幼児の学習バイアスを利用したエージェントによる語意学習の効率化

概要

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Recently, studies on learning of word meanings by agents have begun. In these studies, a human shows objects to an agent and utters words such as ``red or ``box. The agent finds out objects feature represented by each spoken word. In our method, firstly, the agent learns probability distribution p(x) and conditional probability distribution p(x|w), where x is an object feature and w is a word. If a word w does not represent a feature x, p(x) and p(x|w) will be almost same distribution because x is independent of w. This fact enables the agent to use distance between p(x) and p(x|w) when inferring which feature the word represents. Previous works also employ similar stochastic approaches to detect the feature. However, such approaches need a lot of examples to learn correct distributions.
社団法人　人工知能学会の論文
2007-11-01