Evaluating the SBR Algorithm Using Automatically Generated Plan Libraries

Abstract

Most approaches to plan recognition are based on manually constructed rules, where the knowledge base is represented as a plan library for recognising plans. For non-trivial domains, such plan libraries have complex structures representing possible agent behaviour to achieve a plan. Existing plan recognition approaches are seldom tested at their limits, and, though they use conceptually similar plan library representations, they rarely use the exact same domain in order to directly compare their performance, leading to the need for a principled approach to evaluating them. Thus, we develop a mechanism to automatically generate arbitrarily complex plan libraries which can be directed through a number of parameters, in order to create plan libraries representing different domains and so allowing systematic experimentation and comparison among the several plan recognition algorithms. We validate our mechanism by carrying out an experiment to evaluate the performance of a known plan recognition algorithm.