Induction of Interpretable Possibilistic Logic Theories from Relational Data

The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which make them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly encode what can be derived with a given level of certainty. Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.Comment: Longer version of a paper appearing in IJCAI 201

A Nursing Educational Program for Recognizing and Managing Emotional Labor

The purpose of this project is to develop a one hour learning module on emotional labor for all staff that will enable them to recognize and manage emotional labor for themselves and their coworkers in their daily work. This project presents the concept of emotional labor and emotional labor in nursing. A one hour learning module is described. The presentation of this module is discussed. There is discussion of the implications of emotional labor for nursing research, nursing practice and nursing education

Encoding Markov Logic Networks in Possibilistic Logic

Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue, we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a given MLN using maximum a posteriori (MAP) inference. Unfortunately, the size of this theory is exponential in general. We therefore also propose two methods which can derive compact theories that still capture MAP inference, but only for specific types of evidence. These theories can be used, among others, to make explicit the hidden assumptions underlying an MLN or to explain the predictions it makes.Comment: Extended version of a paper appearing in UAI 201

The Tiger\u27s Back A Report on Australian Organizations for Metropolitan Planning Administration

The Australian town planning scene presents many interesting and unusual aspects to the city planner from the United States of America. Aspects which because of familiarity and proximity may not strike the Australian town planner as being either interesting, or unusual. This report will discuss some of these aspects as they seem to be affecting each major metropolitan region which includes the capital city in each of the six Australian States. Throughout the report, metropolitan region refers to the unit, or units of Local Government which are included in the planning area of each organization which is considered in this report. These a.re defined through maps and lists in Item 6.100

Uncoupled material model of ductile fracture with directional plasticity

Proposed paper deals with the application of plastic response with directional distortional hardening (DDH) in uncoupled ductile fracture model and comparison of the results with the same ductile fracture model based on isotropic J2 plasticity. The results of simulations have proven not negligible role of model of plasticity and the response of the model with DDH plasticity is closer to reality then the one of the model with isotropic plasticity