Fractals2019: Combinatorial Optimisation with Dynamic Constraint Annealing

Fractals2019 started as a new experimental entry in the RoboCup Soccer 2D Simulation League, based on Gliders2d code base, and advanced to a team winning RoboCup-2019 championship. Our approach is centred on combinatorial optimisation methods, within the framework of Guided Self-Organisation (GSO), with the search guided by local constraints. We present examples of several tactical tasks based on the fully released Gliders2d code (version v2), including the search for an optimal assignment of heterogeneous player types, as well as blocking behaviours, offside trap, and attacking formations. We propose a new method, Dynamic Constraint Annealing, for solving dynamic constraint satisfaction problems, and apply it to optimise thermodynamic potential of collective behaviours, under dynamically induced constraints. 1 Introduction The RoboCup Soccer 2D Simulation League provides a rich dynamic environment, facilitated by the RoboCup Soccer Simulator (RCSS), aimed to test advances in decentralised collective behaviours of autonomous agents. The challenges include concurrent adversarial actions, computational nondetermin-ism, noise and latency in asynchronous perception and actuation, and limited processing time [1-9]. Over the years the progress of the League has been supported by several important base code releases, covering both low-level skills and standardised world models of simulated agents [10-13]. The release in 2010 of the base code of HELIOS team, agent2d-3.0.0, later upgraded to agent2d-3.1.1,