Editing Subsumption architecture (section)

==Strengths and weaknesses==

The main advantages of the architecture are:
*the emphasis on iterative development and testing of [[real-time computing|real-time]] systems in their target domain;
*the emphasis on connecting limited, task-specific perception directly to the expressed actions that require it; and
*the emphasis on distributive and parallel control, thereby integrating the perception, control, and action systems in a manner similar to animals.<ref name="Brooks 1999"/>{{rp|172–173}}<ref name="Brooks1990"/>

The main disadvantages of the architecture are:
*the difficulty of designing adaptable [[action selection]] through highly distributed system of inhibition and suppression;<ref name="Arkin 1998"/>{{rp|139–140}} and
*the lack of large memory and symbolic representation, which seems to restrict the architecture from understanding language;

When subsumption architecture was developed, the novel setup and approach of subsumption architecture allowed it to be successful in many important domains where traditional AI had failed, namely [[real-time computing|real-time]] interaction with a dynamic environment.  The lack of large memory storage, symbolic representations, and central control, however, places it at a disadvantage at learning complex actions, in-depth [[robotic mapping|mapping]], and [[natural-language understanding|understanding language]].