Special Issue Part 1 (Issue 3) and Part 2 (Issue 4) of AIEDAM
are based on a workshop on Learning and Creativity held at the 2002
conference on Artificial Intelligence in Design, AID '02
(www.cad.strath.ac.uk/AID02_workshop/Workshop_webpage.html; Gero, 2002), the sixth of similar workshops, with the
previous five focusing on Machine Learning in Design and being held at
AIDs '92, '94, '96, '98, and '00 (Gero, 1992, 2000; Gero &
Sudweeks, 1994, 1996,
1998). The first three workshops also resulted
in special issues of AIEDAM (Maher et al.,
1994; Duffy et al., 1996, 1998).
The purpose of the workshop was to explore the subject of learning and
creativity. The objective of the workshop was to bring the ideas,
experiences, and latest research together and to produce an insightful
understanding of the subject of learning and creativity.
The workshop itself focused upon two main topics: the nature of
learning and creativity, and learning and creativity in
computational-based team design. With respect to the first topic,
agreement was reached on defining aspects of creativity. Creativity was
considered a judgement, and to involve a product, a process, and a
situation. Whether a relationship exists between learning and creativity
remained undecided. With respect to the second topic, agreement was
reached that an agent-based, mutually learning design system can be
developed. Agents (both human and software) can learn from each other.
This requires communication, common goals and understanding (e.g.,
ontologies), and knowledge.
The basic ethos of the workshop was the stimulus for this special
issue. A general call was extended to the AIEDAM community and
workshop participants. The position taken and questions raised for the
call was that learning and creativity in design are two related
activities, but can the interrelations between the activities be defined?
What is the nature of these interrelationships? Does creativity
necessarily result in new knowledge and/or learning? Can creativity be
supported by computational means? Can learning be supported by
computational means? If so, can such computational systems support design
practice? Can automated design be considered to be creative? Can learning
from past design manipulations be considered to be creative? Authors were
invited to submit papers discussing their work in relation to these
questions and address:
- the links between learning and creativity in design;
- the nature of creativity and learning;
- creativity and learning in team design; and
- techniques, knowledge, and approaches to computationally supported
creativity and learning.
The result is a mixture of papers presented in Issues 3 and 4 as Parts
1 and 2, respectively. In Part 1, the first three papers concentrate on
creativity and the fourth on learning and creativity. In Part 2, the last
three concentrate on learning in design. The first paper by van Langen,
Wijngaards, and Brazier focuses on the nature of creativity and artificial
systems. The second paper by Weas and Campbell discusses a particular
method to support creativity. Koza et al. present an application of a
creative technique in the third paper. The link between learning and
creativity is discussed by Sim and Duffy in the fourth and last paper in
Part 1. Three studies of learning and design are presented in Part 2. Wu
and Duffy study the nature and interrelationships between learning and
design in teams. By contrast, Nath and Gero focus on reusing experiences
of design processes and Alisantoso, Khoo, and Lee synthesize past design
concepts.
Van Langen et al. address the basic question of whether artificial
systems can be creative and what the requirements are for such creative
systems. They give an overview of a number of papers on creativity before
giving their definition of a creative system. They define a
result as creative if an assessor deems the result as being new,
unexpected, and valuable. They define a creative system as one
that produces creative results sufficiently often. They go on to state
that for a system to be creative it must be able to interact with its
environment, learn, and self-organize. A number of pieces of research work
are then discussed in relation to those requirements. The paper closes by
highlighting areas requiring future research.
The analysis of interconnected decision areas (AIDA) method originated
in the mid-1960s but has never been implemented until very recently,
according to Weas and Campbell. Their paper introduces the first known
implementation of the AIDA process in software for helping decision makers
to hypothesize and develop creative solution concepts. The focus of their
implementation is to combine the solution principles of various
subfunctions within a product in new ways. The method itself does not
automatically create solutions, but rather works interactively with the
user to identify new potential solutions. The method is described, and its
implementation and use in engineering design is discussed. It allows
designers to examine the decision space and relate interdependencies among
the various choices available. The example of designing a coffee grinder
using the method is presented, and the experiences of introducing it into
a mechanical engineering design methodology course discussed. Thirty-five
percent of the students found that as a technique, AIDA resulted in new
and different design solutions. With the exception of patent searches,
other more effective techniques were found to be guided brainstorming
methods.
