Integrating statistical methods for characterizing causal influences on planner behavior over time

Abstract

Statistical causal modeling techniques allow us to develop models of program behavior, but these techniques tend to be limited in what they can model: either continuing, repetitive influences or causal influences without cycles, but not both as appear in many environments. The paper describes how two statistical modeling techniques can be combined to suggest and test specific hypotheses about how the environment and the AI planner's design causally influence the planner's behavior. One technique, dependency detection, is designed to identify relationships (dependencies) between particular failures, the methods that repair them and the occurrence of failures downstream. Another method, path analysis, builds causal models of correlational data. Dependency detection operates over a series of events, and path analysis models within a temporal snapshot. We explain the integration of the techniques and demonstrate it on execution data from the Phoenix planner.<>