Proceedings of the Annual Meeting of the Cognitive Science Society

We present a novel framework for building pragmatic artificialagents with explicit and trainable semantic representations, us-ing the Rational Speech Act model. We train our agents onsupervised and unsupervised communication games and com-pare their behavior to literal agents lacking pragmatic abilities.For both types of games pragmatic but not literal agents evolvea mutual exclusivity bias. This provides a computational prag-matic account of mutual exclusivity and points out a possi-ble direction for solving the mutual exclusivity bias challengeposed by Gandhi and Lake (2019). We find that pragmaticreasoning can cause the bias either by promoting lexical con-straints during learning, or by affecting online inference. In ad-dition we show that pragmatic abilities lead to faster learningand that this advantage is even stronger when meanings to becommunicated follow a more natural distribution as describedby Zipf’s law.