New Fuzzing Biases for Action Policy Testing

Published: 12 Feb 2024, Last Modified: 06 Mar 2024ICAPS 2024EveryoneRevisionsBibTeXCC BY 4.0
Keywords: action policies, policy testing
Abstract: Testing was recently proposed as a method to gain trust in learned action policies in classical planning. Test cases in this setting are states generated by a fuzzing process that performs random walks from the initial state. A fuzzing bias attempts to bias these random walks towards policy bugs, that is, states where the policy performs sub-optimally. Prior work explored a simple fuzzing bias based on policy-trace cost. Here, we investigate this topic more deeply. We introduce three new fuzzing biases based on analyses of policy-trace shape, estimating whether a trace is close to looping back on itself, whether it contains detours, and whether its goal-distance surface does not smoothly decline. Our experiments with two kinds of neural action policies show that these new biases improve bug-finding capabilities in many cases.
Category: Short
Student: Graduate
Supplemtary Material: pdf
Submission Number: 220