Applying knowledge representation and reasoning to (simple) goal models

We consider simple i∗-style goal models with influence (contribution) links and AND/OR refinement (decomposition), and formalize them by translation into three standard logics that are actively studied in AI Knowledge Representation and Reasoning (KR&R): propositional logic, FOL and description logics (the first formalization is well known). In each case, this provides a semantics for the notation, on which we can base the definition of forward (“what if?”) and backward (“how is this achievable?”) reasoning, of interest to requirements engineers. We consider the manner in which AI KR&R research provides off-the-shelf algorithms that can be used to solve these tasks. We compare the representations by reporting known worst-case complexity results for the reasoning, as well as other criteria such as size/understandability of axiomatization, and ease of extension of modeling language.