How deeply does The AI Pocket Book cover exploitation?

Defines the term and situates it in an exploration-exploitation tradeoff, but stops short of formal algorithms.

Exploitation

In reinforcement learning, the practice of choosing actions believed to maximize current reward based on known information.

Exploitation means choosing the action that seems best from what is already known, instead of spending effort to learn something new. section 1.27

In the trial-and-error setup, it follows exploration: after user preferences are discovered, the system shifts toward showing relevant ads rather than random ones. section 1.27

The tradeoff is simple but annoying: too much exploitation can miss new preferences, so reinforcement learning keeps asking how much exploration to leave in. section 1.27

How The AI Pocket Book covers it

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Also explained in

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Bayesian Optimization in ActionExploitation is an optimization strategy that evaluates regions believed to have high objective performance based on current information.
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Optimization Algorithms: AI techniques for design, planning, and control problemsA search strategy that concentrates effort in a region where good solutions have already been found.
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Experimentation for EngineersA decision strategy that selects the alternative currently believed to produce the best observed outcome.
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Evolutionary Deep Learning: Genetic algorithms and neural networksExploitation is the selection of actions believed to produce the highest available value according to current knowledge.
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Building LLM Applications with DSPyIn optimization, the selection of candidate configurations expected to perform well based on existing evaluations.

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Related concepts

multi-armed banditused by · A multi-armed bandit is a sequential decision problem in which an agent chooses among options with uncertain rewards, balancing reward seeking with gathering information about those options.
exploration-exploitation trade-offcontrasts with · The exploration-exploitation trade-off is the decision problem of balancing actions that gather information about uncertain alternatives with actions chosen for their currently expected payoff.
data explorationcontrasts with · Data exploration is the systematic examination and summarization of data to understand its structure, quality, distributions, relationships, patterns, and anomalies.
Bayesian optimizationused by · Bayesian optimization is a sequential method for optimizing an expensive or unknown objective by using a probabilistic model to guide evaluations.

This explainer was generated by Manning's knowledge graph from the text of The AI Pocket Book. The section links point to the parts of the book it draws on.