Context-Attentive Bandits for Partial Feature Observation
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Solution Overview
Problem
Existing contextual bandit algorithms face challenges in scenarios where the full context is not observable, leading to inefficient decision-making in clinical and dialog systems due to the inability to access the whole feature set, resulting in suboptimal treatment plans or responses.
Innovation Solution
The Context-Attentive Bandit with Observations (CABO) algorithm, which uses a limited number of observable features to select additional unknown features through a reinforcement learning policy, combining contextual and combinatorial bandit approaches to maximize reward.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the full feature set is accessed to make decisions, then decision accuracy is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent segments the feature set into observable features (accessible without cost) and unobservable features (requiring resource expenditure to access). The algorithm selectively accesses only the necessary subset of unobservable features based on the current state and budget, rather than accessing all features. This segmentation resolves the contradiction by maintaining decision accuracy through strategic feature selection while reducing system complexity through selective observation.
Solution Approach 2:
The patent applies partial action by accessing only a limited subset of unobservable features rather than the full feature set. The feature budget constraint enforces this partial observation, allowing the system to make decisions with incomplete information when resources are limited, thus reducing complexity while maintaining acceptable decision accuracy through the reinforcement learning policy's ability to prioritize important features.
2Reliability
If more features are observed to improve decision quality, then reward maximization is improved, but resource consumption increases
Solution Approach 1:
The patent implements dynamic feature budget allocation where the number of unobservable features to access is adjusted based on the current state, available budget, and learned policies. The reinforcement learning algorithms (CATSO and CCB-UCB) dynamically determine the optimal feature budget expenditure at each decision point, allowing the system to maximize decision quality while adapting resource consumption to available budgets and situational needs.
Solution Approach 2:
The patent changes the parameter of feature observability from a static all-or-nothing approach to a dynamic selective observation model. By introducing the feature budget as a controllable parameter and using reinforcement learning to optimize its utilization, the system can adjust the quantity of observed features to match resource availability while maintaining decision quality through intelligent feature selection based on learned patterns.
3Productivity
If reinforcement learning policies are updated with feedback, then decision performance is improved, but computational overhead increases
Solution Approach 1:
The patent applies preliminary action by pre-training the reinforcement learning policies (CATSO for feature selection and CCB-UCB for decision-making) offline using historical data and simulations. This pre-training establishes baseline policies that can be executed with minimal online computation. During actual deployment, the system only performs lightweight policy selection and budget allocation rather than full policy learning, significantly reducing computational overhead while maintaining high decision performance through the pre-learned knowledge.
Data Source
AI summary
A novel formulation called the Context-Attentive Bandit with Observations (CABO) is described, where only a limited number of features can be accessed by the learner. The present invention is applicable to many problems including problems arising in clinical settings and dialog systems where it is not possible to reveal the whole feature set. The present invention adapts the standard contextual bandit algorithm known as Thompson Sampling with a novel algorithm, we call Context-Attentive Thompson Sampling with Observations (CATSO). Experimental results are included to demonstrate its effectiveness including a regret analysis and an empirical evaluation demonstrating advantages of the disclosed novel approach on several real-life datasets.