title: Constructing Transition Models of AI Planner Behavior


authors: Adele E. Howe and Larry D. Pyeatt


abstract: Evaluation and debugging of AI systems require coherent
views of program performance and behavior. We have developed a family
of methods, called Dependency Detection, for analyzing execution
traces for small patterns. Unfortunately, these methods provide only a
local view of program behavior. The approach described in this paper
integrates two existing methods, dependency detection and CHAID-based
analysis, to produce an abstract model of system behavior: a
transition diagram of merged states. We present the algorithm and
demonstrate it on synthetic examples and data from two AI planning and
control systems. The models produced by the algorithm summarize
sequences and cycles evident in the synthesized models and highlight
some key aspects of behavior in the two systems. We conclude by
identifying some of the inadequacies of the current algorithm and
suggesting enhancements.


presenter: Adele Howe