Guiding Agent Learning in Design

Grecu, Dan; Brown, David C.

doi:10.1007/978-0-387-35582-5_13

Cited by 2 publications

(2 citation statements)

References 11 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Where expectations exist (Grecu & Brown, 1999), or where expectation failures have occurred (Brown, 2012;Brown et al, 1992), there may be some appropriate knowledge. In other cases, heuristic relationships may exist.…”

Section: Consequences Of the Cpammentioning

confidence: 98%

Developing computational design creativity systems

Brown

2013

International Journal of Design Creativity and Innovation

View full text Add to dashboard Cite

The underlying hypothesis of this paper is that the rigor needed to build computational design creativity systems will produce insights that will increase our understanding of creativity. The paper points to a variety of factors that will need to be studied and understood in detail in order to build such systems. It examines selected technical literature about creativity to determine what key characteristics have been proposed as underlying creative production. In addition, as the existence of creativity is a judgment relative to personal or group norms, it is helpful to understand what factors inform that judgment. Besemer's creative product semantic scale for product assessment provides three dimensions, with nine attributes that can be used for judgments. This paper discusses how creative product assessment scales might provide important clues about how to build computational systems in order to produce creative products.

show abstract

Section: Consequences Of the Cpammentioning

confidence: 98%

Developing computational design creativity systems

Brown

2013

International Journal of Design Creativity and Innovation

View full text Add to dashboard Cite

show abstract

“…Finally, multiagent systems have been used to model the engineering design process (Jin and Levitt, 1993; Jin and Levitt, 1996; Reza Danesh and Jin, 2001) and support engineering design (Jin and Lu, 1998; Jin and Lu, 2004). While these systems typically use rule-based agent architectures and not multiagent learning, learning has been proposed as a possible way for agents to discover how their internal conditions affect external conditions (Grecu and Brown, 2000).…”

Section: Introductionmentioning

confidence: 99%

Modeling multidisciplinary design with multiagent learning

Hulse

Tumer

Hoyle

et al. 2018

AIEDAM

View full text Add to dashboard Cite

Complex engineered systems design is a collaborative activity. To design a system, experts from the relevant disciplines must work together to create the best overall system from their individual components. This situation is analogous to a multiagent system in which agents solve individual parts of a larger problem in a coordinated way. Current multiagent models of design teams, however, do not capture this distributed aspect of design teams – instead either representing designers as agents which control all variables, measuring organizational outcomes instead of design outcomes, or representing different aspects of distributed design, such as negotiation. This paper presents a new model 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. These designers are represented as a multiagent learning system which is shown to perform similarly to a centralized optimization algorithm on the same domain. When used as a model, this multiagent system is shown to perform better when the level of designer exploration is not decayed but is instead controlled based on the increase of design knowledge, suggesting that designers in multidisciplinary teams should not simply reduce the scope of design exploration over time, but should adapt based on changes in their collective knowledge of the design space. This multiagent system is further shown to produce better-performing designs when computational designers design collaboratively as opposed to independently, confirming the importance of collaboration in complex systems design.

show abstract

Guiding Agent Learning in Design

Abstract: In this paper we discuss the need for learning in multi-agent design systems, and the variety of forms it might take. We propose a particular method of guiding learning in these systems, describe an architecture for its implementation, and discuss how the learning should be evaluated.

Cited by 2 publications

References 11 publications

Developing computational design creativity systems

Developing computational design creativity systems

Modeling multidisciplinary design with multiagent learning

Contact Info

Product

Resources

About