The negative feedback amplifier is regarded as one of the most
outstanding inventions in the control arena. Koza et al. show how negative
feedback of electrical circuits can be replicated by the automated design
and invention technique of genetic programming. Genetic programming uses
the Darwinian principle of natural selection coupled with evolutionary
algorithms such as crossover and mutation to create ever improving
populations of programs. That is, randomly generated computer programs are
generated through transformations or combinations of different available
programmatic ingredients. Selection for evolution is based upon a fitness
function of the new program's execution. The paper describes the
approach and lists previously patented inventions that have been
reinvented by genetic programming. Examples and comparisons of
the reinvention of patented inventions are presented and discussed, and a
new patentable general-purpose controller described. The paper outlines
the implications of the approach with increasing computer power and
methods for extending the approach to more complex circuits.
Knowledge transformers are discussed by Sim and Duffy as a link
between learning and creativity. A knowledge transformer is taken as an
operator that derives a piece of new knowledge from a given input or an
existing piece of knowledge. Seven pairs of knowledge transformers were
specified to characterize the learning process: abstracting/detailing,
association/disassociate, derivations/randomization,
explanation/discovery, rationalization/decomposition,
generalization/specialization, and similarity
comparison/dissimilarity comparison. These transformers are considered
to be linked to the thought process in creativity, and a few examples of
where the transformers can be used to explain discoveries in science and
invention in technology are given. Discussion of whether cognitive
processes exist that are similar to the knowledge transformers, in the
formulation of models and theories of creativity, focuses on bisociation
in idea generation, the Darwinian model of evolution, the geneplore model
of generative and explorative processes, and Li's theory of
conceptual intelligence. The paper concludes that the creative process can
be explained through knowledge transformers, and that they may provide the
basis for linking learning with creativity.
Modeling collective learning in design is the focus of Wu and
Duffy's paper. Collective learning is understood to be how a group of
agents interact and learn from each other. Although considerable work
exists in organizational, team, and individual learning, the authors
suggest that there is relatively little known about the nature of learning
between individuals in design. The authors give an overview of the
relevant work in the area before putting forward a number of hypotheses
regarding the nature of collective learning and design. The hypotheses
address aspects such as the inputs and outputs of a team design activity;
the existence, elements, and forms of collective learning; collective
memory being interconnected to learning; and the links between team design
and collective learning. Protocol analysis experiments are reported, to
check the hypotheses, and a model of collective learning in design is
presented.
Nath and Gero present a computational system that can learn search
strategy rules from its own experience of designing. The design activity
is initially taken as an uniformed search during which heuristics are
learned to select or reject design decisions. Where appropriate, these
heuristics are then used during subsequent searches, or new heuristics are
learned to change the computational design generator from a search-based
process to a knowledge-based one. The coupling between computational
design and learning is discussed and the learning mechanism, built on the
SOAR system, explained by an example in architectural layout generation.
The strengths of the developed method are reported such as: learning is a
dynamic activity driven by the requirements of the design process in
situ rather than a passive retrospective activity; a unified
representation space allows seamless transformation between learning and
designing; situated heuristics support reasoning about regions of the
search space rather than specific points; inappropriate solutions can be
eliminated, enabling rapid exploration; and costly knowledge elicitation
can be eliminated. Some weaknesses of the method are presented, as some
heuristics are likely to be too general because of its inability to
account for the absence of instantiated patterns, and that all new
knowledge is within the deductive boundary of the knowledge that was
encoded a priori.
The focus of the paper by Alisantoso et al. is upon extracting and
using past design knowledge for a so-called concept–capability
mapping, to assist designers in estimating the capability of a design to
meet specified requirements, and synthesizing past design concepts. Two
algorithms are presented within the approach: the dissimilar
objects algorithm and the attribute decomposition algorithm.
A case study on the design of vacuum cleaners is used to illustrate the
approach. They conclude that the design rules extracted from past designs
were reasonable and effective in estimating the design, and that the
performance is likely to improve if more past design information was made
available.
The Guest Editors are truly grateful for the efforts of all the
authors in this Special Issue. We particularly thank the reviewers of the
workshop and journal papers. They helped to make this Special Issue a
reality. We feel that the articles in this Special Issue make a
significant contribution to the development of learning and creativity and
hope the readers find them as interesting and beneficial as we did.
