Matter

This chapter examines the question of how hylomorphists should conceive of the matter composing concrete particular objects. It considers three conceptions of matter: the traditional Thomistic doctrine of prime matter, as developed by David Oderberg; the matter-as-stuff hypothesis, as defended by Jeffrey Brower and Ned Markosian; and the hylomorphic conception of matter, according to which the matter of a concrete particular object is nothing other than its material parts and these are themselves conceived of as matter–form compounds, unless or until we reach an empirically confirmed level in the compositional hierarchy at which the hylomorphic analysis no longer applies. The chapter argues that the prime-matter hypothesis and the matter-as-stuff hypothesis give rise to a number of difficulties and concludes that the third, hylomorphic, conception of matter is therefore preferable to the previous two.

2. Computational artefacts

The modern computer is a hierarchically organized system of computational artefacts. Inventing, understanding, and applying rules and principles of hierarchy is a subdiscipline of computer science. ‘Computational artefacts’ explains the concepts of compositional hierarchy, the abstraction/refinement principle, and hierarchy by construction. There are three classes of computational artefacts—abstract, material, and liminal. An important example of an abstract artefact is the Turing machine. Sciences involving artefacts are sciences of the artificial, entailing the study of the relationship between means and ends. The ‘science’ in computer science is, thus, a science of means and ends. It asks: how can a computational artefact demonstrably achieve a given human need, goal, or purpose?

Towards Ontology-Based Realtime Behaviour Interpretation

The authors describe a generic framework for model-based behaviour interpretation and its application to monitoring aircraft service activities. Behaviour models are represented in a standardised conceptual knowledge base using OWL-DL for concept definitions and the extension SWRL for constraints. The conceptual knowledge base is automatically converted into an operational scene interpretation system implemented in Java and JESS that accepts tracked objects as input and delivers high-level activity descriptions as output. The interpretation process employs Beam Search for exploring the interpretation space, guided by a probabilistic rating system. The probabilistic model cannot be efficiently represented in the ontology, but it has been designed to closely correspond to the compositional hierarchy of behaviour concepts. Experiments are described that demonstrate the system performance with real airport data.