Default Reasoning as Situated Monotonic Inference

Abstract

Since its inception, situation theory has been concerned with the situated nature of meaning and cognition, a theme which has also recently gained some prominence in Artificial Intelligence. Channel theory is a recently developed framework which builds on concepts introduced in situation theory, in an attempt to provide a general theory of information flow. In particular, the channel theoretic framework offers an account of fallible regularities, regularities which provide enough structure to an agent‘s environment to support efficient cognitive processing but which are limited in their reliability to specific circumstances. This paper describes how this framework can lead to a different perspective on defeasible reasoning: rather than being seen as reasoning with incomplete information, an agent makes use of a situated regularity, choosing to use the regularity that seems best suited (trading off reliability and simplicity) to the circumstances it happens to find itself in. We present a formal model for this task, based on the channel theoretic framework, and sketch how the model may be used as the basis for a methodology of defeasible situated reasoning, whereby agents reason with simple monotonic regularities but may revise their choice of regularity on learning more about their circumstances.