 Nilsson’s work on probabilistic logic (1986, 1993) has sparked a lot of research on probabilistic reasoning in artificial intelligence (Hansen and Jaumard 2000; chapter 2 of Haenni et al. 2011). However, it should be noted that although Theorem 5 states that the functions \(L_{\Gamma,\phi}\) and \(U_{\Gamma,\phi}\) are effectively determinable from the sentences in \(\Gamma\cup\{\phi\}\), the computational complexity of this problem is quite high (Georgakopoulos et al. 1988, Kavvadias and Papadimitriou 1990), and thus finding these functions quickly becomes computationally unfeasible in real-world applications. Contemporary approaches based on probabilistic argumentation systems and probabilistic networks are better capable of handling these computational challenges. Furthermore, probabilistic argumentation systems are closely related to Dempster-Shafer theory (Dempster 1968; Shafer 1976; Haenni and Lehmann 2003). However, an extended discussion of these approaches is beyond the scope of (the current version of) this entry; see (Haenni et al. 2011) for a recent survey.