基于公理的规划逻辑回归
Logical Regression for Planning with Axioms
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中文总结 AI 辅助
研究含公理领域中动作的逻辑回归，提出近似方法，将条件限制在部分状态以避免公理重算。该方法可泛化部分状态，减少执行监控变量，增强执行监控在意外变化环境中的恢复能力。
中文摘要 AI 辅助
在自动规划中，逻辑回归是一种操作，返回动作实现特定公式所需的最一般条件，有诸多应用。虽在基本规划设置中计算相对简单，但存在公理等额外因素时会显著变复杂。本文引入一种在含公理的领域中近似动作逻辑回归的方法，将条件限制在部分状态，产生最小部分状态且避免公理重新计算。通过嵌入执行监控环境展示其影响，结果表明该回归形式能大幅泛化跨多个领域的部分状态，减少执行监控考虑变量数达70%，且执行监控在意外变化环境中恢复能力强。
英文摘要
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.
