Human-centric intelligent systems for exploration and knowledge discovery.
This speculative article discusses research and development relating to computational intelligence (CI) technologies comprising powerful machine-based search and exploration techniques that can generate, extract, process and present high-quality information from complex, poorly understood biotechnology domains. The integration and capture of user experiential knowledge within such CI systems in order to support and stimulate knowledge discovery and increase scientific and technological understanding is of particular interest. The manner in which appropriate user interaction can overcome problems relating to poor problem representation within systems utilising evolutionary computation (EC), machine-learning and software agent technologies is investigated. The objective is the development of user-centric intelligent systems that support an improving knowledge-base founded upon gradual problem re-definition and reformulation. Such an approach can overcome initial lack of understanding and associated uncertainty.
Parmee
I C
IC
Advanced Computation in Design and Decision-making CEMS, University of the West of England, Bristol, UK. ian.parmee@uwe.ac.uk
D001185:Artificial Intelligence; D001709:Biotechnology; D019295:Computational Biology; D001330:Electronic Data Processing; D006801:Humans; D011340:Problem Solving; D012106:Research; D013597:Systems Analysis