Subsumption architecture

Instead of guiding behavior by symbolic mental representations of the world, subsumption architecture couples sensory information to action selection in an intimate and bottom-up fashion.

Because a robot must have the ability to "avoid objects" in order to "wander around" effectively, the subsumption architecture creates a system in which the higher layers utilize the lower-level competencies.

[5]: 8–12, 15–16 Subsumption architecture attacks the problem of intelligence from a significantly different perspective than traditional AI.

Instead of modelling aspects of human intelligence via symbol manipulation, this approach is aimed at real-time interaction and viable responses to a dynamic lab or office environment.

These discarded signals are common, and is useful for performance because it allows the system to work in real time by dealing with the most immediate information.

This system of AFSM communication is how higher layers subsume lower ones (see figure 1), as well as how the architecture deals with priority and action selection arbitration in general.

The lack of large memory storage, symbolic representations, and central control, however, places it at a disadvantage at learning complex actions, in-depth mapping, and understanding language.