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Abductive Reasoning(ABR)
Infers the most likely explanation from incomplete observations
In 30 seconds
- What
- Generates competing hypotheses from incomplete data, then ranks them by likelihood given available evidence.
- When to use
- Diagnosing root causes with incomplete information, troubleshooting failures, or inferring hidden states from observable effects.
- Watch out
- Favors the most coherent story over the most probable one; confirmation bias can lock you into wrong hypotheses early.
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Abductive Reasoning: Overview
Infers the most likely explanation from incomplete observations
- Best explanation inference
- Hypothesis generation
- Evidence-based reasoning
- Uncertainty handling
- Pattern completion
- Diagnostic reasoning
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References
The papers, specifications, and repositories this pattern is based on.
- Abduction, Reason and Science: Processes of Discovery and Explanation (Josephson & Josephson, 1994)
- The Logic of Scientific Discovery and Abductive Inference (Peirce, 1903)
- Computational Models of Scientific Discovery and Theory Formation (Langley et al., 1987)
- OpenAI Hypothesis Generation and Testing
- SWI-Prolog: Logic Programming for Hypothesis Generation
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