Probabilistic argumentation systems are based on assumption-based reasoning for obtaining arguments supporting hypotheses and on probability theory to compute probabilities of sup...
We investigate NLTL, a linear-time temporal logic with forgettable past. NLTL can be exponentially more succinct than LTL + Past (which in turn can be more succinct than LTL). We ...
Johnson-Laird and coworkers' Mental Model theory of propositional reasoning is shown to be somewhere in between what logicians have defined as "credulous" and "...
Reasoning about perception of depth and about spatial relations between moving physical objects is a challenging problem. We investigate the representation of depth and motion by m...
This paper describes a system combining model-based and learning-based methods for automated reasoning in large theories, i.e. on a large number of problems that use many axioms, l...