Inference fusion: a hybrid approach to taxonomic reasoning.

We present a hybrid way to extend taxonomic reasoning using inference fusion, i.e. the dynamic combination of inferences from distributed heterogeneous reasoners. Our approach integrates results from a DL-based taxonomic reasoner with results from a constraint solver. Inference fusion is carried out by (i) parsing heterogeneous input knowledge, producing suitable homogeneous subset of the input knowledge for each specialised reasoner; (ii) processing the homogeneous knowledge, collecting the reasoning results and passing them to the other reasoner if appropriate; (iii) combining the results of the two reasoners. We discuss the benefits of our approach to the ontological reasoning and demonstrate our ideas by proposing a hybrid modelling languages, DL(D)=S, and illustrating its use by means of examples

A pollen identification expert system ; an application of expert system techniques to biological identification : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science Massey University

The application of expert systems techniques to biological identification has been investigated and a system developed which assists a user to identify and count air-borne pollen grains. The present system uses a modified taxonomic data matrix as the structure for the knowledge base. This allows domain experts to easily assess and modify the knowledge using a familiar data structure. The data structure can be easily converted to rules or a simple frame-based structure if required for other applications. A method of ranking the importance of characters for identifying each taxon has been developed which assists the system to quickly narrow an identification by rejecting or accepting candidate taxa. This method is very similar to that used by domain experts

The (Lack of) Evidence for the Kuhnian Image of Science

In their reviews of The Kuhnian Image of Science: Time for a Decisive Transformation? (2018), both Markus Arnold (2018) and Amanda Bryant (2018) complain that the contributors who criticize Kuhn’s theory of scientific change have misconstrued his philosophy of science and they praise those who seek to defend the Kuhnian image of science. In what follows, then, I would like to address their claims about misconstruing Kuhn’s theory of scientific change. But my focus here, as in the book, will be the evidence (or lack thereof) for the Kuhnian image of science. I will begin with Arnold’s review and then move on to Bryant’s review