Incremental Learning from Positive Examples

Classical supervised learning techniques are generally based on an inductive mechanism able to generalise a model from a set of positive examples, assuring its consistency with respect to a set of negative examples. In case of learning from positive evidence only, the problem of over-generalisation comes into account. This paper proposes a general technique for incremental multi-class learning from positive examples only, which has been embedded in the learning system INTHELEX. The idea is to incrementally suppose the positive evidence for a class to be a negative evidence for all other classes until the environment explicitly declares the contrary. An application of the proposed technique to the agent learning domain has been provided. The proposed framework has been used to simulate an agent learning and revising in an incremental way a logical model of a task by imitating skilled agents. In particular, demonstrations are incrementally received and used as training examples while the agent interacts in a stochastic environment. The experimental results prove the validity of the proposed approach on this application domain.