A METHOD FOR CREATIVE BEHAVIORAL DESIGN BASED ON ANALOGY AND BLENDING FROM NATURAL THINGS

Proceedings of the ASME 2008 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference

IDETC/CIE 2008 August 3-6, 2008, Brooklyn, New York, USA

DETC2008-49389

A METHOD FOR CREATIVE BEHAVIORAL DESIGN BASED ON ANALOGY AND BLENDING FROM NATURAL THINGS

Kazuya Tsujimoto Department of Mechanical Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe

6578501, Japan

Shinji Miura Graduate School of Science and Technology,

Kobe University1-1 Rokkodai-cho, Nada-ku, Kobe 6578501, Japan

Akira Tsumaya Department of Mechanical Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe

6578501, Japan

Yukari Nagai School of Knowledge Science, Japan Advanced

Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 9231292, Japan

Amaresh Chakrabarti Toshiharu Taura Centre for Product Design and Manufacturing, Indian Institute of Science, Bangalore 560012,

India

Department of Mechanical Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe

6578501, Japan
ABSTRACT In the near future, robots and CG (computer graphics) will be required to exhibit creative behaviors that reflect designers’ abstract images and emotions. However, there are no effective methods to develop abstract images and emotions and support designers in designing creative behaviors that reflect their images and emotions. Analogy and blending are two methods known to be very effective for designing creative behaviors. The aim of this study is to propose a method for developing designers’ abstract behavioral images and emotions and giving shape to them by constructing a computer system that supports a designer in the creation of the desired behavior. This method focuses on deriving inspiration from the behavioral aspects of natural phenomena rather than simply mimicking it. We have proposed two new methods for developing abstract behavioral images and emotions by which a designer can use analogies from natural things such as animals and plants even when there is a difference in the number of joints between the natural object and the design target. The first method uses visual behavioral images, the second uses rhythmic behavioral images. We have demonstrated examples of designed behaviors to verify the effectiveness of the proposed methods.
1 INTRODUCTION In the near future, robots and CG (computer graphics) will be an intrinsic part of human life. They will especially play an important role in entertainment and art [1]. Hence, they will be required to exhibit creative behaviors that reflect designers’ abstract images and emotions. Furthermore, it is expected that inexperienced laymen as well as skilled workers will be able

to design creative behaviors, which will result in novel products being introduced in some fields such as entertainment and art. For example, you may desire to make the ornaments in your room behave as you like and change their behaviors every day. It is necessary for humanoid robots and CG characters to reflect your abstract images and emotions in their behaviors, so the behavioral pattern that you can feel closer to them. In order to design the behavioral pattern of an artificial being, it is necessary to give a rough shape to abstract images and emotions. This process involves generating ideas that reflect the design concept [2]. Following this, the designer should implement the design concretely and optimize it. For example, when designing the behavior of a robot arm, a designer first generates ideas or rough images of the type of movements such as powerful, smooth, zigzag, etc. Following this, the necessary calculations and drafting are done. The design of robotic behavior has been researched extensively. However, most of these researches include industrial robots that are able to perform only limited tasks as their behaviors are programmed depending on the programmer’s personal experience. Furthermore, it is difficult to design complex behaviors using conventional methods. On the other hand, researches at the forefront of design engineering have recognized that analogy and blending are very effective in designing creative ideas [3]. Keeping this in mind, Min An proposed an effective method of blending behavioral criteria and then designing creative behaviors [4] (Fig. 1). By using this method, a designer can select his favorite behaviors and blend the criteria by which the

1 Copyright © 2008 by ASME

behaviors can be realized using the optimization method so that the designer will reflect his tastes. However, the initial behaviors were produced randomly, and hence the designed behaviors were incongruous with the designer’s images and emotions.

