Transferring Design Strategies From Human to Computer and Across Design Problems

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Ayush Raina ◽  
Jonathan Cagan ◽  
Christopher McComb
Keyword(s):  
Computational Design ◽  
Configuration Design ◽  
Probabilistic Representation ◽  
Design Strategies ◽  
Design Problems ◽  
Agent Based ◽  
High Performing ◽  
Transfer Strategies ◽  
Human Problem ◽  
Over Time

Abstract Solving any design problem involves planning and strategizing, where intermediate processes are identified and then sequenced. This is an abstract skill that designers learn over time and then use across similar problems. However, this transfer of strategies in design has not been effectively modeled or leveraged within computational agents. This note presents an approach to represent design strategies using a probabilistic model. The model provides a mechanism to generate new designs based on certain design strategies while solving configuration design task in a sequential manner. This work also demonstrates that this probabilistic representation can be used to transfer strategies from human designers to computational design agents in a way that is general and useful. This transfer-driven approach opens up the possibility of identifying high-performing behavior in human designers and using it to guide computational design agents. Finally, a quintessential behavior of transfer learning is illustrated by agents as transferring design strategies across different problems led to an improvement in agent performance. The work presented in this study leverages the Cognitively Inspired Simulated Annealing Teams (CISAT) framework, an agent-based model that has been shown to mimic human problem-solving in configuration design problems.

scholarly journals Design Strategy Transfer in Cognitively-Inspired Agents

2018 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Ayush Raina
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, 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.

scholarly journals Design Strategy Transfer in Cognitively-Inspired Agents

2018 ◽  
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.

scholarly journals Mining Process Heuristics from Designer Action Data via Hidden Markov Models

2018 ◽  
Author(s):  
Christopher McComb ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Topology Design ◽  
Configuration Design ◽  
Design Problems ◽  
High Performing ◽  
Theoretical Approaches ◽  
Behavioral Studies ◽  
Human Problem

Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and parameter design. High-performing designers are distinguished by their opportunistic tuning of parameters early in the process, enabling a more effective and nuanced search for solutions.

scholarly journals Mining Process Heuristics From Designer Action Data via Hidden Markov Models

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Topology Design ◽  
Configuration Design ◽  
Design Problems ◽  
High Performing ◽  
Theoretical Approaches ◽  
Behavioral Studies ◽  
Human Problem

Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and parameter design. High-performing designers are distinguished by their opportunistic tuning of parameters early in the process, enabling a more effective and nuanced search for solutions.

Material and Configuration Design Strategies towards Flexible and Wearable Power Supply Devices: A Review

2021 ◽  
Author(s):  
Jiyuan Gao ◽  
Kezheng Shang ◽  
Yichun Ding ◽  
Zhenhai Wen
Keyword(s):  
Human Health ◽  
Health Monitoring ◽  
Power Supply ◽  
Wearable Sensors ◽  
Configuration Design ◽  
Design Strategies ◽  
Human Machine Interfaces

Flexible and wearable sensors have shown great potential in tremendous applications such as human health monitoring, smart robots, and human–machine interfaces, yet the lack of suitable flexible power supply devices...

Agent-Based Network Infrastructure for E-Communities

2011 ◽  
pp. 428-438
Author(s):  
M. Mari ◽  
A. Poggi ◽  
M. Tomaiuolo
Keyword(s):  
Virtual Communities ◽  
Network Infrastructure ◽  
Agent Based ◽  
Over Time

Virtual communities represent the gathering of people, in an online “space” where they come, communicate, connect, and get to know each other better over time. Virtual communities can be created for different purposes moving from the simple socialization among people to the collaboration among remote people working on shared projects.

Product Form Evolution

2011 ◽  
pp. 499-512 ◽  
Author(s):  
Andrew Muir Wood ◽  
James Moultrie ◽  
Claudia Eckert
Keyword(s):  
Product Design ◽  
Development Strategies ◽  
Consumer Perception ◽  
Product Form ◽  
Design Teams ◽  
Design Strategies ◽  
Structured Interviews ◽  
Product Forms ◽  
Over Time

Companies are coming round to the idea that function and form are complimentary factors in improving the user’s experience of a product and competing in today’s saturated consumer goods markets. However, consumer perception of form is constantly changing, and this manifests itself in the evolving forms of the products that they adopt. From clothes to cameras to cars, change in form is inevitable, and design teams must account for these trends in their product design and development strategies. Through literature, semi-structured interviews with design and trend practitioners, and an archival case study of mobile phone evolution, the authors have developed theories about the continuities that occur in product forms over time, and the forces that can disrupt this behaviour. They then go on to suggest how this view of form as evolving trajectories can benefit future product design strategies.

