Wednesday, April 20, 2011

Complex Artificial Intelligence at Erice

Following my previous post on an extended version of Uncanny Valley applied to the study of complex systems, I want now to explain with more details my new approach to Artificial Intelligence. I've called this approach "Complex Artificial Intelligence".
There have been numerous models of intelligence. The field of AI has been one of the most prolific since its birth in the middle 1950s. We can roughly class them in four categories:
Symbolic AI: In the early years of AI, most models were based on the theory that human intelligence could be reduced to symbol manipulation. The main outcome of this approach was the development of expert systems based on rule inference and symbolic processing.
Neuron-based AI: The study of artificial neural networks began in the decade before the field AI research was founded. It was based on a formal model of the neuron cell in the form of a simple threshold automaton. Researchers focused on elaborating different types of neural networks and their related learning algorithms. Pattern recognition is the main application field for this approach.
Reactive AI: In the 1990s, behavioral robotic architectures and Artificial Life focused on “intelligence without representation” instead of the classical symbolic computational models. The idea was to work on bio-inspired adaptive systems addressing their basic perceptual and sensorimotor tasks.
Distributed AI: During the same period of time, many projects were dedicated to the development of Distributed Artificial Intelligence and Multi-Agent Systems. The key concept is the abstraction called a software agent: a virtual autonomous entity that has an understanding of its environment and acts upon it. An agent communicates with other agents to achieve a common goal, that one agent alone could not achieve.
While taking advantages of the previous ones, Complex AI considers machine intelligence as a global property that emerges from the large amount of non-linear interactions between numerous small software agents and the environment at the edge of order and chaos.
In the workshop “Image In Action” at Erice (Sicily), I have illustrated this approach using three models: (1) a generalized Game of Life Cellular Automata showing the emergence of complex behaviors at the edge of chaos; (2) the emergence of simple “intelligent” behaviors in the evolution of species in the Lifedrop virtual world; and finally the “schizophrenic” model of the Ms House experiment using EVA (Evolutionary Virtual Agent). Ms House is an intelligent character composed of multiple distinct personalities. Each individual personality is implemented as an autonomous bio-inspired nano-agent which is able to react to the user’s input by computing an appropriate message. A coherent identity emerges from the interactions between these nano-agents and the user at the edge of order and chaos. Ms House is composed of near 30 different personalities at this time. Our goal is to have a swarm of over 100 personalities in the near future.

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