This paper presents an approach to learn the agents' action model (action blueprints orchestrating transitions of the system state) from plan execution sequences. It does so by representing intra-action and interaction dependencies in the form of a maximum satisfiability problem (MAX-SAT), and solving it with a MAX-SAT solver to reconstruct the underlying action model. Unlike previous MAX-SAT driven approaches, our chosen dependencies exploit the relationship between consecutive actions, rendering more accurately learnt models in the end.