iPattern

2017 ◽  
Vol 6 (2) ◽  
pp. 12-27
Author(s):  
Youmna Bassiouny ◽  
Rimon Elias ◽  
Philipp Paulsen
Keyword(s):  
Design Pattern ◽  
Design Patterns ◽  
Three Dimensional ◽  
Computational Design ◽  
Pattern Design ◽  
Design Problems ◽  
Innovative Design ◽  
Rule Based ◽  
Science And Art ◽  
Design Software

Computational design takes a computer science view of design, applying both the science and art of computational approaches and methodologies to design problems. This article proposes to convert design methodologies studied by designers into rule-based computational design software and help them by providing suggestions for designs to build upon given a set of primitive shapes and geometrical rules. iPattern is a pattern-making software dedicated to designers to generate innovative design patterns that can be used in a decorative manner. They may be applied on wallpapers, carpets, fabric textiles, three-dimensional lanterns, tableware, etc. The purpose is to create a modern pattern design collection that adds a new essence to the place. In order to generate creative design patterns, primitive shapes and geometrical rules are used. The generated design pattern is constructed based on the grid of the Flower of Life of the sacred geometry or similar grids constructed using primitive shapes (rectangles, squares and triangles) combined in the layout of the Flower of Life.

scholarly journals Design Method to Structure Orthosis Design: Camptocormia Postural Brace Case Study

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Ricardo Duarte ◽  
Jean-Pierre Nadeau ◽  
Antonio Ramos ◽  
Michel Mesnard
Keyword(s):  
Design Process ◽  
Research Method ◽  
Development Process ◽  
Design Method ◽  
Upright Posture ◽  
Design Strategies ◽  
Short Term ◽  
Design Problems ◽  
Class 1

The orthosis is considered a class 1 medical device which often originates from a nonstructured development process. As these devices are mainly developed by small- and medium-sized enterprises, with no standard research method, the result can be an unadapted device which may not respond to the user’s needs and which in the short term may be abandoned. One way to solve this problem is to define and apply standard rules and procedures throughout the development/design process. Although methodologies may solve the “empiricism” in orthosis design problems, these design strategies are not applied during orthosis development due to the particularities of this field and the difficulties in linking the required knowledge and the actors that may be present during the orthosis development. The objective of this work is to develop a methodology to structure the orthosis design process that takes into account both the device life cycle and the different stakeholders involved in the design process. A case study was used to validate the proposed methodology. It was applied to the development of an orthosis to treat a specific postural disorder called camptocormia, also known as bent spine syndrome. This disorder is characterized by the anteroflexion of the trunk and especially affects elderly people. Contrary to scoliosis, the characteristics of camptocormia are not permanent, which means that the patient is able to straighten his posture. A postural brace is used to treat this disorder which enables the patient to redress and maintain the correct upright posture of the trunk.

On combinatorial design spaces for the configuration design of product families

2001 ◽  
Vol 15 (2) ◽  
pp. 91-108 ◽  
Author(s):  
ZAHED SIDDIQUE ◽  
DAVID W. ROSEN
Keyword(s):  
Engineering Design ◽  
Design Space ◽  
Optimization Problems ◽  
Product Family ◽  
Product Variety ◽  
Combinatorial Design ◽  
Configuration Design ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Space Modeling

For typical optimization problems, the design space of interest is well defined: It is a subset of Rn, where n is the number of (continuous) variables. Constraints are often introduced to eliminate infeasible regions of this space from consideration. Many engineering design problems can be formulated as search in such a design space. For configuration design problems, however, the design space is much more difficult to define precisely, particularly when constraints are present. Configuration design spaces are discrete and combinatorial in nature, but not necessarily purely combinatorial, as certain combinations represent infeasible designs. One of our primary design objectives is to drastically reduce the effort to explore large combinatorial design spaces. We believe it is imperative to develop methods for mathematically defining design spaces for configuration design. The purpose of this paper is to outline our approach to defining configuration design spaces for engineering design, with an emphasis on the mathematics of the spaces and their combinations into larger spaces that more completely capture design requirements. Specifically, we introduce design spaces that model physical connectivity, functionality, and assemblability considerations for a representative product family, a class of coffeemakers. Then, we show how these spaces can be combined into a “common” product variety design space. We demonstrate how constraints can be defined and applied to these spaces so that feasible design regions can be directly modeled. Additionally, we explore the topological and combinatorial properties of these spaces. The application of this design space modeling methodology is illustrated using the coffeemaker product family.

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