Dynamic Option Selection with Baseline Constraints
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Solution Overview
Problem
Existing techniques for online optimization of indices can only select a single option per decision-making and fail to ensure performance in each round, while techniques allowing multiple options selection are limited to fixed populations, making them unsuitable for dynamic scenarios where options change over time, leading to potentially low rewards.
Innovation Solution
An information processing apparatus and method that acquires and selects multiple options based on accumulated data, ensuring predicted rewards satisfy a constraint condition determined by a baseline option, thereby preventing excessively low rewards in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple options are selected in a single decision-making, then the quantity of substance (number of options selected) is improved, but the reliability (performance guarantee in each round) deteriorates because the entire options population is fixed and cannot adapt to changing scenarios
Solution Approach 1:
The patent applies dynamics by allowing the options population to change over time rather than remaining fixed. The selection mechanism dynamically adapts to changing scenarios by re-evaluating options in each round, ensuring that the selected options remain optimal and reliable even as the environment evolves. This resolves the contradiction by enabling both multiple option selection and performance guarantee through dynamic adaptation.
2Reliability
If a baseline is set to ensure a certain level of accuracy, then the reliability (performance guarantee) is improved, but the productivity (ability to explore and optimize) deteriorates because the baseline limits the search space for optimization
Solution Approach 1:
The patent segments the options into different categories or groups, allowing the baseline to be established for specific segments while maintaining exploration capabilities in other segments. This segmentation enables the system to ensure performance guarantees in critical areas while preserving productivity and optimization capability in other areas, resolving the contradiction between reliability and productivity.
Solution Approach 2:
The patent changes parameters such as the baseline threshold or constraint conditions dynamically based on accumulated data and observed performance. By adjusting these parameters, the system can maintain performance guarantees when needed while allowing broader exploration and optimization when conditions permit, thus resolving the contradiction between reliability and productivity.
3Productivity
If options are selected based on predicted reward values, then the productivity (reward maximization) is improved, but the reliability (performance stability) deteriorates when the population of options changes over time
Solution Approach 1:
The patent implements feedback mechanisms where observed reward values from selected options are fed back into the system to update predicted reward values. This feedback loop ensures that the prediction models continuously adapt to changing option populations, maintaining both productivity (through reward maximization) and reliability (through performance stability) even as options change over time.
Data Source
AI summary
To prevent rewards that are obtained from becoming excessively low in a situation where selectable options can change every round, an information processing apparatus (1) includes: an acquisition section (11) that acquires information indicative of a set of options selectable in a round; a selection section (12) that selects a plurality of options from the set; and an accumulation section (13) that accumulates data including (i) the plurality of options selected by the selection section (12) and (ii) observed values of rewards obtained by the plurality of options selected by the selection section (12), the selection section (12) selecting the plurality of options such that a predicted value of a reward satisfies a constraint condition determined in accordance with a baseline option selected from the set, the predicted value being obtained with reference to the data which has been accumulated by the accumulation section (13).