MAKEBELIEVE: Using Commonsense Knowledge to Generate Stories

Abstract

Introduction This paper introduces MAKEBELIEVE, an interactive story generation agent that uses commonsense knowledge to generate short fictional texts from an initial seed story step supplied by the user. A subset of commonsense describing causality, such as the sentence “a consequence of drinking alcohol is intoxication,” is selected from the ontology of the Open Mind Commonsense Knowledge Base (Singh, 2002). Binary causal relations are extracted from these sentences and stored as crude trans-frames (Minsky, 1988). By performing fuzzy, creativity-driven inference over these frames, creative “causal chains” are produced for use in story generation. The current system has mostly local pair-wise constraints between steps in the story, though global constraints such as narrative structure are being added. Our motivation for this project stems from two questions: Can a large-scale knowledge base of commonsense benefit the artificial intelligence community by supplying new knowledge and methods to tackle difficult AI problems? And given that full commonsense reasoning has yet to mature, can we demonstrate any success with a more “fail-soft” approach? We picked story generation because we feel it is a classic AI problem that can be approached with a creative use of commonsense knowledge. Compared to problem solving or question answering, story generation is a “softer” problem where there is no wrong solution per se, and that solution is evaluated subjectively. This paper is organized as follows. In the first section we frame our approach in the context of previous work in story generation. In the second section, we present the system’s representation of commonsense and techniques for fuzzy, creativity-driven inference. The third section presents an overview of the story generation architecture of MAKEBELIEVE. We conclude with an evaluation of the system followed by some discussion.