Figure 1. The system proposed by Min An. One of the methods used to design appropriate initial behaviors is to use the motion capture data of natural objects such as animals and plants. Nowadays image processing technology has made remarkable progress [5]; hence, we can easily extract motion data from movies of animals and plants. However, there still exist differences in structure between the design targets and the natural objects, such as the number of their joints. Although we have developed a method to imitate the behavior of a model robot by assuming suitable behavior criteria when there is a difference in the number of joints between an imitating robot and a model robot [6], it is still difficult to reproduce the entire behavioral pattern. In conclusion, there are no effective methods to develop abstract behavioral images and emotions and support designers in designing creative behaviors that reflect their images and emotions regardless of the number of joints. The aim of this study is to propose a method of developing abstract behavioral images and emotions and giving them a rough shape by developing an effective computer system that supports a designer in the creation of the behaviors to be designed. This method focuses on deriving inspiration from the behavioral aspects of natural phenomena rather than simply mimicking it. In order to give a rough shape to abstract images and emotions, we use analogy and blending. In this study, we have assumed that the design target has a linked pole structure. We make this assumption based on the following two points: first, it is possible to represent all natural objects that have interesting motions as skeletal structures using CG. Second, this linked-pole model is the most fundamental model and can be extended to other models such as a meatball, a polygon, etc. In this study, we propose two new methods for developing abstract behavioral images and emotions by which a designer can use analogies from natural objects such as animals and plants even in the case where there is a difference in the number of joints between the natural object and the design target. The first method involves the use of visual behavioral images. The second one uses rhythmic behavioral images. This paper is organized as follows. Section 2 provides an overview of analogy and blending. Then, we point out

Obtaining behavioral criteria randomly produced by GP (genetic

programming)

Producing behavioral patterns by GA (genetic algorithm)

The designer wo desired patterns

selects any t

Combining behavioral criteria selected by the designer by GP

Obtaining the criterion that produces the desired behavior

problems in using them in the design of creative behaviors. Section 3 introduces Hermite interpolation. Section 4 proposes a method to extract behavioral images independent of the number of joints and discusses a way to develop visual behavioral images. In section 5, wavelet analysis and the genetic algorithm are introduced, and we propose a method to extract rhythmic behavioral images and learn them. In section 6, examples of behaviors designed by these new methods are demonstrated. 2 ADAPTATION OF THE CREATIVE DESIGN PROCESS Many studies have been conducted to analyze the characteristics of the design thought process from the viewpoint of creativity. As a result, it has been found that analogical thinking and concept-synthesizing processes such as blending are keys to creative thinking [3]. 2.1 Analogical Reasoning and Metaphor: Analogical reasoning and metaphor are known to play very important roles in creative design (Gero and Maher 1991; Goldschmidt 2001). For example, the “Swan chair,” a famous example of design, was imaged using analogy [3]. The form of the chair resembles a swan: message that this chair is soft and elegant like a swan to users. Analogical reasoning and metaphor are methods of concept creation that involve developing a new concept from an existing concept. In practice, they are frequently used in the design process. In this thought process, the design result and the base object share some common features such as shape and function. 2.2 Concept Blending: The main roles of analogical reasoning and metaphor are to understand or to transfer known concepts, that is, it is analytic rather than synthetic since its primitive process is the extraction of some features from known concepts by analyzing it [3]. From the viewpoint of mental cognition in the domain of cognitive science, Finke et al. (1992) described conceptual synthesis as an efficient means of developing creative insights into new inventions and carried out experiments on creation that involved mental products generated by imagery synthesis [7]. It has been pointed out that the development of creative thinking that is related to the transforming of concepts [8] (Boden 2004) or conceptual spaces [9] (Gardenfors 2000) is necessary to support human creativity. On the other hand, in studies on cognitive linguistics, Fauconnier (1994) focused on the construction process of meaning in ordinary discourse. He analyzed how conceptual integration creates mental products and how to deploy systems of mapping and blending between mental spaces [10]. From the viewpoint of mental space theory, he showed that conceptual integration operates on two input mental spaces to yield a third space that is called “the blend.” The blended space inherits partial structures from the input spaces and has emergent structures of its own. Both mental products, imagery and discourse, have shown emergent features and have stimulated creativity. We can think of concept blending as a process in which two basic concepts are blended at an abstract level and a new concept that inherits some abstract features of the two base concepts but concrete features of neither is generated. For example, if one were to design something by combining the concepts of a musical instrument and a dress, the design result could be a wine glass that induces melody by


blending the concept of a party and that of strings [3].

Base Object

Target Object

Abstraction

Analogy

Figure 2. Analogy and Blending.

