Pandemonium Architecture - Applications and Influences

Applications and Influences

The pandemonium architecture has been applied to solve several real-world problems, such as translating hand-sent Morse codes and identifying hand-printed letters. The overall accuracy of pandemonium-based models are impressive, even when the system was given a short learning period. For example, Doyle constructed a pandemonium-based system with over 30 complex feature-analyzers. He then fed his system several hundred letters for learning. During this phase, the system analyzed the inputted letter and generated its own output (what the system identifies the letter as). The output from the system was compared against the correct identification, which sends an error signal back to the system to adjust the weights between the features analyzers accordingly. In the testing phase, unfamiliar letters were presented (different style and size of the letters than those that were presented in the learning phase), and the system was able to achieve a near 90% accuracy. Because of its impressive capability to recognize words, all modern theories on how humans read and recognize words follow this hierarchal structure: word recognition begins with feature extractions of the letters, which then activates the letter detectors (e.g., SOLAR, SERIOL, IA, DRC).

Based on the original pandemonium architecture, John Jackson has extended the theory to explain phenomena beyond perception. Jackson offered the analogy of an arena to account for "consciousness". His arena consisted of a stand, a playing field, and a sub-arena. The arena was populated by multitude of demons. The demons that were designated in the playing fields were the active demons, as they represent the active elements of human consciousness. The demons in the stands are to watch those in the playing field until something excites them; each demon is excited by different things. The more excited the demons get, the louder they yell. If a demon yells pass a set threshold, it gets to join the other demons in the playing field and perform its function, which may then excite other demons, and this cycle continues. The sub-arena in the analogy functions as the learning and feedback mechanism of the system. The learning system here is similar to any other neural styled networks, which is through modifying the connection strength between the demons; in other words, how the demons respond to each other’s yelling. This multiple agent approach to human information processing became the assumption for many modern artificial intelligence systems.