Handling Uncertainty in Answer Set Programming

We present a probabilistic extension of logic programs under the stable model semantics, inspired by the concept of Markov Logic Networks. The proposed language takes advantage of both formalisms in a single framework, allowing us to represent commonsense reasoning problems that require both logical and probabilistic reasoning in an intuitive and elaboration tolerant way.

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Yi Wang, Joohyung Lee
• Keywords: AAAI 2015, Intelligent Agents