Flexible Synchronisation Within RoboCup Environment: a Comparative Analysis

Butler, Marc; Prokopenko, Mikhail; Howard, Thomas M.

doi:10.1007/3-540-45324-5_10

Cited by 19 publications

(16 citation statements)

References 4 publications

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“….) [8]; -asynchronous perception-action activity and limited window of opportunity to perform an action [9]; -autonomous decision-making under constraints enforced by teamwork (collaboration) and opponent (competition) [10]; -conflicts between reactivity and deliberation [11].…”

Section: A Simulated Worldmentioning

confidence: 99%

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

Prokopenko¹,

Wang²

2017

RoboCup 2016: Robot World Cup XX

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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.

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Section: A Simulated Worldmentioning

confidence: 99%

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

Prokopenko¹,

Wang²

2017

RoboCup 2016: Robot World Cup XX

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“…It is important to distinguish between agent characteristics and MAS properties. An agent may be described in terms of the following qualities [4,27,28]:…”

Section: Multi-agent Systems and Self-organizationmentioning

confidence: 99%

Decentralized Multi-Agent Clustering in Scale-free Sensor Networks

Piraveenan

Prokopenko

Wang

et al. 2008

Studies in Computational Intelligence

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“…The Simulator (server) provides a domain (a virtual environment), simulates all the actions of objects in this domain and controls a scenario according to a set of rules. This is a well-known approach to simulation, used, for example, in the RoboCup Simulation League [9,2,1]. The characteristics of the Simulator are specified by a set of server parameters, e.g., the amount of noise added to sensory perceptions and the maximum speed of an agent.…”

Section: Architecturementioning

confidence: 99%

Symbiotic Sensor Networks in Complex Underwater Terrains: A Simulation Framework

Gerasimov

Healy

Prokopenko

et al. 2006

Lecture Notes in Computer Science

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Abstract. This paper presents a new multi-agent physics-based simulation framework (DISCOVERY), supporting experiments with self-organizing underwater sensor and actuator networks. DISCOVERY models mobile autonomous underwater vehicles, distributed sensor and actuator nodes, as well as multi-agent datato-decision integration. The simulator is a real-time system using a discrete action model, fractal-based terrain modelling, with 3D visualization and an evaluation mode, allowing to compute various objective functions and metrics. The quantitative measures of multi-agent dynamics can be used as a feedback for evolving the agent behaviors. An evaluation of a simple simulated scenario with a heterogeneous team is also described.

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Flexible Synchronisation Within RoboCup Environment: a Comparative Analysis

Cited by 19 publications

References 4 publications

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

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

Decentralized Multi-Agent Clustering in Scale-free Sensor Networks

Symbiotic Sensor Networks in Complex Underwater Terrains: A Simulation Framework

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