Goal and plan recognition via parse trees using prefix and infix probability computation

Abstract

We propose new methods for goal and plan recognition based on prefix and infix probability computation in a probabilistic contextfree grammar (PCFG) which are both applicable to incomplete data. We define goal recognition as a task of identifying a goal from an action sequence and plan recognition as that of discovering a plan for the goal consisting of goal-subgoal structure respectively. To achieve these tasks, in particular from incomplete data such as sentences in a PCFG that often occurs in applications, we introduce prefix and infix probability computation via parse trees in PCFGs and compute the most likely goal and plan from incomplete data by considering them as prefixes and infixes. We applied our approach to web session logs taken from the Internet Traffic Archive whose goal and plan recognition is important to improve websites. We tackled the problem of goal recognition from incomplete logs and empirically demonstrated the superiority of our approach compared to other approaches which do not use parsing. We also showed that it is possible to estimate the most likely plans from incomplete logs. All prefix and infix probability computation together with the computation of the most likely goal and plan in this paper is carried out using logic-based modeling language PRISM.