Robinson E., Ellis T., Channon A. (2007) Neuroevolution of Agents Capable of Reactive and Deliberative Behaviours in Novel and Dynamic Environments. In: Almeida e Costa F., Rocha L.M., Costa E., Harvey I., Coutinho A. (eds) Advances in Artificial Life. ECAL 2007. Lecture Notes in Computer Science, vol 4648. Springer, Berlin, Heidelberg
Both reactive and deliberative qualities are essential for a good action selection mechanism. We present a model that embodies a hybrid of two very different neural network architectures inside an animat: one that controls their high level deliberative behaviours, such as the selection of sub-goals, and one that provides reactive and navigational capabilities. Animats using this model are evolved in novel and dynamic environments, on complex tasks requiring deliberative behaviours: tasks that cannot be solved by reactive mechanisms alone and which would traditionally have their solutions formulated in terms of search-based planning. Significantly, no a priori information is given to the animats, making explicit forward search through state transitions impossible. The complexity of the problem means that animats must first learn to solve sub-goals without receiving any reward. Animats are shown increasingly complex versions of the task, with the results demonstrating, for the first time, incremental neuro-evolutionary learning on such tasks.
Artificial Life Neural Networks Incremental Evolution Reactive and Deliberative Systems Novel and Dynamic Environments