Abstract
Electronic institutions are socially-inspired multi-agent systems, typically operating under a set of policies, which are required to determine system operation and to deal with violations and other non-compliant behaviour. They are often faced with a dynamic population of agents, social network, and environment and their policy should suit this context. However, there is usually a large space of possible system policies, but no tractable systematic method to find an appropriate policy given a joint state of the population, social network, and the environment. We have developed a model of an energy system which encompasses several inter-connected community energy systems. We propose two methods, an offline and an online procedure, which enable this system model to approximately optimise its performance through adaptation and evolution of its operating policy. The policies evolved by our procedures clearly outperform a baseline policy we have designed by hand. Both procedures return policies which are appropriate for a system, given some performance criterion, without a human designer’s intervention. This could lay the foundations for the development of a new methodological paradigm for the engineering of collective adaptive systems based on the convergence of electronic institutions and evolutionary computing.
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Notes
- 1.
Testing policies every 24 time steps and evolving a new generation every two weeks’ time means that a maximum of only 14 policies out of each generation can be tested. Fitness values are initialised to 0, which could be an overestimation. Large population sizes would cause many policies not to be tested, which could result in many bad policies being added to following generations. Hypermutation promotes variability in smaller populations.
- 2.
While this would be a small population size for many GP problems, we have empirically determined it to be appropriate in this case.
- 3.
This refers to functional diversity (a sequence of modes of operation is a consequence of applying one or more policies to the system over time), rather than structural diversity (the shape of the trees which make up a policy).
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Cardoso, R.P., Rossetti, R.J.F., Hart, E., Burth Kurka, D., Pitt, J. (2018). Engineering Sustainable and Adaptive Systems in Dynamic and Unpredictable Environments. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Distributed Systems. ISoLA 2018. Lecture Notes in Computer Science(), vol 11246. Springer, Cham. https://doi.org/10.1007/978-3-030-03424-5_15
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