Special issue on “Imprecise Probabilities, logic and Rationality”
• 大类 : 工程技术 - 3区
• 小类 : 计算机：人工智能 - 3区
The term “imprecise probability” (IP for short) usually refers to a family of models that provide a description of phenomena for which incomplete or imprecise information has been advanced, overcoming the limitations of traditional models of uncertainty based on precise probabilities. The basic idea of IP models is to extend the standard theory of precise probabilities by considering sets of traditional models. From this perspective the uncertainty (beliefs) of an agent about the possible states of the world is for instance modelled by sets of probabilities rather than a single one. Extensions include, among others, lower and upper previsions, belief functions, sets of desirable gambles and partial preference orderings.
IP is strongly linked with another framework where to express, study and reason on forms of imprecision and incompleteness: logic. Indeed, on the one hand standard logic methodologies have been applied to characterise and study IP notions such as coherence and models in terms of lower and upper probabilities/expectations. On the other hand concepts stemming from the IP tradition have been used to formulate appropriate semantics for statistical relational languages (e.g. by enabling to go beyond limitations such as cyclicity in probabilistic logic programs), whereas viewing IP as an abstract logic structure has lead to its application to domains such as for instance quantum mechanics, sum-of-square optimisation and classical logic itself.
This Special Issue intends to contribute to the state-of-the-art of the interactions and connections between imprecise probabilities and logic, and more generally with formal theories of rationality, the hope being that this cross-disciplinary view will lead to new exciting perspectives for both communities and related areas.
Topics of interests include but are not limited to the following:
IP and (modal/epistemic/dependence/probabilistic/possibilistic…) logic