scholarly journals Capturing Human Sequence-Learning Abilities in Configuration Design Tasks Through Markov Chains

2017 ◽  
Vol 139 (9) ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Markov Chains ◽  
Sequence Learning ◽  
Configuration Design ◽  
Modeling Framework ◽  
Solution Quality ◽  
Design Problems ◽  
Learning Abilities ◽  
First Order ◽  
Human Sequence ◽  
Current Design

Designers often search for new solutions by iteratively adapting a current design. By engaging in this search, designers not only improve solution quality but also begin to learn what operational patterns might improve the solution in future iterations. Previous work in psychology has demonstrated that humans can fluently and adeptly learn short operational sequences that aid problem-solving. This paper explores how designers learn and employ sequences within the realm of engineering design. Specifically, this work analyzes behavioral patterns in two human studies in which participants solved configuration design problems. Behavioral data from the two studies are first analyzed using Markov chains to determine how much representation complexity is necessary to quantify the sequential patterns that designers employ during solving. It is discovered that first-order Markov chains are capable of accurately representing designers' sequences. Next, the ability to learn first-order sequences is implemented in an agent-based modeling framework to assess the performance implications of sequence-learning abilities. These computational studies confirm the assumption that the ability to learn sequences is beneficial to designers.

scholarly journals Capturing Human Sequence-Learning Abilities in Configuration Design Tasks through Markov Chains

2018 ◽  
Author(s):  
Christopher McComb ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Markov Chains ◽  
Sequence Learning ◽  
Configuration Design ◽  
Modeling Framework ◽  
Solution Quality ◽  
Design Problems ◽  
Learning Abilities ◽  
First Order ◽  
Human Sequence ◽  
Current Design

Designers often search for new solutions by iteratively adapting a current design. By engaging in this search, designers not only improve solution quality but also begin to learn what operational patterns might improve the solution in future iterations. Previous work in psychology has demonstrated that humans can fluently and adeptly learn short operational sequences that aid problem-solving. This paper explores how designers learn and employ sequences within the realm of engineering design. Specifically, this work analyzes behavioral patterns in two human studies in which participants solved configuration design problems. Behavioral data from the two studies are first analyzed using Markov chains to determine how much representation complexity is necessary to quantify the sequential patterns that designers employ during solving. It is discovered that first-order Markov chains are capable of accurately representing designers' sequences. Next, the ability to learn first-order sequences is implemented in an agent-based modeling framework to assess the performance implications of sequence-learning abilities. These computational studies confirm the assumption that the ability to learn sequences is beneficial to designers.

scholarly journals Designing the Characteristics of Design Teams via Cognitively Inspired Computational Modeling

2018 ◽  
Author(s):  
Christopher McComb
Keyword(s):  
Simulated Annealing ◽  
Problem Solving ◽  
Optimization Algorithm ◽  
Engineering Students ◽  
Design Teams ◽  
Modeling Framework ◽  
Solution Quality ◽  
Design Problems ◽  
Holistic Understanding ◽  
Team Characteristics

Teams are a ubiquitous part of the design process and a great deal of time and effort is devoted to managing them effectively. Although teams have the potential to search effectively for solutions, they are also prone to a number of pitfalls. Thus, a greater understanding of teams is necessary to ensure that they can function optimally across a variety of tasks. Teams are typically studied through controlled laboratory experiments or through longitudinal studies that observe teams in situ. However, both of these study types can be costly and time-consuming. Months, if not years, pass between the initial conception of a study and the final analysis of results. This work creates a computational framework that efficiently emulates human design teams, thus facilitating the derivation of a theory linking the properties of design problems to optimized team characteristics, effectively making it possible to design design teams.This dissertation first introduces and validates the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework. The central structure of CISAT is modeled after simulated annealing, a global optimization algorithm that has been shown to effectively mimic the problem-solving process of individuals. Specifically, a multi-agent analog of simulated annealing is used in CISAT to mimic the behavior of teams. Several additional components, drawn from the psychology and problem-solving literature, are then included in the framework to enable a more accurate description of individual activity and interaction within the team. CISAT is then used to investigate the relationship between design problem properties, team characteristics, and task performance. Multiple computational simulations are conducted in which simulated teams with various characteristics solve a variety of different configuration problems. These simulations are then post-processed to produce a set of equations that make it possible to predict optimal team characteristics based on problem properties, thus enabling the optimal design of design teams. To validate these equations a behavioral study is designed and conducted in which teams of engineering students interact at different frequencies while designing a complex system. Results of the study offer a limited validation of the predictive equations.This dissertation further highlights the resource efficiency and versatility of CISAT by demonstrating its use in two additional applications. In the first, a new numerical optimization algorithm is derived directly from CISAT by stripping away all but the most quintessential team-based characteristics. The team-based characteristics of this algorithm allow it to achieve high performance across a variety of objective function with diverse topographies. In the second application, CISAT is used in conjunction with Markov concepts to examine the order in which designers make changes to their solutions. Although it has been demonstrated that humans apply changes in a specific order (called a sequence) when solving puzzles, such patterns have not been examined for engineers solving design problems. It is shown that operation sequences are used by designers, and improve solution quality. This dissertation demonstrates how characteristics of individual designers and design teams can be captured and accurately reproduced within a computational model to advance our knowledge of design methodology. Future extensions of this work have the potential to inform a deeper and more holistic understanding of the search process.

