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A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful "white box" neural network model with better generalization performance. In this paper, the problem formulation, the neural network configuration, and the associated optimization software are discussed in detail. This methodology is then applied to a practical real-world system to illustrate its effectiveness.

Software metrics based maintainability prediction is leading to development of new sophisticated techniques to construct prediction models. This paper proposes a new software maintainability prediction framework, which bases on Fuzzy Network, a novel exploratory modeling technique. The proposed framework utilizes both the metric data collected from software system and the subjective appraisals from experts. An application example of the framework is shown. In comparison to the Standard Fuzzy System based models, Fuzzy Network based models improves the transparency more than 71.3% and the accuracy more than 11.0%. It is confirmed that Fuzzy Network based framework is more appropriate for constructing SMP model.

Many of the artificial intelligence (AI) systems used nowadays have a very high level of accuracy but fail to explain their decisions. This is critical, especially in sensitive areas such as medicine and the health area at large but also for applications of the law, finance etc., where explanations for certain decisions are needed and are often useful and valuable as the decision itself. This paper presents a review of four different methods for creating transparency in AI systems. It also suggests a list of criteria under which circumstances one should use which methods.

A critique of some central themes in Pentti Haikonen's recent book, Consciousness and Robot Sentience, is offered. Haikonen maintains that the crucial question concerning consciousness is how the inner workings of the brain or an artificial system can appear, not as inner workings, but as subjective experience. It is argued here that Haikonen's own account fails to answer this question, and that the question is not in fact the right one to ask anyway. It is argued that making the required changes to the question reveals an important lacuna in Haikonen's explanation of consciousness.