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11
Recognition and Resolution of
“Comprehension Uncertainty” in AI
Sukanto Bhattacharya 1,* and Kuldeep Kumar 2
1 Deakin Graduate School of Business, Deakin University,
2 School of Business, Bond University,
Australia
1. Introduction
1.1 Uncertainty resolution as an integral characteristic of intelligent systems
Handling uncertainty is an important component of most intelligent behaviour - so
uncertainty resolution is a key step in the design of an artificially intelligent decision system
(Clark, 1990). Like other aspects of intelligent systems design, the aspect of uncertainty
resolution is also typically sought to be handled by emulating natural intelligence (Halpern,
2003; Ball and Christensen, 2009). In this regard, a number of computational uncertainty
resolution approaches have been proposed and tested by Artificial Intelligence ( AI )
researchers over the past several decades since birth of AI as a scientific discipline in early
1950s post- publication of Alan Turing's landmark paper (Turing, 1950).
The following chart categorizes various forms of uncertainty whose resolution ought to be a
pertinent consideration in the design an artificial decision system that emulates natural
intelligence:
Fig. 1. Broad classifications of “uncertainty” that intelligent systems are expected to resolve
* Corresponding author
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