1 October 2008 - 28 February 2010
Computer systems seldom operate in isolation and the outcome of learning tasks on one component may affect a related task on another. For example learning how best to redirect network traffic will once implemented affect the solution that should be adopted at an adjacent node. Cognitive systems composed of multiple agents are another example in which different components may be adapting their behaviour to achieve certain goals, the effects of which will influence the operating environment of other components.
The design and analysis of systems involving interacting learning systems is still in its infancy, particularly when we consider theoretical analysis that can be used to guide their design, and if we include self-organisation as a design principle. A related set of challenges arise when we consider integrating information from diverse sources as for example in distributed sensor networks. Once again learning must be used to decide how to filter the data to ensure the network can provide informed responses to a range of different queries. Learning at one node of the network will influence the optimisations at other nodes. The key objective that can enable solutions in all of these applications is to build a well-founded theoretical framework analysing learning in a game theoretic setting. The learning approach can deliver the flexibility, robustness and scalability that are properties required for many applications of cognitive systems, for example in robotics. Such a framework can then provide the criteria that can be used to design and optimise multicomponent systems for a wide range of applications.
