提出一种带公理的规划逻辑回归近似方法，显著减少执行监控变量数。
Logical Regression for Planning with Axioms
- 通过限制条件为部分状态，避免重复计算公理，实现高效近似
- 在多领域测试中使监控变量减少最高达70%，恢复率超50%
- 适合需要鲁棒执行监控的非确定性规划场景
在自动规划中，逻辑回归用于返回使某动作达成特定公式的最一般条件，广泛应用于增强计划执行鲁棒性及生成紧凑策略。尽管基础设定下计算简单，但引入公理后复杂度显著上升。本文提出一种在含公理域中近似逻辑回归的方法，将条件限制在部分状态，避免公理重复计算，同时生成最小部分状态。为验证效果，我们将该回归嵌入执行监控场景——一个能极大受益于逻辑回归的经典设置。结果表明，该方法在多个领域大幅泛化部分状态，使执行监控所考虑变量数最多减少70%；且生成的监控器具备强鲁棒性，在环境出现意外变化时可实现超过50%的恢复率。
原文摘要 · Abstract (English)
In automated planning, logical regression is an operation that returns the most general condition necessary for an action to achieve a particular formula. It has many applications, such as allowing for more robust plan execution and providing compact policies for non-deterministic planning. Although relatively simple to calculate in basic planning settings, logical regression becomes significantly more complex when additional factors, such as axioms, are present. We introduce a methodology for approximating the logical regression of an action in a domain that includes axioms; an approximation that limits conditions to partial states. Our method produces minimal partial states while avoiding the recalculation of axioms. To demonstrate the impact of our methods, we embed our form of regression in an execution monitoring context, a well-established setting that can benefit greatly from logical regression. Our results show that this form of regression can dramatically generalize partial states across multiple domains, reducing the number of variables considered for execution monitoring by up to 70%, and demonstrate that the resulting execution monitor is robust enough to recover frequently in an environment with unexpected changes: several domains recover over 50% of the time in our tests.
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