Emergence of Conventions for Efficiently Resolving Conflicts in Complex Networks

Sugawara, Toshiharu

doi:10.1109/wi-iat.2014.171

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Clearly, the scale-free network is the most efficient network for achieving high level of consensus compared with the other two networks. Previous studies have shown that this effect is due to the small graph diameter of scale-free networks 11 39 40 41 .…”

Section: Resultsmentioning

confidence: 97%

Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

Tan

et al. 2016

Sci Rep

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Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics.

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Section: Resultsmentioning

confidence: 97%

Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

Tan

et al. 2016

Sci Rep

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“…operational constraints. This happens in contrast to other approaches where agents learn by iteratively interacting with a single opponent from the population [32] [28], or by playing repeatedly with randomly chosen neighbours [4].…”

Section: Related Workmentioning

confidence: 97%

Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods

Kravaris,

Spatharis,

Bastas

et al. 2019

Preprint

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In this article, we report on the efficiency and effectiveness of multiagent reinforcement learning methods (MARL) for the computation of flight delays to resolve congestion problems in the Air Traffic Management (ATM) domain. Specifically, we aim to resolve cases where demand of airspace use exceeds capacity (demand-capacity problems), via imposing ground delays to flights at the pre-tactical stage of operations (i.e. few days to few hours before operation). Casting this into the multiagent domain, agents, representing flights, need to decide on own delays w.r.t. own preferences, having no information about others' payoffs, preferences and constraints, while they plan to execute their trajectories jointly with others, adhering to operational constraints. Specifically, we formalize the problem as a multiagent Markov Decision Process (MA-MDP) and we show that it can be considered as a Markov game in which interacting agents need to reach an equilibrium: What makes the problem more interesting is the dynamic setting in which agents operate, which is also due to the unforeseen, emergent effects of their decisions in the whole system. We propose collaborative multiagent reinforcement learning methods to resolve demand-capacity imbalances: Extensive experimental study on real-world cases, shows the potential of the proposed approaches in resolving problems, while advanced visualizations provide detailed views towards understanding the quality of solutions provided.

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“…RL is suitably used for learning through trial and error by mapping situations that lead to the discovery of actions that gain the most reward (exploration) and executing actions to maximize a numerical reward signal (exploitation) (Sutton & Barto, 2018). Within the context of NorMAS, RL is a method to invoke convention emergence or norm emergence (Frantz et al, 2014(Frantz et al, , 2015Hosseini & Ulieru, 2012;Mashayekhi et al, 2022;Neufeld et al, 2021;Pujol et al, 2005;Riveret et al, 2014aRiveret et al, , 2014bSen & Airiau, 2007;Shoham & Tennenholtz, 1992, 1997Sugawara, 2014;Yu et al, 2013Yu et al, , 2014Yu et al, , 2015Yu et al, , 2017. The current de-facto standard algorithm used in past studies to induce norm emergence using RL is QL (Sutton & Barto, 2018;Watkins & Dayan, 1992), a model-free RL algorithm, in the case of NorMAS through social learning (learning from interactions with other agents).…”

Section: Norm Emergencementioning

confidence: 99%

Norm Augmented Reinforcement Learning Agents With Synthesized Normative Rules

Kadir,

Selamat,

Krejcar

2024

Journal of Cases on Information Technology

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The dynamic deontic (DD) is a norm synthesis framework that extracts normative rules from reinforcement learning (RL), however it was not designed to be applied in agent coordination. This study proposes a norm augmented reinforcement learning framework (NARLF) that extends said model to include a norm deliberation mechanism for learned norms re-imputation for norm biased decision-making RL agents. This study aims to test the effects of synthesized norms applied on-line and off-line on agent learning performance. The framework consists of the DD framework extended with a pre-processing and deliberation component to allow re-imputation of normative rules. A deliberation model, the Norm Augmented Q-Table (NAugQT), is proposed to map normative rules into RL agents via q-values weight updates. Results show that the framework is able to map and improve RL agent's performance but only when synthesized off-line edited absolute norm salience value norms are used. This shows limitations when unstable salience norms are applied. Improvement in norm extraction and pre-processing are required.

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Emergence of Conventions for Efficiently Resolving Conflicts in Complex Networks

Cited by 5 publications

References 9 publications

Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods

Norm Augmented Reinforcement Learning Agents With Synthesized Normative Rules

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