Probabilistic Logic Network - Implementation

Implementation

PLN begins with a term logic foundation, and then adds on elements of probabilistic and combinatory logic, as well as some aspects of predicate logic and autoepistemic logic, to form a complete inference system, tailored for easy integration with software components embodying other (not explicitly logical) aspects of intelligence.

PLN represents truth values as intervals, but with different semantics than in Imprecise Probability Theory. In addition to the interpretation of truth in a probabilistic fashion, a truth value in PLN also has an associated amount of certainty. This generalizes the notion of truth values used in autoepistemic logic, where truth values are either known or unknown, and when known, are either true or false.

The current version of PLN has been used in narrow-AI applications such as the inference of biological hypotheses from knowledge extracted from biological texts via language processing, and to assist the reinforcement learning of an embodied agent, in a simple virtual world, as it is taught to play "fetch".