Computational agents based on the BDI framework typically rely on abstract plans and plan refinement to reach a degree of autonomy in dynamic environments, agents are provided with the ability to select how-to achieve their goals by choosing from a set of options. In this work we focus on a related, yet under-studied feature, abstract goals. These constructs refer to the ability of agents to adopt goals that are not fully grounded at the moment of invocation, refining them only when and where needed, the ability to select what-to (concretely) achieve at run-time. We present a preference-based approach to goal refinement, defining preferences based on extended Ceteris Paribus Networks (CP-Nets) for an AgentSpeak(L)-like agent programming language, and mapping the established CP-Nets logic and algorithms to guide the goal refinement step. As a technical contribution, we present an implementation of this method that solely uses a Prolog-like inference engine of the agent’s belief-base to reason about preferences, thus minimally affecting the decision-making mechanisms hard-coded in the agent framework.