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Dynamic Structures for Evolving Tactics and Strategies in Team Robotics
The autonomous robot systems of the future will be teams of robots with complementary specialisms. At any instant robot interactions determine relational structures, and sequences of these structures describe the team dynamics as trajectories through space and time. These structures can be represented in algebraic forms that are realizable as dynamic multilevel data structures within individual robots, as the basis of emergent team data structures. Such formalisms are necessary for robots to learn new individual and collective behaviours. The theory is illustrated by the example of robot soccer where robot interactions create structures and trajectories essential to the evolution of new tactics and strategies in a changing environment
Presenting Multiagent Challenges in Team Sports Analytics
This paper draws correlations between several challenges and opportunities
within the area of team sports analytics and key research areas within
multiagent systems (MAS). We specifically consider invasion games, defined as
sports where players invade the opposing team's territory and can interact
anywhere on a playing surface such as ice hockey, soccer, and basketball. We
argue that MAS is well-equipped to study invasion games and will benefit both
MAS and sports analytics fields. Our discussion highlights areas for MAS
implementation and further development along two axes: short-term in-game
strategy (coaching) and long-term team planning (management).Comment: 5 pages, 1 figure, In Proceedings of the 22nd International
Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023
Complex networks analysis in team sports performance: multilevel hypernetworks approach to soccer matches
Humans need to interact socially with others and the environment. These interactions
lead to complex systems that elude naïve and casuistic tools for understand these
explanations. One way is to search for mechanisms and patterns of behavior in our
activities. In this thesis, we focused on players’ interactions in team sports performance
and how using complex systems tools, notably complex networks theory and tools, can
contribute to Performance Analysis. We began by exploring Network Theory,
specifically Social Network Analysis (SNA), first applied to Volleyball (experimental
study) and then on soccer (2014 World Cup). The achievements with SNA proved
limited in relevant scenarios (e.g., dynamics of networks on n-ary interactions) and we
moved to other theories and tools from complex networks in order to tap into the
dynamics on/off networks. In our state-of-the-art and review paper we took an
important step to move from SNA to Complex Networks Analysis theories and tools,
such as Hypernetworks Theory and their structural Multilevel analysis. The method
paper explored the Multilevel Hypernetworks Approach to Performance Analysis in
soccer matches (English Premier League 2010-11) considering n-ary cooperation and
competition interactions between sets of players in different levels of analysis. We
presented at an international conference the mathematical formalisms that can express
the players’ relationships and the statistical distributions of the occurrence of the sets
and their ranks, identifying power law statistical distributions regularities and design
(found in some particular exceptions), influenced by coaches’ pre-match arrangement
and soccer rules.Os humanos necessitam interagir socialmente com os outros e com o
envolvimento. Essas interações estão na origem de sistemas complexos cujo
entendimento não é captado através de ferramentas ingénuas e casuísticas. Uma
forma será procurar mecanismos e padrões de comportamento nas atividades.
Nesta tese, o foco centra-se na utilização de ferramentas dos sistemas complexos,
particularmente no contributo da teoria e ferramentas de redes complexas, na
Análise do Desempenho Desportivo baseado nas interações dos jogadores de
equipas desportivas. Começámos por explorar a Teoria das Redes, especificamente
a Análise de Redes Sociais (ARS) no Voleibol (estudo experimental) e depois no
futebol (Campeonato do Mundo de 2014). As aplicações da ARS mostraram-se
limitadas (por exemplo, na dinâmica das redes em interações n-árias) o que nos
trouxe a outras teorias e ferramentas das redes complexas. No capítulo do estadoda-
arte e artigo de revisão publicado, abordámos as vantagens de utilização de
outras teorias e ferramentas, como a análise Multinível e Teoria das Híperredes.
No artigo de métodos, apresentámos a Abordagem de Híperredes Multinível na
Análise do Desempenho em jogos de futebol (Premier League Inglesa 2010-11)
considerando as interações de cooperação e competição nos conjuntos de
jogadores, em diferentes níveis de análise. Numa conferência internacional,
apresentámos os formalismos matemáticos que podem expressar as relações dos
jogadores e as distribuições estatísticas da ocorrência dos conjuntos e a sua ordem,
identificando regularidades de distribuições estatísticas de power law e design
(encontrado nalgumas exceções estatísticas específicas), promovidas pelos
treinadores na preparação dos jogos e constrangidas pelas regras do futebol
Modelling Players' Interactions in Football: A Multilevel Hypernetworks Approach.
