Cooperative Sign Language Tutoring: A Multiagent Approach

Sign languages can be learned effectively only with frequent feedback from an expert in the field. The expert needs to watch a performed sign, and decide whether the sign has been performed well based on his/her previous knowledge about the sign. The expert’s role can be imitated by an automatic system, which uses a training set as its knowledge base to train a classifier that can decide whether the performed sign is correct. However, when the system does not have enough previous knowledge about a given sign, the decision will not be accurate. Accordingly, we propose a multiagent architecture in which agents cooperate with each other to decide on the correct classification of performed signs. We apply different cooperation strategies and test their performances in varying environments. Further, through analysis of the multiagent system, we can discover inherent properties of sign languages, such as the existence of dialects.