Implicitly preserving semantics during incremental knowledge base acquisition under uncertainty.
DOI10.1016/S0888-613X(02)00148-2zbMath1048.68103OpenAlexW2092479617WikidataQ57518780 ScholiaQ57518780MaRDI QIDQ1400222
Eugene S. Santos, Eugene jun. Santos, Solomon Eyal Shimony
Publication date: 13 August 2003
Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0888-613x(02)00148-2
UncertaintyKnowledge acquisitionBayesian knowledge basesKnowledge engineeringProbabilistic semantics
Reasoning under uncertainty in the context of artificial intelligence (68T37) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)
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Cites Work
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- Approximating probabilistic inference in Bayesian belief networks is NP- hard
- Probabilistic Horn abduction and Bayesian networks
- Finding MAPs for belief networks is NP-hard
- Reasoning with BKBs -- algorithms and complexity
- The computational complexity of probabilistic inference using Bayesian belief networks
- Verification and validation of Bayesian knowledge-bases
- On Learning Read-k-Satisfy-j DNF
- A framework for building knowledge-bases under uncertainty
- Towards validation and refinement of rule-based systems
- Bayesian Graphical Models for Discrete Data
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