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

First-Order Decomposition Trees

Lifting attempts to speed up probabilistic inference by exploiting symmetries in the model. Exact lifted inference methods, like their propositional counterparts, work by recursively decomposing the model and the problem. In the propositional case, there exist formal structures, such as decomposition trees (dtrees), that represent such a decomposition and allow us to determine the complexity of inference a priori. However, there is currently no equivalent structure nor analogous complexity results for lifted inference. In this paper, we introduce FO-dtrees, which upgrade propositional dtrees to the first-order level. We show how these trees can characterize a lifted inference solution for a probabilistic logical model (in terms of a sequence of lifted operations), and make a theoretical analysis of the complexity of lifted inference in terms of the novel notion of lifted width for the tree

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

Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation

Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning.Comment: ICML2012, fixed citations to use correct tech report numbe

The Effectiveness of the Secondary Weapon of the West Virginia State Police

The present study examines the effectiveness of the West Virginia State Troopers’ secondary weapon, Oleoresin Capsicum (OC) pepper spray (CAP-STUN®), as a means of alternative use of force for non-cooperative subjects. The WV State Police have adopted OC in an effort to reduce the number and severity of injuries sustained by suspects. This method was adopted as an optional means to effect arrests through non-lethal force. The use of OC can control and restrain individuals while causing the least possible harm to the individuals without increasing danger to troopers or others. Questionnaires were sent to West Virginia State Troopers for their responses and opinions of the efficiency of OC pepper spray. The study explores troopers’ perceptions of OC as a weapon of safe and effective use-of-force

Evaluation of operating parameters for tank rinsing systems

An experimental apparatus was designed to quantify the effectiveness of commercially available tank rinse nozzles to adequately clean residues from inner surfaces of sprayer reservoirs. A laboratory scale test stand was constructed to perform two specific functions: 1) provide mounting and support for typical sprayer tank mounting brackets; and 2) house two independent fluid delivery systems. A multi-position frame was designed to accept mounting brackets for four tank sizes. Design considerations included rapid and easy exchange of various tanks and still provide support for the mass of tank and fluid. Two fluid delivery systems were designed and assembled to facilitate the testing of sprayer tanks. One fluid delivery system was devoted to transferring concentrated trace solution (10000 parts per million ionic bromide) between a separate storage reservoir and test tanks. A 1136-L [300-gal.] polyethylene tank served as a storage vessel for the test solution. The ionic bromide tracer was used to contaminate interior surfaces of test tanks. The transfer system was used to move the concentrated chemical into test tanks. After filling a test tank, the solution was transferred back to the storage reservoir. A second fluid delivery system supplied clean rinse water to a tank rinsing nozzle centrally located inside a test tank. Clean water was stored in an auxiliary 114-L [30-gaI.] cone tank mounted on the test stand. Two separate fluid delivery systems were utilized to avoid potential rinse water contamination, which would ultimately alter rinsate sample concentration. Two commercial tank rinsing nozzles (Spraying Systems Company model 27500-E %-18 TEF and Lechler, Inc. model 5E) were evaluated. Three operating parameters (pressure, rinse sequence, and rinse volume) were varied to evaluate performance of these two nozzles. Nozzle operating pressures were set according to manufacturers recommendations. The Spraying Systems nozzle was operated at 138, 207, 276, and 345 kPa [20, 30, 40, and 50 psi] for all tank sizes (190, 380, 760, 1136 L cylindrical and elliptical [50, 100, 200, and 300 gal.]). The Lechler nozzle was tested only on the 380-L [100-gal.] tank at pressure of 207, 276, and 345 kPa [30, 40, and 50 psi]. Two rinse volumes (5 and 10 percent of tank capacity) and three rinse procedures (single, double, and triple) were evaluated at each pressure. For the single rinse procedure, the tank was rinsed once with the total rinse volume. In the double (triple) rinse procedure, the tank was rinsed twice (thrice) with half (one-third) of the total rinse volume. All rinse tests were replicated a minimum of three times. Rinsate samples were collected in disposable paraffin lined paper cups to prevent cross contamination between samples. Rinsates were drained between each rinse cycle. An Orion model 290A portable pH meter was used to measure rinsate sample concentrations. The meter, equipped with an ion specific bromide electrode and its corresponding double junction reference electrode, enabled direct concentration measurement ranging from 0.0000 to 199000 parts per million. Rinse sequence was statistically significant (a=0.05) in reducing rinsate concentration for all tank sizes tested. Rinse volume and tank size were also shown to have an effect. Operating pressure had little impact on rinsate concentration. Tank geometry and nozzle type were not significant (α=0.05) factors in this study