A multi-layered perceptron type neural network is presented and analyzed in this paper. All neuronal parameters such as input, output, action potential and connection weight are encoded by quaternions, which are a class of hypercomplex number system. Local analytic condition is imposed on the activation function in updating neurons’ states in order to construct learning algorithm for this network. An error back-propagation algorithm is introduced for modifying the connection weights of the network.

Grouping motion of creatures is observed in various scenes in nature. As its typical cases, bird flocking, land animal herding, and fish schooling are well-known. Many observations have shown that there are no leading agents to control the behavior of the group. Several models have been proposed for describing the flocking behavior. In these models, some fixed rule is given to each of the individuals a priori for their interactions in reductive and rigid manner. Instead of this, we have proposed a new framework for self-organized flocking of agents by reinforcement learning. It will become important to introduce a learning scheme for making collective behavior in artificial autonomous distributed systems. In this paper, anti-predator behaviors of agents are examined by our scheme through computer simulations. We demonstrate the feature of behavior under two learning modes against agents of the same kind and predators