Engineering explored

Meifly: Models explored interactively

Why do we build models? There are two basic reasons: explanation or prediction (Ripley, 2004). Using large ensembles of models for prediction is commonplace, but is rarely used for explanation, where we typically choose one “best” model. When there are several equally good models, it is common sense to look at them too, but can the “bad” models tell us something as well? This paper describes ex...

Reuse of constraint knowledge bases and problem solvers explored in engineering design

Reuse has long been a major goal of the Knowledge Engineering community. We present a case study of the reuse of constraint knowledge acquired for one problem solver, by two further problem solvers. For our analysis we chose a well known benchmark knowledge base system written in CLIPS, which was based on the propose-and-revise (P+R) problem solving method (PSM), and which had a lift/elevator k...

Computational intelligence and soft computing: some thoughts on already explored and not yet explored paths

We comment upon the very essence, roots, potentials, and applicability of computational intelligence and soft computing. We followed a different path than those traditionally employed, and which are so well and in a deep and comprehensive way documented in other papers in this special issue. First, we consider relations between computational intelligence and artificial intelligence, starting fr...

Learning Pla es in Newly Explored Environments

Reflective Practice: How the World Bank Explored Its Own Biases?

While many international organisations have independent evaluations, including the International Monetary Fund (IMF) and World Health organization (WHO), uniquely the World Bank in its 2015 World Development Report sought to ascertain the potential biases that influence how its staff interpret evidence and influence policy. Here, we describe the World Bank’s study design, including experiments ...