Generating Robust and General Real-World Behavior by Exploiting Regularities at Multiple Levels of Abstraction
Project Description
Intelligent behavior must consider information about the world. Based on this information, an intelligent agent must select the most appropriate action to achieve a task. We hypothesize that agents perform action selection by considering information at different levels of abstraction. We argue that this, in fact, is a principle of intelligence. Abstractions capture regularities in the relationship between perception and suitable action. These regularities are either present in the physics of the world or can be imposed via feedback control. Both types of regularities restrict the evolution of the system state. This facilitates action selection because only actions consistent with these restrictions need to be considered. Regularities exist at different levels of abstraction, with each level contributing different limitations to the evolution of the system state. We hypothesize that the exploitation of regulatities on different levels of abstraction is a principle of intelligence.
To gather support for this hypothesis, we will apply the principle to complex manipulation tasks. We will identify different levels of abstractions, investigate how levels of abstractions depend on specific tasks, and which regularities such abstractions exploit. We aim at understanding how abstraction levels interact, or, more precisely, how different actions generated on the basis of regularities at different levels of abstraction lead to an overall behavior that solves the given tasks. By understanding these relationships, we will make them usable for the systematic solution of multi-level decision and control problems for intelligent behavior. This will enable us to deliver a sound methodological underpinning for generating intelligent behavior based on multi-layer control.
