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Uncertainty in AI - Coggle Diagram
Uncertainty in AI
Dealing with Uncertainty
Limitations of Logic: In real-world scenarios, complete knowledge and perfect reasoning are often infeasible due to:
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Markov Chains
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Types:
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Hidden Markov Model (HMM): Underlying states are not directly observable, but influence observations.(Speech recognition, gene sequencing.)
Absorbing Markov Chain: Has at least one absorbing state (a state that cannot be left). (Games of chance.)
Decision Theory
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Utility Theory: Assigns values (utilities) to different outcomes, reflecting preferences.
Expected Utility: Weighted average of utilities of possible outcomes, considering their probabilities.
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