We study proof systems for reasoning about logical consequences and refinement of structured specifications, based on similar systems proposed earlier in the literature [ST 88, Wi...
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
The last few years have seen the advent of a new breed of decision procedures for various fragments of first-order logic based on ional abstraction. A lazy satisfiability checker ...
Recovery can be extended to new domains at reduced logging cost by exploiting "logical" log operations. During recovery, a logical log operation may read data values fro...
In this paper, we propose a model named Logical Markov Decision Processes with Negation for Relational Reinforcement Learning for applying Reinforcement Learning algorithms on the ...