2.3 Analogy by Nature: The importance of learning from nature has long been recognized and attempts have been made by researchers like Vogel (1988) and French (1998) to learn from nature with product development in mind [11]. Systems in the natural world and their functionality are seen as rich sources of inspiration for the generation of ideas. Hence, we use natural objects as base objects for analogy. Chakrabarti developed a computational tool for supporting designers in the generation of novel solutions for product design problems by providing natural or artificially inspired and analogical ideas; his aim was to initiate work in systematic biomimetics and to develop an integrated approach for systematic idea generation in product design using inspirations from natural and artificial systems [11]. The name of this system is IDEA-INSPIRE [Chakrabarti et al. 2004]. Designers can use the corpus of diverse motions that nature provides as a potent source for solving product design problems and as an inspiration for developing creative and innovative novel products. The database of natural systems has been developed with approximately 100 entries from the plant and the animal domains [11]. The motions analyzed are varied in both the media in which they occur. The information collected contains details about the function, behavior, and structure of these systems as perceived in the source materials [11]. The types of information collected include written, diagrammatic, pictorial, and video data about the systems and their varied motions [11]. The SAPPhIRE model [11] is used to represent the entries in the databases, and reasoning procedures are developed to help browse and search for the entries that are analogically relevant for solving a design problem.

Object1

Object2

New Object

Blending

In this study, we assume that designers can extract motion data from video data by using the latest image processing techniques. First, they use IDEA-INSPIRE to search for animals and plants that are congruous with their images and emotions. Following this, they input the extracted motion data into the methods developed in this study.

Figure 3. IDEA-INSPIRE [Chakrabarti et al. 2004]. 2.4 Visual Image and Rhythmic Image: In order to develop behavioral images and emotions and give them a rough shape, it is necessary to define them. From the viewpoint of physical phenomena, behaviors can be described using physical quantities such as acceleration and velocity. However, it is not possible to calculate physical quantities while imitating the behavior of something. The features of behavior should be more intuitive in nature. In this study, we assume that behavioral images can be divided into two groups–visual behavioral images and rhythmic behavioral images. A visual behavioral image is literally what a behavior looks like. This image can be represented using mimetic words. For example, a snake’s behavior is termed as “zigzag”; this word invokes visual images. A rhythmic behavioral image represents a way of changing quantities such as the angles of joints and velocities. This image can be represented using onomatopoeic words. For example, we usually use the word “hop” to describe a frog’s behavior; this word represents rhythmic behavioral image. However, a part of visual behavioral images and a part of rhythmic behavioral images dependent on each other. One of the aims of this study is to distinguish rhythmic behavioral images from visual behavioral images. In the next section, we describe the Hermite interpolation theory, which plays an important role in this study.

Figure 4. Relation between behavioral images.

Visual image

Rhythmic image

Behavioral image


Object 2 Frame

1−n

tΔ+

tΔ+

tΔ+

Object 1

Object 1

Object 2

Frame n

)(1.1 sXn −

)(1, sxn

)(1, syn

)(1.1 sYn −

)(2,1 sxn −

)(2,1 syn −

)(2,1 sXn −

)(2,1 sYn −

)(1, sXn

)(1, sYn

)(1,1 sxn −

)(1,1 syn −

)(2, sxn

)(2, syn

)(2, sXn

)(2, sYn

Pictures Hermite

polynomials Hermite

polynomialsShape images

(interpolated at defined points)

Blended shape images

Transferred pictures

Blended polynomials

2,122,11 −− + nn Xwxw

Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7

x

y

X

Y

y

Y

X

x

0

0

0

0

1=s

4=s3=s

2=s

2,122,11 −− + nn Ywyw

2,22,1 nn Xwxw +

2,22,1 nn Ywyw +

)1,0( 21 ≤≤ ww

)1,0( 21 ≤≤ ww

Figure 5. Abstracting visual behavioral images and blending.
3 HERMITE INTERPOLATION Hermite interpolation is a method that allows us to consider given derivatives at data points as well as the data points themselves. The interpolation is represented as a polynomial that has a degree less than or equal to the number of both data points and their derivatives minus 1 [12].
∑∑==

′+=n

k

n

kkkkk

xfxgxfxhxP00

)()()()()(

gives the unique Hermite interpolating fundamental polynomial for which [12]

)()(

)()(

ixfxP

xfxP

i

ii

′=′

=

where ),...,2 ,1 ,0( ni =

xi denotes the points. The polynomials hk(x) and gk(x) in the fundamental Hermite interpolating polynomials are defined as follows [12]:

2

2

)}(){()(

)}()}{)((21{)(

xLxxxg

xLxxxLxh

kkk

kkkkk

−=

−′−=

where ),...,2 ,1 ,0( nk =

The fundamental polynomials of Lagrange interpolation are defined by [12]

(1)

(2)

(3)

∏

∏

=

=

−=′

−

−=

n

m

n

m

mk

kk

mk

m

k

xxxL

xxxx

xL

0

0

1)(

)((4)

