Additive Modular Learning in Preemptrons

Cognitive scientists, AI researchers in particular, have long-recognized the enormous benefits of modularity (e.g., Simon, 1969), as well as the need for self-organization (Samuel, 1967) in creating artifacts whose complexity approaches that of human intelligence. And yet these two goals seem almost incompatible, since truly modular systems are usually designed, and systems that truly learn are inherently nonmodular and produce only simple behaviors. Our paper seeks to remedy this shortcoming by developing a new architecture of Additive Adaptive Modules which we instantiate as Addam, a modular agent whose behavioral repertoire evolves as the complexity of the environment is increased.1

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