Na presente tese procura-se avançar com fundamentação teórica e prática, assim como com demonstrações empíricas referentes à reconceptualização das equipas de futebol enquanto redes sociais complexas. Estas redes evidenciam comportamentos sinérgicos emergentes e auto-organizados cuja complexidade, enraizada nas redes de interações dos jogadores, pode ser discernida através da análise de redes sociais. Não obstante, as técnicas tradicionais de rede exibem algumas limitações que podem levar a dados imprecisos e falaciosos. Essas limitações estão relacionadas com a exagerada ênfase que é colocada nos comportamentos de ataque das equipas, negligenciando-se as ações defensivas. Tal leva a que: a troca de informações incida maioritariamente nos comportamentos de passe; a variabilidade do comportamento dos jogadores seja, na maioria dos casos, desconsiderada; e a maioria das métricas usadas para modelar as interações dos jogadores se baseiem em distâncias geodésicas. Assim, as hiperredes multiníveis são aqui propostas enquanto nova abordagem metodológica capaz de superar aquelas limitações. Esta abordagem multinível caracteriza-se por um conjunto de conceitos e ferramentas metodológicas coerentes com a análise da dinâmica relacional subjacente aos processos sinergísticos evidenciados durante a competição. Por um lado, estes processos foram capturados na dinâmica de alteração das configurações táticas exibidas pelas equipas durante a competição, pela quantificação do tipo de simplices (interações de grupos de jogadores, e.g., 2vs.1) atendendo à localização da bola, e na dinâmica de interação, transformação dos simplices em determinados eventos do jogo. Por outro lado, a aplicação das hiperredes multiníveis permitiu, de igual modo, capturar as tendências de sincronização local (nível meso) emergentes em contextos de prática. Esta tese destacou o valor da adoção de uma abordagem de hiperredes multiníveis para melhorar a compreensão sobre os processos sinérgicos dos jogadores e equipas de futebol emergentes durante a prática e a competição. Estas poderão vir a revelar-se ferramentas promissoras na análise da performance desportiva, tendo igualmente um papel relevante na monitorização e controlo do treino.PALAVRAS-CHAVE: FUTEBOL, CIÊNCIA DAS REDES, HIPERREDES MULTINÍVEL, DINÂMICA DA EQUIPA, ANÁLISE DA PERFORMANCEThis thesis aims to advance practical and theoretical understanding, as well as empirical evidence regarding the re-conceptualisation of Football teams as complex social networks. These networks display synergetic, emergent and self-organised behaviour and the complexity rooted in the networks of players' interactions can be discerned through analysis of social networks. Notwithstanding, traditional network techniques display some limitations that can lead to inaccurate and misleading data. Such limitations are related with an over-emphasis on network attacking behaviours thus neglecting the defensive actions of the opposing team. This leads to: information exchange mainly analysed through passing behaviours; the variability of players' performance is in most cases disregarded; most metrics used to model players' interactions are based on geodesic distances. Thus, multilevel hypernetworks are proposed as a novel methodological approach capable of overriding such limitations. This multilevel approach is characterised by a set of conceptual and methodological tools consistent with analysis of the relational dynamics underlying the synergistic processes evidenced during competition. On the one hand, these processes were captured in the changing dynamics of tactical configurations of teams during competition, by the quantification of the type of simplices (interactions between sub-groups of players, e.g., 2vs.1) in relation to ball location, and in the dynamics of simplices' interactions and transformations in certain game events. On the other hand, the application of multilevel hypernetworks allowed to capture local (meso level) synchronisation tendencies in practice contexts. This thesis highlighted the value of adopting a multilevel hypernetworks approach for enhancing understanding about the synergistic processes of players and football teams emerging during practice and competition. These tools may prove to be promising in the analysis of sports performance, also having an important role in the monitoring and control of training
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration
We consider the problem of cooperative exploration where multiple robots need
to cooperatively explore an unknown region as fast as possible. Multi-agent
reinforcement learning (MARL) has recently become a trending paradigm for
solving this challenge. However, existing MARL-based methods adopt
action-making steps as the metric for exploration efficiency by assuming all
the agents are acting in a fully synchronous manner: i.e., every single agent
produces an action simultaneously and every single action is executed
instantaneously at each time step. Despite its mathematical simplicity, such a
synchronous MARL formulation can be problematic for real-world robotic
applications. It can be typical that different robots may take slightly
different wall-clock times to accomplish an atomic action or even periodically
get lost due to hardware issues. Simply waiting for every robot being ready for
the next action can be particularly time-inefficient. Therefore, we propose an
asynchronous MARL solution, Asynchronous Coordination Explorer (ACE), to tackle
this real-world challenge. We first extend a classical MARL algorithm,
multi-agent PPO (MAPPO), to the asynchronous setting and additionally apply
action-delay randomization to enforce the learned policy to generalize better
to varying action delays in the real world. Moreover, each navigation agent is
represented as a team-size-invariant CNN-based policy, which greatly benefits
real-robot deployment by handling possible robot lost and allows
bandwidth-efficient intra-agent communication through low-dimensional CNN
features. We first validate our approach in a grid-based scenario. Both
simulation and real-robot results show that ACE reduces over 10% actual
exploration time compared with classical approaches. We also apply our
framework to a high-fidelity visual-based environment, Habitat, achieving 28%
improvement in exploration efficiency.Comment: This paper is accepted by AAMAS 2023. The source code can be found in
https://github.com/yang-xy20/async_mapp
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