Abstract
A major challenge in knowledge representation is to express uncertain knowledge. One possibility is to combine logic and probability. In this paper, we investigate the logic FO-PCL that uses first-order probabilistic conditionals to formulate uncertain knowledge. Reasoning in FO-PCL employs the principle of maximum entropy which in this context refers to the set of all ground instances of the conditionals in a knowledge base \(\mathcal R\). We formalize the syntactic criterion of FO-PCL interactions in \(\mathcal R\) prohibiting the maximum entropy model computation on the level of conditionals instead of their instances. A set of rules is developed transforming \(\mathcal R\) into an equivalent knowledge base \(\mathcal R^{\prime}\) without FO-PCL interactions.
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Janning, R., Beierle, C. (2011). Transformation Rules for First-Order Probabilistic Conditional Logic Yielding Parametric Uniformity. In: Bach, J., Edelkamp, S. (eds) KI 2011: Advances in Artificial Intelligence. KI 2011. Lecture Notes in Computer Science(), vol 7006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24455-1_15
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DOI: https://doi.org/10.1007/978-3-642-24455-1_15
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