19. Architectures: GPS, SOAR, Subsumption, Society of Mind

General Problem Solver

By analyzing the difference between a current state and desired state, a set of intermediary steps can be created to solve the problem = problem solving hypothesis.

SOAR (State Operator And Result)

SOAR Components:

  • Long-term memory
  • Short-term memory
  • Vision system
  • Action system

Key parts of the SOAR architecture:

  1. Long-term memory and short-term memory
  2. Assertions and rules (production)
  3. Preferences systems between the rules
  4. Problem spaces (make a space and search through that space)
  5. Universal sub-goaling: new problems that emerge during the resolution become entire new goal with assertions rules, etc.

SOAR relies on the symbol system hypothesis. It primarily deals with deliberative thinking.

Emotion machine

Created by Marvin Minsky to tackle more complex problems, this architecture involves thinking about several layers:

  • Reflective thinking
    • Self-conscious
    • Self-reflective
  • Deliberative thinking
  • Learned reaction
  • Instinctive reaction

It is based upon the common sense hypothesis.


System created by Rodney Brooks. By generalizing layers of abstraction in the building of robots (such as for robot vision and movement), modifications to certain layers don’t interfere with other layers computation, allowing for better incremental improvement of the system as a whole.

It primarily deals with instinctive reaction and learned reaction.

This is the creature hypothesis, if a machine can act as an insect, then it will be easy to develop further later. This architecture relies upon the following principles:

  1. No representation
  2. Use the world instead of model: reacting to the world constantly
  3. Finite state machines


Based upon language, this system involves perception and description of events, which then allow to understand stories and further, culture both at the macro (country, religion…) and micro (family…) levels. This system relies upon the strong story hypothesis.

1. Introduction and scope

What is Artifical Intelligence?

Algorithms, enabled by constraints, exposed by representations, that support the building of models targeted at thinking, perception and action, and the loops that tie them together.

Artificial Intelligence is applied through problem solving procedures, methods, techniques and algorithms.

How to approach a problem

Generate solutions and test to obtain positive or negative results.

This approach involved building generators with certain properties: not redundant (should not give the same solution twice), they should also be informable (able to select a category and disregard other)

Rumpelstiltskin principle

Being able to name what you’re talking about gives you power over it, to understand and solve problems. Naming things grants power over concepts.

Difference between trivial and simple

Trivial ideas implies that they are worthless, useless. In AI, the most simple ideas are often the most powerful.

The benefits of language

  1. Enables to tell stories
  2. Enables to marshal the resources of the perceptual apparatus. It lets us imagine things that we never saw before.