graph LR
BayesianPriorManager["BayesianPriorManager"]
StrategyPrior["StrategyPrior"]
PriorUpdateLogic["PriorUpdateLogic"]
StrategySelectionLogic["StrategySelectionLogic"]
BayesianPriorManager -- "manages" --> StrategyPrior
BayesianPriorManager -- "coordinates" --> PriorUpdateLogic
BayesianPriorManager -- "coordinates" --> StrategySelectionLogic
PriorUpdateLogic -- "modifies" --> StrategyPrior
StrategySelectionLogic -- "consumes" --> StrategyPrior
PriorUpdateLogic -- "notifies" --> BayesianPriorManager
StrategySelectionLogic -- "informs" --> BayesianPriorManager
The Bayesian Meta-Learning subsystem is designed to enable adaptive strategy selection based on learned performance priors. At its core, the BayesianPriorManager orchestrates the entire learning process, maintaining and managing StrategyPrior objects for various strategies and task types. It leverages PriorUpdateLogic to refine these priors with new performance data and StrategySelectionLogic to intelligently choose strategies for future execution. This closed-loop system continuously learns and optimizes strategy effectiveness, ensuring that the most promising strategies are prioritized while also allowing for exploration of less-known but potentially high-performing alternatives.
The central orchestrator of the Bayesian learning process. It manages the lifecycle of strategy priors, including initialization, loading, saving, and coordinating updates and strategy selection. It acts as the primary interface for external components to interact with the learning engine.
Related Classes/Methods:
The fundamental data model representing the probabilistic prior distribution for a single strategy. It encapsulates the parameters (e.g., alpha, beta for a Beta distribution) that define a strategy's learned performance characteristics.
Related Classes/Methods:
Responsible for incorporating new performance observations into a strategy's prior distribution, updating its probabilistic model. It also calculates confidence metrics associated with these priors. This component embodies the "Bayesian Update" mechanism.
Related Classes/Methods:
Implements the mechanisms for selecting strategies based on their current prior distributions. This includes sampling strategies (Thompson sampling), ranking them by expected performance, and deciding whether to prioritize exploration of new strategies or exploitation of known good ones.
Related Classes/Methods: