The Birth of Symbols in Design

Mukerjee, Amitabha; Dabbeeru, Madan Mohan

doi:10.1115/detc2009-86709

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…All points are represented as a linear combination of their nearest neighbors. The approach has been used in Mukerjee & Dabbeeru (2009) to identify manifolds embedded in high-dimensional decision spaces and deduce the number of intrinsic dimensions. 8.…”

Section: The Simplest Dimensionality Reduction Technique Is Principalmentioning

confidence: 99%

Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

Bandaru

Deb

2017

Expert Systems with Applications

151

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Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems.

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Section: The Simplest Dimensionality Reduction Technique Is Principalmentioning

confidence: 99%

Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

Bandaru

Deb

2017

Expert Systems with Applications

151

View full text Add to dashboard Cite

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“…In earlier work, we have explored the emergence of image schemas in a baby designer through computational simulations [23]. Here we focus on learning the labels for a schema, so the resulting label-schema pair becomes a true symbol.…”

Section: Learning Symbolsmentioning

confidence: 99%

“…Discovering these interdependences is a first step towards the process of creating semantically rich models of design. While the example here deals with only linear subspaces, we have elsewhere dealt with nonlinear manifold discovery [23].…”

Section: Learning About Clearancementioning

confidence: 99%

Learning Concepts and Language for a Baby Designer

Dabbeeru

Mukerjee

2011

Design Computing and Cognition ’10

Self Cite

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“…[A color version of this figure can be viewed online at journals.cambridge.org/aie] space; in such situations they may readily yield lower dimensional manifolds, an example of which is presented for a simple linear chunk. In other work, we have explored nonlinear dimensionality reduction, especially as they may relate to multiple-objective optimization of designs (Mukerjee & Dabbeeru, 2009). The existence of such lower dimensional manifolds indicates the existence of tight interrelations between the design variables that must hold in order to meet some functional requirement.…”

Section: Conclusion: Scalability and Future Workmentioning

confidence: 99%

Discovering implicit constraints in design

Dabbeeru

Mukerjee

2011

AIEDAM

Self Cite

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Abstract. In familiar design domains, expert designers are able to quickly focus on "good designs", based on constraints they have learned while exploring the design space. This ability to learn novel constraints is a key aspect in which design differs from traditional optimization; the constraints on the search are constantly re-defined based on the search experience itself. Moreover, such constraints are often implicit, i.e. the designer may find it difficult to articulate these constraints and provide reasons for them. Here, we ask if computer-aided-design systems can discover such implicit constraints in well-understood design situations, where the function can be articulated clearly enough to be quantified in terms of performance metrics. By considering function across a large number of design instances, patterns of functional feasibility may be learned as a byproduct of evaluating different designs. We show how patterns of functional infeasibility result in novel constraints that rule out certain regions of the design space. We demonstrate this process using examples from the design of simple locking mechanisms, and as in human experience, we show that the nature of the constraints learned depends on the extent of exposure in the design space, and may be widely variable in early stages. We also show how the process of design change, when the design space is modified, e.g. by adding new design variables, can build on patterns learned on past designs. In conclusion, we discuss the ramifications of this process on chunking and representational change, and also on design creativity.

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The Birth of Symbols in Design

Cited by 4 publications

References 26 publications

Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

Learning Concepts and Language for a Baby Designer

Discovering implicit constraints in design

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