We present a framework to represent and reason about narratives that combines logical and probabilistic representations of commonsense knowledge. Unlike most natural language understanding systems, which merely extract facts or semantic roles, our system builds probabilistic representations of the temporal sequence of world states and actions implied by a narrative. We use probabilistic actions to represent ambiguities and uncertainties in the narrative. We present algorithms that take a representation of a narrative, derive all possible interpretations of the narrative, and answer probabilistic queries by marginalizing over all the interpretations. With a focus on spatial contexts, we demonstrate our framework on an example narrative. To this end, we apply natural language processing (NLP) tools together with statistical approaches over common sense knowledge bases.
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