Propositional Measure Logic

However, to deal with ambiguity and partial information, new approache s have emerged - examples of which are fuzzy logic, probabilistic modal logic, Bayesian networks and belief-based systems. Even though progress has been made, these approaches genera lly have a limitation: the probability or degree of belief, in general, being kept out of the l ogical semantics, remaining at another level of interpretation on a deterministic model. In other w ords, maintaining the binary characteristic of truth - true or false, with uncertainty being treate d as associated with models, rather than a property of logical language in itself. The proposed logic will be used to solve the problem of tackling certain types of uncertainty and imprecision with Bayesian Networks. The aim is to take advantage of the conceptual and practical benefits of this sy stem in practical situations that have not yet been adequately explored.