4 APPROACH 1–DEVELOPMENT OF VISUAL BEHAVIORAL IMAGES Since this study aims to extract and develop behavioral images regardless of the number of joints, we capture motion as a sequence of pictures and extract smooth images from each picture using Hermite interpolation. A visual behavioral image is a collection of these smooth shape images. However, this collection cannot be directly applied to analogy or blending since the number of terms in Hermite interpolation polynomials differs according to the number of sample data points. Therefore, the second set of Hermite interpolation polynomials is obtained from the first interpolated data at the defined points, so that the numbers of terms in these polynomials are equal in all the samples. The following method is used to extract visual behavioral images and transfer them to a design target (Fig. 5). Step 1) Motion data is divided to obtain a collection of sequence

frames (pictures). Step 2) Hermite interpolation polynomials are obtained in each

frame (In fig. 5, s is the parameter representing the number of points).

Step 3) Interpolation is carried out at defined points to acquire

smooth images.


Step 4) Hermite interpolation polynomials are obtained again in each frame (the number of terms in Hermite interpolation polynomials are the same).

Step 5) The Hermite interpolation polynomials are blended

(calculating weighted averages). The weights can be changed by the designers.

Step 6) Interpolation is carried out at some points corresponding

to the design target Step 7) The data of defined points is transferred to the

corresponding joints of the design target. 5 APPROACH 2–DEVELOPMENT OF RHYTHMIC BEHAVIORAL IMAGE In this step, we use wavelet analysis and the genetic algorithm to transfer rhythmic behavioral images to design targets. 5.1 Wavelet Analysis: The fundamental idea of wavelets is to analyze signals according to scale. Wavelets are functions that satisfy certain mathematical requirements and are used to represent data or other functions. In wavelet analysis, the scale that one uses for looking at data plays a special role. Wavelet algorithms process data at different scales or resolutions. If we look at a signal with a large “window”, we would only notice overall features [13]. However, if we look at a signal with a small “window,” we would notice small discontinuities [13]. Using wavelet analysis, we can observe the data in detail and analyze discontinuities in the changes. The analysis is performed using contracted high-frequency version of the prototype wavelet. The wavelet analysis procedure adopts a wavelet prototype function called an “analyzing wavelet” or “mother wavelet [13].” In this study, we use Daubechies wavelets [13] as wavelet prototype functions. Because the original signal or function can be represented in terms of a wavelet expansion (using coefficients in a linear combination of the wavelet functions), data operations can be performed using only the corresponding wavelet coefficients [13]. Moreover, if the best wavelets that can be adapted to the data are chosen or the coefficients below a threshold are truncated, the amount of data can be reduced [13]. This “sparse coding” makes wavelets an excellent tool in the field of data compression. 5.2 Genetic Algorithm: Genetic algorithms are inspired by Darwin’s theory of evolution. We have developed a solution using the genetic algorithm. In general, an algorithm starts with a set of solutions (represented by chromosomes) called a population [14]. The solutions from one population are taken and used to form a new population [14]. This is done to ensure that the new population will be better than the old one. Solutions are selected to form new solutions (offspring) based on their fitness–the more suitable they are, the greater the chance of reproduction [14]. This process is repeated until some condition (for example, the number of populations or the improvement of the best solution) is satisfied. The genetic algorithm (GA) transforms a population of individual behaviors into a new generation using the Darwinian evolutionary principle and naturally occurring genetic operations such as crossover and mutation [14]. Each individual in the population represents a possible behavior. The individuals

in the population are usually fixed-length character strings patterned after chromosome strings. GA attempts to find a very good behavior or the best behavior by genetically breeding the population of individuals. The basic operations of the genetic algorithm are simple and straight forward [14]: Reproduction: The act of making a copy of a potential solution. Crossover: The act of swapping gene values between two

potential solutions, simulating the “mating” of the two solutions.

Mutation: The act of randomly altering the value of a gene in a potential solution.

It is necessary to be able to evaluate how “good” a potential solution is relative to the other potential solutions. The “fitness function” is responsible for performing this evaluation and returning a positive integer number, or “fitness value” that reflects how optimal the solution is: the higher the number, the better the solution [14]. The fitness values are then used in a process of natural selection to choose which potential solutions will continue on to the next generation and which will die out. 5.3 How to Obtain Rhythmic Behavioral Images: First, we analyze the standardized changes in the angles of points corresponding to a design target using Daubechies wavelets and place the results into a database. Next, in order to reflect rhythmic behavioral images in the design targets, we use the GA. We need to decide on the makeup of the chromosomes, which includes the number of genes and what those genes represent. In this study, a chromosome represents a time series of the angles of a joint. We set up a gene to represent an angle in a frame (Fig. 6). It seems reasonable in this study that the measure of fitness in the GA would be a correlation between the rhythms of natural objects and those of the design target. In GA, the system analyzes the standardized changes in the angles of joints in a design target using Daubechies wavelets and then evaluates the correlation. Through this method, it is possible to design behaviors that reflect rhythmic behavioral images even if the behaviors are apparently not alike. The procedure is explained as follows: Step 1) The first three steps of approach 1 are performed. Step 2) The interpolated data is converted to a time series and

the changes in the angles are calculated at each interpolated point.

