An Information Theoretic Approach To Approximate Reasoning
S.N. Gottschlich, Harry E. Stephanou · 2005
In traditional geometric modeling, it is common to assume that models of objects created are complete and correct. Unfortunately in real life robots and other autonomous machines may have only uncer- tain knowledge of only a portion of an object's geom- etry and therefore we need to be able to create object models that are incomplete and uncertain. Towards this end we have developed mechanisms for represent- ing geometric entities in terms of Dempster-Shafer belief functions. To do this we have extended the no- tion of belief functions to include continuous frames of discernment. A fractal model of the belief func- tions is developed and information theory concepts are applied for reasoning about a sensor data model vis-a-vis reference object models. An implementation of these concepts and experiments involving them are discussed.