scholarly journals Figures of merit for stellarators near the magnetic axis

2021 ◽  
Vol 87 (1) ◽  
Author(s):  
Matt Landreman
Keyword(s):  
Magnetic Field ◽  
Field Strength ◽  
Magnetic Axis ◽  
Configuration Design ◽  
New Paradigm ◽  
First Order ◽  
Figures Of Merit ◽  
Minor Radius ◽  
Error Field ◽  
Quantities Of Interest

A new paradigm for rapid stellarator configuration design has been recently demonstrated, in which the shapes of quasisymmetric or omnigenous flux surfaces are computed directly using an expansion in small distance from the magnetic axis. To further develop this approach, here we derive several other quantities of interest that can be rapidly computed from this near-axis expansion. First, the $\boldsymbol {\nabla }\boldsymbol {B}$ and $\boldsymbol {\nabla }\boldsymbol {\nabla }\boldsymbol {B}$ tensors are computed, which can be used for direct derivative-based optimization of electromagnetic coil shapes to achieve the desired magnetic configuration. Moreover, if the norm of these tensors is large compared with the field strength for a given magnetic field, the field must have a short length scale, suggesting it may be hard to produce with coils that are suitably far away. Second, we evaluate the minor radius at which the flux surface shapes would become singular, providing a lower bound on the achievable aspect ratio. This bound is also shown to be related to an equilibrium beta limit. Finally, for configurations that are constructed to achieve a desired magnetic field strength to first order in the expansion, we compute the error field that arises due to second-order terms.

scholarly journals Determination of the Order of a Markov Chain for Daily Rainfall Data of North East India: "Application AIC criterion"

2018 ◽  
Vol 10 (1) ◽  
pp. 80-87
Author(s):  
Surobhi Deka
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Akaike Information Criterion ◽  
Markov Chain Model ◽  
Information Criterion ◽  
Chain Model ◽  
Adequate Model ◽  
First Order ◽  
North East India ◽  
North East

The paper aims at demonstrating the application of the Akaike information criterion to determine the order of two state Markov chain for studying the pattern of occurrence of wet and dry days during the rainy season (April to September) in North-East India. For each station, each day is classified as dry day if the amount of rainfall is less than 3 mm and wet day if the amount of rainfall is greater than or equal to 3 mm. We apply Markov chain of order up to three to the sequences of wet and dry days observed at seven distantly located stations in North East region of India. The Markov chain model of appropriate order for analyzing wet and dry days is determined. This is done using the Akaike Information Criterion (AIC) by checking the minimum of AIC estimate. Markov chain of order one is found to be superior to the majority of the stations in comparison to the other order Markov chains. More precisely, first order Markov chain model is an adequate model for the stations North Bank, Tocklai, Silcoorie, Mohanbari and Guwahati. Further, it is observed that second order and third order Markov chains are competing with first order in the stations Cherrapunji and Imphal, respectively. A fore-knowledge of rainfall pattern is of immense help not only to farmers, but also to the authorities concerned with planning of irrigation schemes. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season.

Modality and representation in analogy

2008 ◽  
Vol 22 (2) ◽  
pp. 85-100 ◽  
Author(s):  
J.S. Linsey ◽  
K.L. Wood ◽  
A.B. Markman
Keyword(s):  
Prior Knowledge ◽  
English Language ◽  
Functional Model ◽  
Cognitive Mechanisms ◽  
Support Design ◽  
Design Problems ◽  
Functional Models ◽  
Current Design ◽  
Design By Analogy ◽  
General Semantic

AbstractDesign by analogy is a powerful part of the design process across the wide variety of modalities used by designers such as linguistic descriptions, sketches, and diagrams. We need tools to support people's ability to find and use analogies. A deeper understanding of the cognitive mechanisms underlying design and analogy is a crucial step in developing these tools. This paper presents an experiment that explores the effects of representation within the modality of sketching, the effects of functional models, and the retrieval and use of analogies. We find that the level of abstraction for the representation of prior knowledge and the representation of a current design problem both affect people's ability to retrieve and use analogous solutions. A general semantic description in memory facilitates retrieval of that prior knowledge. The ability to find and use an analogy is also facilitated by having an appropriate functional model of the problem. These studies result in a number of important implications for the development of tools to support design by analogy. Foremost among these implications is the ability to provide multiple representations of design problems by which designers may reason across, where the verb construct in the English language is a preferred mode for these representations.

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.

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.

Implicit Spoken Words and Motor Sequences Learning Are Impaired in Children with Specific Language Impairment

2016 ◽  
Vol 22 (5) ◽  
pp. 520-529 ◽  
Author(s):  
Lise Desmottes ◽  
Thierry Meulemans ◽  
Christelle Maillart
Keyword(s):  
Sequence Learning ◽  
Language Impairment ◽  
Specific Language Impairment ◽  
Implicit Sequence Learning ◽  
Learning Abilities ◽  
Serial Search ◽  
Specific Language ◽  
Spoken Words ◽  
Procedural Deficit Hypothesis ◽  
Simultaneous Assessment

AbstractObjectives: This study aims to compare verbal and motor implicit sequence learning abilities in children with and without specific language impairment (SLI). Methods: Forty-eight children (24 control and 24 SLI) were administered the Serial Search Task (SST), which enables the simultaneous assessment of implicit spoken words and visuomotor sequences learning. Results: Results showed that control children implicitly learned both the spoken words as well as the motor sequences. In contrast, children with SLI showed deficits in both types of learning. Moreover, correlational analyses revealed that SST performance was linked with grammatical abilities in control children but with lexical abilities in children with SLI. Conclusions: Overall, this pattern of results supports the procedural deficit hypothesis and suggests that domain general implicit sequence learning is impaired in SLI. (JINS, 2016, 22, 1–10)

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