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The Application of Knowledge-Growing System to Multiagent Collaborative Computation for Inferring the Behavior of Genes Interaction


Arwin Datumaya Wahyudi Sumari


Vol. 9  No. 11  pp. 82-92


Knowledge Growing System (KGS) is a novel perspective in Artificial Intelligence (AI) which is aimed to emulate how the human brain obtains new knowledge from information delivered by human sensory organs. The new knowledge is then used as the basis for making estimation in the future of the phenomenon being observed as the basis for the most appropriate decision or action that will be decided or taken. In this paper we address the application of KGS to infer the behavior of genes interaction in Genetic Regulatory System (GRS) in order to estimate their behavior in the subsequent interaction time. For this purpose we model the genes as multi-agent that performs collaborative computations in Multiagent Collaborative Computation (MCC) paradigm. The knowledge regarding the genes behavior is obtained by applying a novel information-inferencing fusion method called Observation Multi-time Arwin-Adang-Aciek-Sembiring (OMA3S). In order to show how KGS works in MCC framework, we use yeast genes-interaction values as the case study.


AI, GRS, knowledge growing, KGS, MCC, OMA3S