<title>Modeling confusion for autonomous systems</title>

Abstract

Autonomous systems process sensory information to build representations of the external world, which serve as the basis for response decisions. These representations may be characterized by property lists, some of which are not direct sensor measurements, but inferred. Inferred properties are identified by classifiers or pattern recognition devices, which identify the existence of `fuzzy' concepts, such as `flying object.' Fuzzy classifiers assign properties with infinite degrees of existence, represented by numbers on the closed unit interval [0,1], which raises issues not present with classifiers based on binary logic, in which properties either exist or do not. One of these issues is confusion. A system is said to be in a state of confusion when it is generating similar confidence factors for mutually exclusive properties. For example, the fuzzy concepts `civilian' and `military' may be properties of the object class `aircraft,' and a state of confusion exists if confidence factors for these properties are both relatively close. An autonomous system that reacts to aircraft needs an explicit representation of confusion to enable it to decide whether it should react to object instances in their present form or continue data gathering. We discuss approaches to modeling confusion using fuzzy logical operators and present illustrative examples of its application in multi-level classification.