Achieving human-like action planning requires profound reasoning and context-awareness capabilities. It is especially true for autonomous robotic mobile manipulation in dynamic environments. In the case of component failure, the autonomous robotic system requires reliable adaptation capabilities combined with a consistent understanding of the task, environment, and the robot’s capabilities for successful task completion. Recent research has shown that Active Inference, a unifying neuroscientific theory of the brain, has the potential to intrinsically handle substantial uncertainties in the system, resembling the adaptability of humans. These works, however, have the following limitations: (1) no distinction is made between actions with some commonality, capable of satisfying similar tasks, and (2) actions are assumed to be always feasible when preconditions are satisfied, regardless of their context. Given the situation, certain actions satisfying a task might not lead to task succession. This work proposes the AI for retail (Airet) framework, a novel extension of action planning through Active Inference for mobile manipulation. The Airet framework uses Bayesian networks and Ontological Reasoning to facilitate context-awareness in action planning through Active Inference. Reasoning on robot components, action-, manipulation- and environmental constraints is facilitated through a description-logic-based reasoner and an OWL-based ontology containing concepts relevant for action selection in a retail context. The capabilities of the Airet framework are demonstrated through the following cases (1) irrecoverable task & component. Failure prevention when dealing with ill-defined tasks, (2) Selection of the best action given the situation & the component capabilities through context-awareness (3) failure recovery & adaptation when dealing with component failure. Lastly, these situations are compared with research on reactive task planning through Active Inference without context-awareness. This thesis represents a leap forward from the current state-of-the-art in Active Inference for task planning in robotics, laying the foundations for further research in the direction of this thesis