Proceedings Article10.1115/DETC2017-68042
Towards a Distributed Multiagent Learning-Based Design Optimization Method
Daniel Hulse,Brandon Gigous,Kagan Tumer,Christopher Hoyle,Irem Y. Tumer +4 more
- 03 Nov 2017
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About: This article is published in Design Automation Conference. The article was published on 03 Nov 2017. The article focuses on the topics: Engineering optimization & Multidisciplinary design optimization.
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Citations
KABOOM: an agent-based model for simulating cognitive style in team problem solving
Samuel Lapp,Kathryn W. Jablokow,Christopher McComb +2 more
- 01 Jan 2019
TL;DR: By simulating cognitive style in the context of team problem solving, KABOOM lays the groundwork for the development of team simulations that reflect humans’ diverse problem-solving styles.
Modeling multidisciplinary design with multiagent learning
TL;DR: A new model is presented which captures the distributed nature of complex systems design by decomposing the ability to control design variables to individual computational designers acting on a problem with shared constraints and is shown to produce better-performing designs when computational designers design collaboratively as opposed to independently.
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OUP accepted manuscript
TL;DR: In this paper , a multi-objective optimization algorithm based on a multiagent blackboard system (MABS) is proposed to find the Pareto front in a nuclear engineering design problem.
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Modeling Collaboration in Parameter Design Using Multiagent Learning
Daniel Hulse,Kagan Tumer,Christopher Hoyle,Irem Y. Tumer +3 more
- 02 Jul 2018
TL;DR: A model of collaboration in multidisciplinary engineering based on multiagent learning is presented, which shows that complex engineered systems are often designed through the collaboration of many designers or experts, with good results.
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