SESiL: Social, Evolutionary Supported Learning

Tianshu Zhao, Zinovi Rabinovich

Keywords: Multi-Agent Learning, Social Learning, Evolutionary Computing

Abstract:

Social Learning describes several variations of interaction between a learning agent and a source of (potentially) beneficial behaviour. Mainly, though outliers exist, there are three forms of this interaction. First, is the obvious "monkey-see-monkey-do", learning by the imitation of the observed behaviour. Second, the "teacher-learner" relationship, where an experienced agent actively guides or instructs the learner. Finally, knowledge extraction from observation, where an agent generalizes from the observed interactions of others with the environment to its own needs and goals. However, in spite of the enormous volume of work in social learning, once commonality persists — an agent can directly benefit from the additional information and improve its own behaviour. But what happens if the agent has identified the benefits of other's behaviour, but cannot absorb them?

In this paper, we study the support that social learning can garner from an evolutionary perspective on the process: rather than absorbing additional behavioural information directly, agents share and merge their behavioural information by choosing a mate. It is the children that represent and carry the socially learned, combined behaviour. We term the combined learning process SESiL (Social, Evolutionary Supported Learning). Besides the formal definition of the framework, we provide experimental studies of its properties. Specifically, we deploy SESiL in multi-tasking classification. Starting from a population of agents who have been partially-pretrained on small subsets of labels, we give them the agency to seek and choose a mate based on the observed classification performance. Presuming availability of a "genetic merger" operator (in our case, classifier network merger), we allow the mutually-agreed mating pairs to be replaced by two children that carry their (imperfectly) merged knowledge. We baseline SESiL against a full-data access classifier, a distributed learner (split-learn-merge) and several forms of more classical evolutionary compute, where agents have no say in choosing a mate, but are bred following their overall performance.

Area: Learning and Adaptation (LEARN)

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Submission Number: 1200