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This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.Experiment Label: 202300120-MTOAExperiment design: Agents specialize through expressing their preferences.Experiment setting: Agents are trained with respect to different tasks and then coordinate upon acting on them. Each time they disagree, one agent adapts its knowledge with respect to the current task.Hypotheses: The higher is the number of carried out tasks, the higher the average accuracy is.Detailed information can be found in index.html or notebook.ipynb.[1] https://sake.re/20230120-MTOA[2] https://gitlab.inria.fr/moex/lazylav/
Cultural Evolution, Multi-agent Simulation
Cultural Evolution, Multi-agent Simulation
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |