Disruptive Innovations in RoboCup 2D Soccer Simulation League: From Cyberoos’98 to Gliders2016

Abstract

We review disruptive innovations introduced in the RoboCup 2D Soccer Simulation League over the twenty years since its inception, and trace the progress of our champion team (Gliders). We conjecture that the League has been developing as an ecosystem shaped by diverse approaches taken by participating teams, increasing in its overall complexity. A common feature is that different champion teams succeeded in finding a way to decompose the enormous search-space of possible single-and multi-agent behaviours, by automating the exploration of the problem space with various techniques which accelerated the software development efforts. These methods included interactive debugging, machine learning, automated planning, and opponent modelling. The winning approach developed by Gliders is centred on human-based evolutionary computation which optimised several components such as an action-dependent evaluation function, dynamic tactics with Voronoi diagrams, information dynamics, and bio-inspired collective behaviour.

Gliders2d: Source Code Base for RoboCup 2D Soccer Simulation League

“…Each solution, represented as the team source code, can be interpreted as a "genotype", encoding the entire team behaviour in a set of "design points". A design point, in the context of a data-farming experiment, describes a specific combination of input parameters [31], defining either a single parameter (e.g., pressing level), complex multi-agent tactics (e.g., a set of conditional statements shaping a positioning scheme for several players), or multi-agent communication protocols [9,10,32].…”

“…The RoboCup Soccer 2D Simulation League contributes to the overall RoboCup initiative, sharing its inspirational Millennium challenge: producing a team of fully autonomous humanoid soccer players capable of winning a soccer game against the 2050 FIFA World Cup holder, while complying with the official FIFA rules [1]. Over the years, the 2D Simulation League made several important advances in autonomous decision-making under constraints, flexible tactical planning, collective behaviour and teamwork, communication and coordination, as well as opponent modelling and adaptation [2,3,4,5,6,7,8,9,10]. These advances are to a large extent underpinned by the standardisation of many low-level behaviours, world model updates and debugging tools, captured by several notable base code releases, offered by "CMUnited" team from Carnegie Mellon University (USA) [11,12], "UvA Trilearn" team from University of Amsterdam (The Netherlands) [13], "MarliK" team from University of Guilan (Iran) [14], and "HELIOS" team from AIST Information Technology Research Institute (Japan) [15].…”

“…As a result, almost 80% of the League's teams eventually switched their code base to agent2d over the next few years [9]. The 2016 champion team, Gliders2016 [16,9], was also based on the well-developed code base of agent2d-3.1.1 [15], and fragments of MarliK source code [14], all written in C++.…”

Fractals2019: Combinatorial Optimisation with Dynamic Constraint Annealing

“…Fractals2019 is a new team which is partially based on Gliders2d [39], while retaining some elements of our previous champion team Gliders2016 [33,38]. To a large extent, Fractals2019 is an experimental entry, motivated by a new set of aims.…”

“…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 nondeterminism, noise and latency in asynchronous perception and actuation, and limited processing time [3,5,7,29,37,38,42,43,46]. The League progress has been supported by several important base code releases, covering both low-level skills and standardised world models of simulated agents [1,22,45,47].…”