Combining Propositional Logic with Maximum Entropy Reasoning on Probability Models

We present a system for non-monotonic reasoning based on the probability calculus. This calculus incorporates this type of reasoning in two ways: Non-monotonic decisions (which can be treated as decisions under incomplete knowledge as well) can be the result of reasoning in a single probability model (via conditionalization) or in a set of probability models (via additional principles of rational decisions). But probability theory is too ne-grained to model common sense reasoning in general (think about "paradoxes" due to the unexpected existence of certain P-Models ((2, 15])). The remaining degrees of freedom have to be lled (of course without introducing subjective biases). We therefore use additional (context-sensitive) constraints (resp. principles), which are able to support rational decisions based on incomplete knowledge. These principles have to be global (context-dependent on all assumptions) to avoid loosing the sensitivity of the language to the assumptions. The central principle of rational decisions used by our system is the method of Maximum En-tropy (MaxEnt), which is a well founded extension of probability theory with global properties