An abductive counterfactual reasoning approach in logic programming. Available from http://goo.gl/bx0mIZ

We construct a counterfactual statement when we reason conjecturally about an event which did or did not occur in the past: If an event had occurred, what would have happened? Would it be relevant? Real world examples, as stud-ied by Byrne, Rescher and many others, show that these conditionals involve a complex reasoning process. An intuitive and elegant approach to evaluate counterfactuals, without deep revision mechanisms, is proposed by Pearl. His Do-Calculus identifies causal relations in a Bayesian network resorting to coun-terfactuals. Though leaving out probabilities, we adopt Pearl’s stance, and its prior epistemological justification to counterfactuals in causal Bayesian net-works, but for programs. Logic programming seems a suitable environment for several reasons. First, its inferential arrow is adept at expressing causal direc-tion and conditional reasoning. Secondly, together with its other functionalities such as abduction, integrity constraints, revision, updating and debugging (a form of counterfactual reasoning), it proffers a wide range of expressibility it-self. We show here how programs under the weak completion semantics in an abductive framework, comprising the integrity constraints, can smoothly and uniformly capture well-known and off-the-shelf counterfactual problems and conundrums, taken from the psychological and philosophical literature. Our approach is adroitly reconstructable in other three-valued LP semantics, or re-stricted to two-valued ones