Step 3) The changes in the angles are standardized at each

interpolated point. Step 4) Rhythmic features are analyzed using Daubechies

wavelets at each interpolated point (input signals are the standardized changes in the angles and output signals are the coefficiencies).

The first four steps are performed on two natural objects to be blended. Step 5) The population is analyzed using GA.


Step 6) The correlation between the rhythms of two natural objects and those of a design target are evaluated and certain chromosomes are selected for breeding (The correlation is calculated at each point corresponding to a design target).

Step 7) The chromosomes are evolved.

Step 8) The procedure continues through a number of iterations between Step 5 and Step 7 until a design target can learn the rhythms.

.

Wavelet coefficiencies

Evaluate correlation

GA coding

)0θ()2θ(

)1(θ

Joint 0

Joint 1

Joint 2


Changes in the angles

Standardization

Signal processing using Daubechies wavelets

Frame 1

Chromosome of a point

Frame n

)0(iθ )1(iθ )2(iθ )1( −niθ )(niθFrame

0 Frame

2 Framen－1

°≤≤° 360)(0 jiθ: frame number : joint number

ji

),...,2 ,1 ,0( ki =


),...,2 ,1 ,0( ki =Changes in the angles

Standardization


),...,2 ,1 ,0( ki =Changes in the angles

Standardization

Frame n Frame n + 1

tΔ+ tΔ+ tΔ+ tΔ+ tΔ+ tΔ+

)(niθ )1( +niθ )(niθ )1( +niθ

Frame n Frame n + 1

Natural object 1 Natural object 2


Evaluate correlation

design target

: point number corresponding to a design target

i

Figure 6. Extracting and obtaining rhythmic images by GA.


Acquiring visual behavioral image of a natural object

The interpolated data (visual behavioral images) is converted to a time series

The changes in the angles are calculated at each interpolated point.

The behavior whose rhythm is the most similar to those of the natural objects is output

Behaviors whose rhythms are similar to those of the natural objects are evolved

Evaluating similarity between the natural objects and generated behaviors

Extracting rhythms of natural objects

Learning rhythms of natural objects by GA

Storing the

Figure 7. The procedure of learning rhythmic behavioral

images.

wavelet coefficiencies in the database

Initializing population (Generating many behavioral patterns)

The changes in the angles are calculated at each joint

Analyzing rhythms by wavelet analysis

Analyzing rhythms by wavelet analysis

calculation flow

reference

6 EXAMPLE OF BEHAVIORAL DESIGN USING THE PROPOSED METHODS

We designed “jumping and wavelike” behaviors for a robot arm on CG by using the two proposed methods. The structure of the robot arm is shown in Fig. 8. First, we designed using visual behavioral images. Next, we designed using rhythmic behavioral images. Designers can search for any natural systems using IDEA-INSPIRE. In this example, we created animations of natural objects instead of extracting motion data by image processing. We used the movements of frogs and snakes as the base objects for analogy in both visual and rhythmic operations.

Figure 8. Structure of a design target.

6.1 Example of approach 1–visual image: We designed “jumping and wavelike” behaviors for a robot arm using approach 1, which transfers visual behavioral images to a design target. Figure 9 shows some frames in sequence of the visual image of a frog and some frames in sequence of the visual image of a snake. Figure 10 shows some frames in sequence of blended visual behavioral images. The weights for the blending are shown in Table 1. Figure 11 shows some frames in sequence of the designed behavior for the robot arm.

Table 1. The weights for blending.

Item Value Weight 1 0.5 Weight 2 0.5

o x

y

)0θ()2θ()1(θ

Joint 0

Joint 1

Joint 2

Joint 3

1l 2l3l

)2 ,1 ,0(100

==

ili

°≤≤° 3600 iθ


Figure 9. Sequenced frames of the visual images of a frog and a snake.

