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首页> 外文会议>European Conference on Logics in Artificial Intelligence >Counting Strategies for the Probabilistic Description Logic ALL~(ME) Under the Principle of Maximum Entropy
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Counting Strategies for the Probabilistic Description Logic ALL~(ME) Under the Principle of Maximum Entropy

机译：在最大熵原则下计算概率描述逻辑逻辑逻辑逻辑逻辑的策略

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摘要

We present ALC~(ME), a probabilistic variant of the Description Logic ALC that allows for representing and processing conditional statements of the form "if E holds, then F follows with probability p" under the principle of maximum entropy. Probabilities are understood as degrees of belief and formally interpreted by the aggregating semantics. We prove that both checking consistency and drawing inferences based on approximations of the maximum entropy distribution is possible in ALC~(ME) in time polynomial in the domain size. A major problem for probabilistic reasoning from such conditional knowledge bases is to count models and individuals. To achieve our complexity results, we develop sophisticated counting strategies on interpretations aggregated with respect to the so-called conditional impacts of types, which refine their conditional structure.
机译：我们呈现ALC〜（ME），描述逻辑ALC的概率变体，其允许表示和处理形式的条件陈述“如果E e保持，则在最大熵原理下跟随概率p”。概率被理解为信仰程度，并由聚集语义正式解释。我们证明，在域大小的时间多项式中，可以在ALC〜（ME）中可以基于最大熵分布的近似的检查一致性和吸引推断。从这种有条件知识库的概率推理的主要问题是计算模型和个人。为实现我们的复杂性结果，我们开发了对关于所谓有条件影响的解释的复杂计数策略，从而改进其条件结构。

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