This paper presents generalizations of Bayes likelihood-ratio updating rule which facilitate an asynchronous propagation of the impacts of new beliefs and/or new evidence in hierarchically organized inference structures with multi-hypotheses variables.
This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.
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| Category | 🤖 Artificial Intelligence |
| Published | Feb 28, 2022 |
| Journal | ACM eBooks |
| Authors | Judea Pearl |
| DOI | 10.1145/3501714.3501727 |
| Citations | 759 |
| Source | OpenAlex |