A broad range of theoretical issues dealing with multi-agent learning systems will be investigated. Cooperation is the key to successfully completing a task when more than one agent is involved. Attention will be restricted to learning system where multiple agents cooperate in learning a concept (or function). The ultimate goal is to devise templates for cooperating learning strategies which hopefully can be applied to other practical problems. The goal will be approached by investigating learners with differing convergence and/or correctness criteria as well as other features such as totality of hypothesized programs and resource boundedness. This wide spectrum of type of learners will help in identifying the true nature and complexity of the cooperation one should expect in multi-agent learning systems. In particular, an on-going investigation of the structure of probabilistic and pluralistic hierarchy in FIN-type (and subsequently in other types of)learning will be continued, which recently revealed a new but very intimate cooperative strategy.
