This paper presents a novel approach to the problem of action selection for an autonomous agent. An agent is viewed as a collection of competence modules. Action selection is modelled as an emergent property of an activation/inhibition dynamics among these modules. A concrete action selection algorithm is presented and a detailed account of the results is given. This algorithm combines characteristics of both traditional planners and reactive systems. It provides global parameters, which one can use to tune the action selection behavior along several criteria, such as goal orientedness versus situation orientedness, bias towards ongoing plans versus adaptivity, and sensitivity to goal conflicts and ‘thoughtfulness’ versus speed.