Philosophical grounding and computational formalization for practice based engineering knowledge

Abstract

Michael Polanyi’s idea of tacit knowing and Martin Heidegger’s concept of pre-theoretical shared practice are presented as providing a strong rationale for the notion of practice based knowledge. Artificial Intelligence (AI) approaches such as Artificial Neural Networks (ANN), Case Based Reasoning (CBR) and Grounded Theory (with Interval Probability Theory) are able to model these philosophical concepts related to practice based knowledge. The AI techniques appropriate for modeling Polanyi’s and Heidegger’s ideas should be founded more on a connectionist rather than a cognitivist paradigm. Examples from engineering practice are used to demonstrate how the above techniques can capture, structure and make available such knowledge to practitioners.