Reasons for Beliefs in Understanding: Applications of Non-Monotonic Dependencies to Story Processing
Abstract
Many of the inferences and decisions which contribute to understanding involve fallible assumptions. When these assumptions are undermined, computational models of comprehension should respond rationally. This paper crossbreeds AI research on problem solving and understanding to produce a hybrid model (reasoned understanding). In particular, the paper shows how non-monotonic dependencies [Doyle79] enable a schema-based story processor to adjust to new information requiring the retraction of assumptions.
Cite
Text
O'Rorke. "Reasons for Beliefs in Understanding: Applications of Non-Monotonic Dependencies to Story Processing." AAAI Conference on Artificial Intelligence, 1983.Markdown
[O'Rorke. "Reasons for Beliefs in Understanding: Applications of Non-Monotonic Dependencies to Story Processing." AAAI Conference on Artificial Intelligence, 1983.](https://mlanthology.org/aaai/1983/oaposrorke1983aaai-reasons/)BibTeX
@inproceedings{oaposrorke1983aaai-reasons,
title = {{Reasons for Beliefs in Understanding: Applications of Non-Monotonic Dependencies to Story Processing}},
author = {O'Rorke, Paul},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1983},
pages = {306-309},
url = {https://mlanthology.org/aaai/1983/oaposrorke1983aaai-reasons/}
}