Observation, Communication and Intelligence in Agent-Based Systems

Chmait, Nader; Dowe, David L.; Green, David G.; Li, Yuan Fang

doi:10.1007/978-3-319-21365-1_6

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…We simulate the behaviour of artificial agents foraging for food in toroidal environments consisting of 400×400 cells, over 10,000 repeated experiments, each consisting of 300 iterations or sequences of observation-action-reward, similar to the architecture described by Chmait et al (2015;. This number of iterations makes it possible for artificial agents to discover a fraction of the environment space, and therefore this number of iterations models the agents' short-term foraging behaviour.…”

Section: Green and Yuan-fang LImentioning

confidence: 99%

Hope, exploration, and equilibrated action schemes

Gozli

Gao

2019

Behav Brain Sci

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Section: Green and Yuan-fang LImentioning

confidence: 99%

Hope, exploration, and equilibrated action schemes

Gozli

Gao

2019

Behav Brain Sci

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“…Chmait et al 36 studied the intelligence of multiagent systems. They considered that the intelligence is influenced by the communication and observation abilities of the member agents.…”

Section: Metrics For Measuring the Intelligence Of Agent-based Systemsmentioning

confidence: 99%

“…Using some tests collaborative agents across different communication and observation abilities were compared. Based on the obtained results, Chmait et al 36 formulated the conclusion that the effectiveness of multiagent systems with low observation/perception abilities can be significantly improved by making the communication more efficient. They presented some situations where these assumptions were not verified, and also analyzed the dependency between the studied factors.…”

Section: Metrics For Measuring the Intelligence Of Agent-based Systemsmentioning

confidence: 99%

MetrIntPair-A Novel Accurate Metric for the Comparison of Two Cooperative Multiagent Systems Intelligence Based on Paired Intelligence Measurements

Iantovics

Rotar

Niazi

2017

Int. J. Intell. Syst.

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In this paper, we propose a novel metric called MetrIntPair (Metric for Pairwise Intelligence Comparison of Agent‐Based Systems) for comparison of two cooperative multiagent systems problem‐solving intelligence. MetrIntPair is able to make an accurate comparison by taking into consideration the variability in intelligence in problem‐solving. The metric could treat the outlier intelligence indicators, intelligence measures that are statistically different from those others. For evaluation of the proposed metric, we realized a case study for two cooperative multiagent systems applied for solving a class of NP‐hard problems. The results of the case study proved that the small difference in the measured intelligence of the multiagent systems is the consequence of the variability. There is no statistical difference between the intelligence quotients/level of the multiagent systems. Both multiagent systems should be classified in the same intelligence class.

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“…Nader Chmait [14,17,18] provided an information-theoretic solution and at first time quantified and analyzed the impact of communication and observation abilities on the intelligence of homogeneous multi-agent system.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Mechanism of Collective Intelligence for Heterogeneous Agents Group

Dai

Zhao

et al. 2020

IEEE Access

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Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action. In this paper, we define and quantify the intelligence level of heterogeneous agents group with the improved Anytime Universal Intelligence Test(AUIT), based on an extension of the existing evaluation of homogeneous agents group. The relationship of intelligence level with agents composition, group size, spatial complexity and testing time is analyzed. The intelligence level of heterogeneous agents groups is compared with the homogeneous ones to analyze the effects of heterogeneity on collective intelligence. Our work will help to understand the essence of collective intelligence more deeply and reveal the effect of various key factors on group intelligence level.

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Observation, Communication and Intelligence in Agent-Based Systems

Cited by 11 publications

References 11 publications

Hope, exploration, and equilibrated action schemes

Hope, exploration, and equilibrated action schemes

MetrIntPair-A Novel Accurate Metric for the Comparison of Two Cooperative Multiagent Systems Intelligence Based on Paired Intelligence Measurements

Evaluation Mechanism of Collective Intelligence for Heterogeneous Agents Group

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