Language Evolution and Computation Bibliography

This paper describes efficient word meaning acquisition for infant agents (IAs) based on learning biases that are observed in children's language development. An IA acquires word meanings through learning the relations among visual features of objects and acoustic features of human speech. In this task, the IA has to find out which visual features are indicated by the speech. Previous works introduced stochastic approaches to do this, however, such approaches need many examples to achieve high accuracy. In this paper, firstly, we propose a word meaning acquisition method for the IA based on an Online-EM algorithm without learning biases. Then, we implement two types of biases into it to accelerate the word meaning acquisition. Experimental results show that the proposed method with biases can efficiently acquire word meanings.

In word meaning acquisition through interactions among humans and agents, the efficiency of the learning depends largely on the dialog strategies the agents have. This paper describes automatic acquisition of dialog strategies through interaction between two agents. In the experiments, two agents infer each other's comprehension level from its facial expressions and utterances to acquire efficient strategies. Q-learning is applied to a strategy acquisition mechanism. Firstly, experiments are carried out through the interaction between a mother agent, who knows all the word meanings, and a child agent with no initial word meaning. The experimental results showed that the mother agent acquires a teaching strategy, while the child agent acquires an asking strategy. Next, the experiments of interaction between a human and an agent are investigated to evaluate the acquired strategies. The results showed the effectiveness of both strategies of teaching and asking.