154 research outputs found
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
- …