An Ontology for Observation of Multiagent Based Simulation

Ralambondrainy, Tiana; Courdier, Rémy; Payet, Denis

doi:10.1109/wi-iatw.2006.43

Cited by 1 publication

(1 citation statement)

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“…Ralambondrainy et al identify the complex task of observing a simulation, for which they propose a separate multi-agent system. 24 They describe an ontology to facilitate the communications of the agents in the second system. The observation agents have three main tasks, namely (1) acquisition of observational elements, (2) processing of simulation results, and (3) presentation of the results to human actors.…”

Section: Related Workmentioning

confidence: 99%

Abstraction of agent interaction processes: Towards large-scale multi-agent models

2013

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The typically large numbers of interactions in agent-based simulations come at considerable computational costs. In this article, we present an approach to reduce the number of interactions based on behavioural patterns that recur during runtime. We employ machine learning techniques to abstract the behaviour of groups of agents to cut down computational complexity while preserving the inherent flexibility of agent-based models. The learned abstractions, which subsume the underlying model agents’ interactions, are constantly tested for their validity: after all, the dynamics of a system may change over time to such an extent that previously learned patterns would not reoccur. An invalid abstraction is, therefore, removed again from the system. The creation and removal of abstractions continues throughout the course of a simulation in order to ensure an adequate adaptation to the system dynamics. Experimental results on biological agent-based simulations show that our proposed approach can successfully reduce the computational complexity during the simulation while maintaining the freedom of arbitrary interactions.

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Section: Related Workmentioning

confidence: 99%