Emergent models of cognition are attractive for artificial cognitive agents because they overcome the brittleness of systems that are fully specified in axiomatic terms at design time, increasing, for example, the ability to deal with uncertainty and unforeseen events. When the agent is created to fulfil specific requirements defined by a given application, there is an apparent conflict between the emergent (i.e. self-defining) nature of the agent's behaviour and the pre-specified (i.e. axiomatically-defined) nature of the requirements.
Here, we develop a framework for the design of emergent models of cognition whose behaviour can be shaped to fulfil application requirements while retaining the desired characteristics of emergence. We achieve this by viewing the artificial agent as forming an eco-system with the environment in which it is deployed. Consequently, the objective function that determines the agent's behaviour is cast in terms that factor in interaction with the environment (while not being controlled by it) and therefore implicitly includes the application requirements.
This framework is particularly relevant to application driven research where artificial agents are designed to interact with humans in a certain manner. We illustrate this with the example of robot-enhanced therapy for children with autism spectrum disorder