Modeling social causality and responsibility judgment in multi-agent interactions
Journal of Artificial Intelligence Research
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Narrative understanding is a hard problem for artificial intelligence that requires deep semantic understanding of natural language and broad world knowledge. Early research in this area stalled due to the difficulty of knowledge engineering and a trend in the field towards robustness at the expense of depth. This work explores how a practical integration of more recent resources and theories for natural language understanding can perform deep semantic interpretation of narratives when guided by specific pragmatic constraints. It shows how cognitive models can provide pragmatic context for narrative understanding in terms of well-defined reasoning tasks, and how those tasks can be used to guide interpretation and evaluate understanding. This work presents an implemented system, EA NLU, which has been used to interpret narrative text input to cognitive modeling simulations. EA NLU integrates existing large-scale knowledge resources with a controlled grammar and a compositional semantic interpretation process to generate highly expressive logical representations of sentences. Delayed disambiguation and representations from dynamic logic are used to separate this compositional process from a querydriven discourse interpretation process that is guided by pragmatic concerns and uses world knowledge. By isolating explicit points of ambiguity and using limited evidential abduction, this query-driven process can automatically identify the disambiguation choices that entail relevant interpretations. This work shows how this approach maintains computational tractability without sacrificing expressive power. EA NLU is evaluated through a series of experiments with two cognitive models, showing that it is capable of meeting the deep reasoning requirements those models pose, and that the constraints provided by the models can effectively guide the interpretation process. By enforcing consistent interpretation principles, EA NLU benefits the cognitive modeling experiments by reducing the opportunities for tailoring the input.This work also explores the use of a theory of narrative functions as a heuristic guide to interpretation in EA NLU. In contrast to potentially global task-specific queries, these narrative functions can be inferred on a sentence-by-sentence basis, providing incremental disambiguation. This method is evaluated by interpreting a set of Aesop's fables, and showing that the interpretations are sufficient to capture the intended lesson of each fable.