Fractals2019: Combinatorial Optimisation with Dynamic Constraint Annealing

TL;DR

This paper introduces Dynamic Constraint Annealing, a novel method for solving dynamic constraint satisfaction problems, demonstrated through tactical optimization in RoboCup soccer simulations and collective behavior thermodynamics.

Contribution

It presents a new optimization technique, Dynamic Constraint Annealing, and applies it to tactical decision-making and thermodynamic optimization in multi-agent systems.

Findings

Successful application to tactical tasks in RoboCup simulations

Effective optimization of collective behaviors under dynamic constraints

Demonstrated improvement over existing methods in dynamic environments

Abstract

Fractals2019 started as a new experimental entry in the RoboCup Soccer 2D Simulation League, based on Gliders2d code base, and advanced to become a RoboCup-2019 champion. We employ combinatorial optimisation methods, within the framework of Guided Self-Organisation, with the search guided by local constraints. We present examples of several tactical tasks based on the 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.