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Computational Intelligent Design

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Decisions in large building projects are difficult to take. In particular the reasoning behind preferred solution directions should be comprehensive, consistent and transparent. The difficulty is due to involvement of diverse, linguistic concepts in the decision criteria, such as operational certainty, financial certainty, sustainability, or attractiveness. This makes differentiation among solutions imprecise, and hampers communication of the reasoning between experts and clients. This issue is alleviated when advanced computational methods are used to model the criteria. Beyond this, computation is employed to generate designs with maximal performance. This way the chance of overlooking a prominent solution is minimized.

Multi-dimensional performance models: Beyond Common 3D-models or Building Information Modeling (BIM)

Computation of visual perception aspects of an urban scene

Judging a design needs consideration of many facts at the same time. Existing analysis tools, such as computational fluid dynamics (CFD) and finite element methods (FEM) together with expert assessments provide elemental information about a design. The expert assessments include judgements of aesthetic aspects in immersive virtual reality (VR) environments, for example. However, when design decisions are taken it is necessary to interpret and combine the results from the tools. This way the suitability of solutions is judged in a holistic manner. Improving this judgement processes is especially proficient in the early design stage, where uncertainty is high and the influence on later stages is crucial.

Design alternative with a computed performance of 0.52

This means that sketches, common 3D models and building information models (BIM) are sources to obtain input information for a multi-dimensional performance model. In the model every dimension refers to a certain design aspect. Using this model the suitability of a design is computed with respect to multiple dimensions at the same time.

Multi-dimensional performance model using a neuro-fuzzy system

This is accomplished by representing computationally the linguistic concepts involved in the judgement, such as constructability, functionality, sustainability, attractiveness or cost effectiveness.

Design alternative with a computed performance of 0.48

This has previously not been accomplished since computation conventionally deals with crisp numeric information. The essential machinery to compute with linguistic concepts is fuzzy logic. However, traditionally fuzzy logic systems are not suitable to handle many dimensions. Therefore it was not possible to make immediate use of available expert knowledge in a computational manner up till now. We developed special fuzzy information processing system that resolves this bottleneck.

Enhanced decision making

The next step beyond multi-dimensional performance modelling is multi-dimensional performance-based design. In this approach a multi-dimensional performance model is integrated into a computational generation process.

Multi-dimensional performance-based design by means of a cognitive system using evolutionary computation & fuzzy logic A cognitive system based on fuzzy information processing and multi-objective evolutionary algorithm, IEEE Congress on Evolutionary Computation –CEC 2009, Trondheim, Norway, 18-21st May 2009

Illustration of fuzzy information processing at a neuron to obtain the design performance. A cognitive system based on fuzzy information processing and multi-objective evolutionary algorithm , IEEE Congress on Evolutionary Computation –CEC 2009, Trondheim, Norway, 18-21st May 2009

This way design parameters are incorporated into a complex algorithm, namely evolutionary algorithm with fuzzy neural computation, that finds the best set of solutions to meet the objectives set by the design team.

Two Pareto optimal solutions generated by a multi-dimensional performance-based design system Solution Diversity in Multi-Objective Optimization: A study in Virtual Reality. World Congress on Computational Intelligence WCCI 2008, Hong Kong (2008)

Visual representation of optimal solutions for two objectives in the urban design

The solutions obtained in this way are known as Pareto-optimal front. They provide a variety of outstanding alternatives to a decision maker, since none of these solutions is outperformed by another one. Every solution is equally valid, and a decision maker selects among them with great confidence.

One of the Pareto optimal solutions for an interior design task, where visual perception plays a role in the computational optimisation  A cognitive system based on fuzzy information processing and multi-objective evolutionary algorithm, IEEE Congress on Evolutionary Computation –CEC 2009, Trondheim, Norway, 18-21st May 2009

Visual representation of optimal solutions for four objectives in the interior design task

As a computational solution set has been built, alternate designs are explored by varying the parameters. The generative system can handle effectively up to five objectives, and has no restriction regarding the number of variables playing role on the objectives. The amount of variables characterizing a solution is only limited by available computational time and power. For more than four objectives the five-dimensional Pareto front can be represented as well.

Visual representation of optimal solutions for five objectives

The strength of the approach is that project solutions can be assessed without any presupposition, and confidence of finding the best solution is increased. Human and computational cognitive system are in an interaction loop: Human decision maker is setting the criteria, computations privide optimal solutions for these, based on these solutions the decision maker modifies criteria and so on, until a Pareto-optimal solution matches the designer's complete preferences as far as possible.

