Qualitative reasoning about physical systems with multiple perspectives

Abstract

To study commonsense reasoning and to write programs that reason effectively about the physical world, we must understand the nature of perspective-taking in qualitative reasoning--which perspectives to take, how to represent them, and when to shift from one perspective to another. This dissertation defines a task-driven approach to perspective-taking for automated qualitative reasoning. Central to our approach is the notion that model formulation and selection is an integral part of reasoning about complex physical systems. Given a task, models can be created that reflect different configurations of the system topology, at various structural granularities, and in distinct language forms. This dissertation describes a computational framework that integrates three perspective-taking dimensions in reasoning about electronic circuits: topological configuration, structural aggregation, and ontological choice. The research shows that by using a task-driven, perspective-taking approach, we can extend the range of automated qualitative causal reasoning about complex physical systems.