Method for evaluating plan recovery strategies in dynamic multi-agent environments

Moreira, Leonardo Henrique; Ralha, Célia Ghedini

doi:10.1080/0952813x.2022.2078887

Cited by 2 publications

(1 citation statement)

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“…Leonardo Henrique et al [45], developed a domain-independent statistical framework to assess recovery solutions in dynamic situations. The number of agents, goals, actions, failure probability, and the extent of agent connection were all varied in the authors' simulation studies to validate the suggested methodology.…”

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confidence: 99%

Distributed, Dynamic and Recursive Planning for Holonic Multi-Agent Systems: A Behavioural Model-Based Approach

Dehimi,

Galland,

Tolba

et al. 2023

Electronics

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In this work, we propose a new distributed, dynamic, and recursive planning approach able to consider the hierarchical nature of the holonic agent and the unpredictable evolution of its behaviour. For each new version of the holonic agent, introduced because of the agent members obtaining new roles to achieve new goals and adapt to the changing environment, the approach generates a new plan that can solve the new planning problem associated with this new version against which the plans, executed by the holonic agent, become obsolete. To do this, the approach starts by generating sub-plans capable of solving the planning subproblems associated with the groups of the holonic agent at its different levels. It then recursively links the sub-plans, according to their hierarchical and behavioural dependency, to obtain a global plan. To generate the sub-plans, the approach exploits the behavioural model of the holonic agent’s groups, thereby minimising the computation rate imposed by other multi-agent planning methods. In our work, we have used a concrete case to show and illustrate the usefulness of our approach.

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confidence: 99%