Establishing heuristic-search based chess programs as appropriate tools for estimating human skill levels at chess may seem impossible due to the following issues: the programs' evaluations and decisions tend to change with the depth of search and with the program used. In this research, we provide an analysis of the differences between heuristic-search based programs in estimating chess skill. Our research findings speak in favour of computer heuristic search being adequate for estimating skill levels of chess players, despite the above stated issues.
COBISS.SI-ID: 8654932
We described the process of knowledge elicitation for a neurological decision support system. To alleviate the difficult problem of knowledge elicitation from data and domain experts, we used ABML (Argument Based Machine Learning). We developed a decision support system to help the neurologists differentiate between three types of tremors: Parkinsonian, essential, and mixed tremor (comorbidity). The system is intended to act as a second opinion for the neurologists, and most importantly to help them reduce the number of patients in the »gray area« that require a very costly further examination (DaTSCAN).
COBISS.SI-ID: 8469332
Qualitative models are similar to regression models, except that instead of numerical predictions they provide insight into how a change of a certain input variable affects the output within a context of other inputs. Although people usually reason qualitatively, machine learning has mostly ignored this type of model. We present a new approach to learning qualitative models from numerical data. We describe Padé, a suite of methods for estimating partial derivatives of nknown sampled target functions. We show how to build qualitative models using standard machine learning algorithms by replacing the output variable with signs of computed derivatives. Experiments show that the developed methods are quite accurate, scalable to high number of dimensions and robust with regard to noise.
COBISS.SI-ID: 8863572