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Abstract. Although probabilistic knowledge representations and probabilistic reasoning have by now secured their position in intelligent systems research, it is not uncommon to encounter misunderstanding of their foundations and lack of appreciation for their strengths. This paper discusses ve issues related to in-telligent systems research and shows how they are addressed by the probabilistic knowledge representations. Directed probabilistic graphs capture essential qualitative properties of a domain, along with its causal structure. Concepts such as relevance and con icting evi-dence have a natural, formally sound meaning in probabilistic models. Probabilis-tic schemes support sound reasoning at a variety of levels ranging from purely quantitative to purely qualitative levels. Probabilistic knowledge representations provide insight into the foundations of logic-based schemes for reasoning under uncertainty, showing their diculties in highly uncertain domains. Finally, proba-bilistic knowledge representations support automatic generation of understandable explanations of inference for the sake of user interfaces to intelligent systems.