Abstract The best accuracy arguments for probabilism apply only to credence functions with finite domains, that is, assign at most finitely many propositions. This is a significant limitation. It reveals the support accuracy-first programme in epistemology lot weaker than it seems first glance, and means cannot yet accomplish everything their competitors, pragmatic (Dutch book) arguments, can. ...

Constraint satisfaction problems occur in many areas of computer science, most prominently in artificial intelligence including temporal or spacial reasoning, belief maintenance, machine vision, and scheduling (for an overview see [Kumar, 1992,Dechter, 2003]). Other areas are graph theory, boolean satisfiability, type systems for programming languages, database theory, automatic theorem proving...

Combining first-order logic and probability has long been a goal of AI. Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Unfortunately, it does not have the full power of first-order logic, because it is only defined for finite domains. This paper extends Markov logic to infin...

Many robotic planning applications involve continuous actions with highly non-linear constraints, which cannot be modeled using modern planners that construct a propositional representation. We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints. The outputs of these generat...