Visual image of frog

Visual image of snake

+

+

+

+

+

+

+

+

+

+

+

+

Blended visual image

Figure 10. Blended image.

Designed robot

arm behavior

Figure 11. Designed behavior of a robot arm.


http://www.hitohaku.jp/education/frog/zukan/img/kajika.jpg

7 Example of Approach 2–rhythmic image: We designed “jumping and wavelike” behaviors for a robot arm using approach 2, which utilizes rhythmic behavioral images. The extracted rhythmic behavioral images are shown in Fig. 12 and Fig. 13. The system randomly generates angles between 0° and 90°at each gene and calculates the changes in the angles. The parameters of GA are shown in Table 2. The designed behavior is shown in Fig. 14.

Figure 12. Rhythmic image of a frog.

Figure 13. Rhythmic image of a snake.

Table 2. GA parameters.

Item Value Population 1000 Number of Generations 200 Probability of Crossover 0.6 Probability of Mutation 0.3

Rhythmic image of frog

Rhythmic image of snake

Designed robot

arm behavior

Figure 14. A designed behavior obtained using rhythmic images.

8 SUMMARY AND CONCLUSIONS In this paper, we have proposed two new methods for developing abstract behavioral images and emotions by which a designer can use analogies from natural things such as animals and plants even when there is a difference in the number of joints between the natural object and the design target. The first method uses visual behavioral images, the second uses rhythmic behavioral images. We used Hermite interpolation to extract behavioral images independent of the number of joints. We used wavelet analysis and the GA to abstract and learn the rhythmic images, respectively. We simulated the design of behaviors using the proposed methods and designed creative behaviors that reflect the images of the original objects. Creative behavior of a robot arm should satisfy utility as well as novelty. It should be explained how the behavior generated by the method satisfies the utility requirement of creativity. However, in this study we have only focused on the generating process of creative behaviors. In the future, we intend to evaluate the creativity of the behaviors designed by the two methods in this work from the viewpoint of originality and utility. In addition, we also plan to develop a searching method using rhythmic behavioral images.


http://www.hitohaku.jp/education/frog/zukan/img/kajika.jpg

9 REFERENCES [1] Billard, A., 2000, “Dreams and imitation in robot”,

Proceedings of the Socially Intelligent Agents-The Human in the Loop, AAAI Fall Symposium, Menlo Park, CA, pp. 9-12.

[2] Pahl, G., and Wolfgang, B., Engineering design: a systematic approach, Springer-Verlag, Berlin Ltd.

[3] Taura, T., Nagai, Y., 2005, “Primitives and Principles of Synthetic Process for Creative Design”, Computational and Cognitive Models of Creative Design Ⅵ, pp. 177-194.

[4] An, M., Taura, T., Shiose, T., 2007, “A study on Acquiring Underlying Behavioral Criteria for Manipulator Motion by Focusing on Learning Efficiency”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 37 (4), pp. 445-455.

[5] Gonzalez, R., and Woods, R., 2007, Digital Image Processing, Prentice Hall, US.

[6] An, M., Kagawa, K., and Taura, T., 2003, “A study on acquiring model’s criterion focusing on learning efficiency”, Proceedings of the Twelfth IASTED International Conference on Applied Simulation and Modeling.

[7] Finke, R., Ward, T., and Smith, S., 1992, Creative Cognition: Theory, Research, and Applications, Cambridge, the MIT Press, A Bradford Book, London.

[8] Boden, A., 2004, The Creative Mind Myths and Mechanisms, Routledge.

[9] Gardenfors, P., 2000, Conceptual Space, the MIT Press, A Bradford Book, London.

[10] Fauconnier, G., and Turner, M., 2002, The Way We Think – Conceptual Blending and the Mind’s Hidden Complexities, Basic Book, NY.

[11] Chakrabarti, A., Sarkar, P., Leelavathamma, B., and Nataraju, B.S., 2005, “A functional representation for aiding biomimetic and artificial inspiration of new ideas”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 19 (2), pp. 113-132.

[12] Ralston, A., and Rabinowitz, P., 2001, A First Course in Numerical Analysis, DoverPublications, UK.

[13] Percival, D., and Walden, A., 2000, Wavelet methods for time series analysis, Cambridge University Press, UK.

[14] Miettinen, K., Neittaanmaki, P., Periaux, J., and Makela, M., 1999, Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications, John Wiley & Sons Ltd, UK.


A METHOD FOR CREATIVE BEHAVIORAL DESIGN BASED ON ANALOGY AND BLENDING FROM NATURAL THINGS

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