Concept Formation and Language Sharing: Combining Steels’ Language Games with Simple Competitive Learning

Following on from the work of Luc Steels, this paper presents a model of Concept Formation and the subsequent use of Language Games to share those concepts in a society of artificial agents. The system is naturally driven towards stability due to two important mechanisms. In the Concept Formation stage, Simple Competitive Learning results in the formation of normative categories due to the identification of naturally occurring clusters of input data. In the Language Sharing stage, the preference for words that are frequently used creates a positive feedback loop towards linguistic coherence. The model can be seen as an extension to Steels’ work because it makes an important generalisation. In Steels’ model, all inputs are one-dimensional and categories are formed and refined by repeatedly bisecting the space. In this model the Input Space can have any dimension and is partitioned into categories of different shapes which are not fixed. The nature of the Input Space is deliberately very general any number of dimensions and distribution of data can be specified. Consequently the model can be applied to any categorisation situation and is relevant to both scientific research and commercial applications. This paper begins by imitating the model used by Steels and then goes on to show the importance of a “forgetting mechanism” in order to improve communicative success. Higher dimensional spaces are then considered and the effect of various parameters on communicative success is examined. In particular, the importance of choosing the number of objects in the Language Game correctly is explained. Finally, agents’ adaptability to a foreign environment is investigated by mixing two distinct populations.