Better manufacturing process organization using multi-agent self-organization and co-evolutionary classifier systems: The multibar problem

Hercog, Luis Miramontes

doi:10.1016/j.asoc.2012.04.033

Cited by 9 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-agent models, with agents interacting and co-adapting in a population, have been used to address various real-world problems, such as manufacturing process organization [62] and natural resource allocation [63]. This section describes the proposed evolutionary IPD model with multiple agents of adaptive risk attitudes, which helps provide insights into the relationship of risk adaptation to cooperation.…”

Section: Evolutionary Modelmentioning

confidence: 99%

Cooperation in the evolutionary iterated prisoner’s dilemma game with risk attitude adaptation

Zeng

Chen

2016

Applied Soft Computing

View full text Add to dashboard Cite

The Iterated Prisoner's Dilemma (IPD) game has been commonly used to investigate the cooperation among competitors. However, most previous studies on the IPD focused solely on maximizing players' average payoffs without considering their risk preferences. By introducing the concept of income stream risk into the IPD game, this paper presents a novel evolutionary IPD model with agents seeking to balance between average payoffs and risks with respect to their own risk attitudes. We build a new IPD model of multiple agents, in which agents interact with one another in the pair-wise IPD game while adapting their risk attitudes according to their received payoffs. Agents become more risk averse after their payoffs exceed their aspirations, or become more risk seeking after their payoffs fall short of their aspirations. The aspiration levels of agents are determined based on their historical self-payoff information or the payoff information of the agent population. Simulations are conducted to investigate the emergence of cooperation under these two comparison methods. Results indicate that agents can sustain a highly cooperative equilibrium when they consider only their own historical payoffs as aspirations (called historical comparison) in adjusting their risk attitudes. This holds true even for the IPD with a short game encounter, for which cooperation was previously demonstrated difficult. However, when agents evaluate their payoffs in comparison with the population average payoff (called social comparison), those agents with payoffs below the population average tend to be dissatisfied with the game outcomes. This dissatisfaction will induce more risk-seeking behavior of agents in the IPD game, which will constitute a strong deterrent to the emergence of mutual cooperation in the population.

show abstract

Section: Evolutionary Modelmentioning

confidence: 99%

Cooperation in the evolutionary iterated prisoner’s dilemma game with risk attitude adaptation

Zeng

Chen

2016

Applied Soft Computing

View full text Add to dashboard Cite

show abstract

“…The latter purpose enforces the individual agents to compete in achieving higher rewards through out of the entire process, which makes the study further important since collaboration has to be achieved while competing. Previously, a competitionbased collective learning algorithm has been attempted with learning classifier systems for modelling social behaviours [18]. Although there are many other studies conducted for collective learning of multi-agents with Q learning [15,29], the proposed algorithm implements a competition-based collective learning approach extending Q learning with the notion of individuals and their positions in particle swarm optimisation (PSO) algorithm, which ends up as Q learning embedded in PSO.…”

Section: Introductionmentioning

confidence: 99%

Building Collaboration in Multi-agent Systems Using Reinforcement Learning

Aydın

Fellows

2018

Computational Collective Intelligence

View full text Add to dashboard Cite

This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory, either. Particles are devised with Q learning for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced experimental results are supportive to the proposed idea suggesting that a substantive collaboration can be build via proposed learning algorithm.

show abstract

“…The latter purpose enforces the individual agents to compete in achieving higher rewards through out of the entire process, which makes the study further important since collaboration has to be achieved while competing. Previously, competition-based collective learning algorithm has been attempted with learning classifier systems for modelling social behaviours [17]. Although there are many other studies conducted for collective learning of multi-agents with Q learning [14], [28], the proposed algorithm implements a competition-based collective learning algorithm extending Q learning with the notion of individuals and their positions in particle swarm optimisation (PSO) algorithm, which ends up as Q learning embedded in PSO.…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning algorithm for building collaboration in multi-agent systems

Aydin,

Fellows

2017

Preprint

View full text Add to dashboard Cite

This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via some sort competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory. Particles are devised with Q learning algorithm for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced results are supportive to the algorithmic structures suggesting that a substantive collaboration can be build via proposed learning algorithm.

show abstract

Better manufacturing process organization using multi-agent self-organization and co-evolutionary classifier systems: The multibar problem

Cited by 9 publications

References 22 publications

Cooperation in the evolutionary iterated prisoner’s dilemma game with risk attitude adaptation

Cooperation in the evolutionary iterated prisoner’s dilemma game with risk attitude adaptation

Building Collaboration in Multi-agent Systems Using Reinforcement Learning

A reinforcement learning algorithm for building collaboration in multi-agent systems

Contact Info

Product

Resources

About