Learning by Learning To Communicate
Mathematics and Computation
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Human intelligence is a product of cooperation among many different specialists. Much of this cooperation must be learned, but we do not yet have a mechanism that explains how this might happen for the "high-level" agile cooperation that permeates our daily lives.I propose that the various specialists learn to cooperate by learning to communicate, basing this proposal on the phenomenon of "communication bootstrapping," in which shared experiences form a basis for agreement on a system of signals. In this dissertation, I lay out a roadmap for investigating this hypothesis, identifying problems that must be overcome in order to understand the capabilities of communication bootstrapping and in order to test whether it is exploited by human intelligence.I then demonstrate progress along the course of investigation laid out in my roadmap:* I establish a measure of "developmental cost" that allows me to eliminate many possible designs* I develop a method of engineering devices for use in models of intelligence, including characterizing their behavior under a wide variety of conditions and compensating for their misbehavior using "failure simplification."* I develop mechanisms that reliably produce communication bootstrapping such that it can be used to connect specialists in an engineered system.* I construct a demonstration system including a simulated world and pair of observers that learn world dynamics via communication bootstrapping.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
artificial intelligence, cognitive science