Cross-situational and supervised learning in the emergence of communication

Details Cross-situational and supervised learning in the emergence of communication Cross-situational and supervised learning in the emergence of communication

2009-01-26

arxiv.org/abs/0901.4012v3

Interaction Studies: Social Behaviour and Communication in Biological and Artificial Systems, 12, 119-133 (2011)

Computer Science

Learning

Scientific paper

10.1075/is.12.1.05fon

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the realistic limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.

Affiliated with

Also associated with

No associations

Rating

Cross-situational and supervised learning in the emergence of communication does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Cross-situational and supervised learning in the emergence of communication, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Cross-situational and supervised learning in the emergence of communication will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-372110