AAAI Publications, Sixteenth International Conference on Principles of Knowledge Representation and Reasoning

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Knowledge Representation and Reasoning in Answering Science Questions: A Case Study for Food Web Questions
Arindam Mitra, Chitta Baral, Peter Clark

Last modified: 2018-09-24


A group of researchers from the Allen Institute of Artificial Intelligence has proposed the Aristo challenge that requires answering science questions. The goal of the challenge is to aid in the development of machines that can understand natural language, use knowledge and reason. In this work, we take a subset of those questions, namely the questions from the chapters of food web. We model a consequence operator for the food webs that given a food web and a perturbation to some of the populations aims to compute possible effects on the other populations in the food web. We then use this operator to answers questions of the kind, ‘Explain why the population of rabbits might decrease if the population of mice decreased.’ or ‘Explain why the population of rabbits might change if the population of mice decreased.’ Unlike the previous works which deal with only direct predator-prey situations, here we aim to characterize the effect(s) even when the two populations in the question are indirectly related.


aristo; asp; food web

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