A Probabilistic Model for Heterogeneous Populations and Related Burn-in Design Problems

Planning and strategizing are essential parts of the design process and are based on the designer’s skill. Further, planningis an abstract skill that can be transferred between similar problems. However, planning and strategy transfer within design have not been effectively modeled within computational agents. This paper presents an approach to represent this strategizing behavior using a probabilistic model. This model is employed to select the operations that computational agents should perform while solving configuration design tasks. This work also demonstrates that this probabilistic model can be used to transfer strategies from human data to computational agents ina way that is general and useful. This study shows a successful • transfer of design strategy from human-to-computer agents, opening up the possibility of deriving high-performing behavior from designers and using it to guide computational design agents. Finally, a quintessential behavior of transfer learning is illustrated by agents while transferring design strategies across different problems, improving agent performance significantly. The work presented in this study leverages a computational framework built by embedding cognitive characteristics into agents, which has shown to mimic human problem-solving in configuration design problems.

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Design Strategy Transfer in Cognitively-Inspired Agents

Volume 2A: 44th Design Automation Conference ◽

10.1115/detc2018-85599 ◽

2018 ◽

Cited By ~ 2

Author(s):

Ayush Raina ◽

Christopher McComb ◽

Jonathan Cagan

Keyword(s):

Probabilistic Model ◽

Computational Design ◽

Design Strategy ◽

Configuration Design ◽

Design Strategies ◽

Computational Framework ◽

Design Problems ◽

High Performing ◽

Transfer Strategies ◽

Strategy Transfer

Planning and strategizing are essential parts of the design process and are based on the designer’s skill. Further, planning is an abstract skill that can be transferred between similar problems. However, planning and strategy transfer within design have not been effectively modeled within computational agents. This paper presents an approach to represent this strategizing behavior using a probabilistic model. This model is employed to select the operations that computational agents should perform while solving configuration design tasks. This work also demonstrates that this probabilistic model can be used to transfer strategies from human data to computational agents in a way that is general and useful. This study shows a successful transfer of design strategy from human-to-computer agents, opening up the possibility of deriving high-performing behavior from designers and using it to guide computational design agents. Finally, a quintessential behavior of transfer learning is illustrated by agents while transferring design strategies across different problems, improving agent performance significantly. The work presented in this study leverages a computational framework built by embedding cognitive characteristics into agents, which has shown to mimic human problem-solving in configuration design problems.

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The artwork and the beholder: A probabilistic model for the joint scaling of persons and objects.

Psychology of Aesthetics Creativity and the Arts ◽

10.1037/aca0000239 ◽

2020 ◽

Vol 14 (2) ◽

pp. 224-236

Author(s):

Georg Hosoya

Keyword(s):

Probabilistic Model

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Probabilistic Model Building Genetic Programming based on Estimation of Bayesian Network

Transactions of the Japanese Society for Artificial Intelligence ◽

10.1527/tjsai.22.37 ◽

2007 ◽

Vol 22 ◽

pp. 37-47 ◽

Cited By ~ 3

Author(s):

Yoshihiko Hasegawa ◽

Hitoshi Iba

Keyword(s):

Genetic Programming ◽

Bayesian Network ◽

Probabilistic Model ◽

Model Building

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Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

Tire Science and Technology ◽

10.2346/1.2345640 ◽

2006 ◽

Vol 34 (3) ◽

pp. 170-194 ◽

Cited By ~ 10

Author(s):

M. Koishi ◽

Z. Shida

Keyword(s):

Inverse Problems ◽

Conceptual Design ◽

Dimensional Space ◽

Design Stage ◽

Objective Functions ◽

Pareto Solutions ◽

Design Problems ◽

Multi Objective ◽

Design Engineers ◽

Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

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