming for Markov Decision Processes with Imprecise Probabilities

Markov Decision Processes (MDPs) are extensively used to encode sequences of decisions with probabilistic effects. Markov Decision Processes with Imprecise Probabilities (MDPIPs) encode sequences of decisions whose effects are modeled using sets of probability distributions. In this paper we examine the computation of Γ-maximin policies for MDPIPs using multilinear and integer programming. We discuss the application of our algorithms to “factored” models and to a recent proposal, Markov Decision Processes with Set-valued Transitions (MDPSTs), that unifies the fields of probabilistic and “nondeterministic” planning in artificial intelligence research.