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Human-centric intelligent systems for exploration and knowledge discovery

Parmee, I. C.

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I. C. Parmee


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. (2005). Human-centric intelligent systems for exploration and knowledge discovery. Analyst, 130(1), 29-34.

Journal Article Type Review
Publication Date Jan 1, 2005
Deposit Date Jan 22, 2010
Publicly Available Date Dec 2, 2016
Journal Analyst
Print ISSN 1364-5528
Publisher Royal Society of Chemistry
Peer Reviewed Not Peer Reviewed
Volume 130
Issue 1
Pages 29-34
Keywords intelligent systems, exploration, knowledge discovery
Public URL
Publisher URL
Additional Information Additional Information : Paper invited by journal committee for Perspective i-Section then subjected to peer review process. Invitation due to recognition of future potential of research which has successfully transfered knowledge and technology from the engineering design domain into pharmaceutical drug design and discovery. Paper introduces develoment of user-centric intelligent systems for the early stages of engineering design and translates this in terms of potential and suitable integration with in-silico drug design processes with illustrations of perceived systems and processes. Recognition and invitation is indicative that this research is considered significant and ground-breaking by the Society. Journal Impact Factor: 3.198.


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