Learning Procedures by Augmenting Sequential Pattern Mining with Planning Knowledge

Abstract

Procedure automation can relieve users of the burden of repetitive, time-consuming, or complex procedures and enable them to focus on more cognitively demanding tasks. Procedural learning is a method by which procedure automation can be achieved by intelligent computational assistants. This paper explores the use of filtering heuristics based on action models for automated planning to augment sequence mining techniques. Sequential pattern mining algorithms rely primarily on frequency of occurrence to identify patterns, leaving them susceptible to discovering patterns that make little sense from a cognitive perspective. In contrast, humans are able to form models of procedures from small numbers of observations, even without explicit instruction. We posit that humans are able to do so because of background knowledge about actions and procedures, which lets them effectively filter out meaningless sequential patterns. The action models foundational to artificial intelligence (AI) planning is one way to provide semantics to actions, supporting the design of heuristics for eliminating spurious patterns discovered from event logs. We present experiments with various filters derived from these action models, the results of which show the value of the filters in greatly reducing the number of sequential patterns discovered without sacrificing the number of correct patterns found, even with small, noisy event logs.