A Conceptual Model of an Artificially Intuitive Reasoning Engine for Ecosystem Control

Y.C. Sun and R. Kok (Canada)


Artificial Intelligence, Ecosystem Control, Fuzziness, Intuitive Reasoning, Limited Certainty.


Based on the observation of humans, one can state that intuition is an inherent mentation capability. Human intuition has demonstrated decision-making capacity under novel complex situations where certain and exact knowledge scarcely exists. In this study, intuition in the context of artificial intelligence (AI) was examined. Research in cognitive psychology and computational cognition has commonly suggested intuition as illogical and emotional. Alternatively, we propose to look at intuitive mentation as a particular type of information processing, whereby numerous low-certainty inputs and causations are integrated to obtain reinforced, certain enough conclusions. In this context, the intuitive mentation process is perceived rational and deductive. The study sought to investigate approaches to constructing an inference engine capable of solving complex problems in an intuitive manner, particularly problems related to ecosystem control. The knowledge involved in complex problems was found to present three traits: limited certainty, fuzziness, and multiple values. A new system attribute, the intuition measure, is discussed, and a rule-based reasoning scheme followed in the development of a conceptual model of an intuitive inference engine.

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