Agents with General Intelligence

An agent capable of general intelligence approximates the knowledge level on an unbounded set of problems with little inherent knowledge of the domain. The capabilities needed to support general intelligence are not generally known (although many have been empirically determined to be of significant importance; e.g., learning) Additionally, no theory exists for determining either the necessary or sufficient structures needed to support particular capabilities and certainly not to support general intelligence (although see Unified Theories of Cognition for work in developing such theories).

As direction and inspiration towards the development of such theory or tools, Newell posits that one way to approach sufficiency is by modelling human cognition in computational layers or bands.

He suggests that these computational layers emerge from the natural hierarchy of information processing. The lowest layers comprising the biological band perform the most primitive tasks in a machine-efficient way. The next level up, the cognitive band, is postulated as the first strong layer and the first layer predominated by symbols. This can be taken to be the symbol layer. Many of the architectures studied in this set of documents operate at the symbol level in order to provide information processing in the rational band.

The place where analytical tools could most benefit AI is in the analysis of necessary and sufficient support for capabilities. Also needed is a method of decomposition which could bound or define the capabilities necessary to support general intelligence.


Press UP to go to the list of theories.

Press HOME to go to the Table of Contents.