Case-based plan recognition using action sequence graphs

Abstract

We present SET-PR, a novel case-based plan recognition algorithm that is tolerant to missing and misclassified actions in its input action sequences. SET-PR uses a novel representation called action sequence graphs to represent stored plans in its plan library and a similarity metric that uses a combination of graph degree sequences and object similarity to retrieve relevant plans from its library. We evaluated SET-PR by measuring plan recognition convergence and precision with increasing levels of missing and misclassified actions in its input. In our experiments, SET-PR tolerated 20%-30% of input errors without compromising plan recognition performance.