Abduction Apparatus Merging Probabilistic and Reward-Based Evaluation
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Solution Overview
Problem
Current abduction frameworks cannot perform evaluations other than probabilistic evaluations, and they lack flexibility in adjusting importance for query logical formulas, limiting their applicability.
Innovation Solution
An abduction apparatus and method that includes a probability calculation unit to assess the truth probability of candidate hypotheses and a reward selection unit to assign reward values based on predefined conditions, allowing for both probabilistic and non-probabilistic evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only probabilistic evaluation is performed in abduction, then the reliability of hypothesis assessment is improved, but the flexibility to adjust importance for different query logical formulas deteriorates
Solution Approach 1:
The patent merges probabilistic evaluation and non-probabilistic evaluation into a unified abduction framework. The probability calculation unit computes probabilistic reliability, while the reward selection unit applies non-probabilistic importance weights to query logical formulas. These two evaluation dimensions are combined to comprehensively assess candidate hypotheses, thereby achieving both reliability and flexibility.
Solution Approach 2:
The abduction apparatus is designed with multi-functional evaluation capabilities. It can perform probabilistic evaluation through the probability calculation unit, non-probabilistic evaluation through the reward selection unit, and both simultaneously. This universal evaluation framework allows the system to adapt to different task requirements while maintaining rigorous hypothesis assessment.
2Adaptability or versatility
If only non-probabilistic evaluation is performed in abduction, then the flexibility to adjust importance for query logical formulas is improved, but the reliability of hypothesis assessment deteriorates
Solution Approach 1:
The patent combines non-probabilistic reward-based evaluation with probabilistic evaluation. The reward selection unit provides flexible importance adjustment for different query logical formulas, while the probability calculation unit ensures reliable hypothesis assessment through probabilistic reasoning. The integration of both approaches resolves the contradiction between flexibility and reliability.
3Adaptability or versatility
If a unified evaluation framework is created combining probabilistic and non-probabilistic evaluations, then the versatility of the abduction system is improved, but the device complexity increases
Solution Approach 1:
The patent segments the evaluation framework into distinct functional units: a probability calculation unit for probabilistic evaluation, a reward selection unit for non-probabilistic evaluation, and a solution hypothesis determination unit for integrating results. This segmentation allows each unit to specialize in its evaluation type while working together in a coordinated manner, managing complexity through modular design.
Solution Approach 2:
The solution hypothesis determination unit acts as an intermediary that receives outputs from both the probability calculation unit and the reward selection unit. It integrates the probabilistic evaluation results and non-probabilistic reward values to determine the final solution hypothesis. This intermediary structure facilitates the combination of different evaluation types without creating excessive system complexity.
Data Source
AI summary
An abduction apparatus 1 includes: a probability calculation unit 2 configured to, with respect to each of candidate hypotheses generated using observation information and knowledge information, calculate a probability that the candidate hypothesis holds true as an explanation of the observation information; and a reward selection unit 3 configured to, when the candidate hypothesis holds true, select a reward value regarding the candidate hypothesis that has held true by referring to reward definition information in which a condition that the candidate hypothesis holds true is associated with the reward value.