Cognitive approach to performance-based design: a designer explores the Pareto-front and modifies the criteria in an iterative manner. From perceptual towards cognitive robotics in the framework of evolutionary computation. In: Pennacchio, S. (ed.): Emerging Technologies, Robotics and Control Systems. InternationalSAR, Palermo, Italy (2009)

Perception modeling

Architectural design involves perception-based requirements, such as visual openness or visual privacy. Such requirements are challenging to treat, because the human vision process is highly complex, involving brain processes. Therefore the comparison of perceptual properties among scenes is imprecise.

To let perception play a more prominent role in design, a model of human vision is developed. The model is based on probabilistic terms. This way the complexity of the vision process is absorbed.

Unbiased visual attention for a nearby object

Unbiased visual attention for a distant object

The model is implemented by means of an avatar in virtual reality. The avatar experiences the environment in a human-like manner, so that the results are used during the evaluation of design alternatives.

From perceptual towards cognitive robotics in the framework of evolutionary computation. In: Pennacchio, S. (ed.): Emerging Technologies, Robotics and Control Systems. InternationalSAR, Palermo, Italy (2009)

Probability density of perception for objects that are oriented perpendicular to the observer's forward direction Visual perception theory underlying perceptual navigation. In: Emerging Technologies, Robotics and Control Systems International Society for Advanced Research (2007) 139-153

Perception measurement in virtual reality. Towards computer-based perception by modeling visual perception: a probabilistic theory. Proc. IEEE International Conference on Systems, Man and Cybernetics, October 8-11, 2006, Taipei, Taiwan.

Perception analysis of an interior space

The authors are with the Chair of Design Informatics, Delft University of Technology, Faculty of Architecture, Dept. of Building Technology, Julianalaan 134, 2628 BL Delft , NL  | Email: i.s.sariyildiz@tudelft.nl ; m.s.bittermann@tudelft.nl ; o.ciftcioglu@tudelft.nl   | Tel: +31-6 39250915

 

Recent publications (2006-2009)

Book chapters

1. Bittermann, M.S., Sariyildiz, I.S., and Ciftcioglu, Ö.: Blur in Human Vision and Increased Visual Realism in Virtual Environments. In: Lecture Notes on Computer Science: Springer Verlag (2007)
2. Ciftcioglu, O.: Multiresolutional Filter Application for Spatial Information Fusion in Robot Navigation. Robotics, Automation and Control. IN-TECH Publishing, Vienna (2008)
3. Ciftcioglu Ö., Bittermann M.S.: From perceptual towards cognitive robotics in the framework of evolutionary computation. In: Pennacchio, S. (ed.): Emerging Technologies, Robotics and Control Systems. InternationalSAR, Palermo, Italy (2009)
4. Ciftcioglu, Ö.: Shaping the Perceptual Robot Vision and Multiresolutional Kalman Filtering Implementation. Int. Journal Factory Automation, Robotics and Soft Computing (2008) In: Emerging Technologies, Robotics and Control Systems International Society for Advanced Research (2007)
5. Ciftcioglu, Ö., Bittermann, M.S., and Sariyildiz, I.S.: Visual perception theory underlying perceptual navigation. In: Emerging Technologies, Robotics and Control Systems International Society for Advanced Research (2007) 139-153
6. Sariyildiz, I.S., Bittermann, M.S., and Ciftcioglu, O.: Perception & Architecture. In: The Architecture Annual 2005-2006 Delft University of Technology, H. Bekkering, D. Hauptmann, A. d. Heijer, J. Klatte, U. Knaack, and S. v. Manen, Eds. Rotterdam: 010 Publishers (2007) 104-109
7. Ciftcioglu Ö and Sariyildiz I.S Fuzzy logic for stochastic modeling in Soft Methods for Integrated Uncertainty Modelling, Advances in Soft Computing, J. Lawry, E. Miranda, A. Bugarin, S. Li, M. A. Gil, P. Grzegorzewski, and O. Hryniewicz, Eds. Tokyo: Springer, 2006.

Journal papers

1. Bittermann M.S., Ciftcioglu Ö.: Visual perception model for architectural design. Journal of Design Research 7 (2008) 35-60
2. Bittermann M.S., Sariyildiz I.S. and Ciftcioglu Ö. Visual perception in design and robotics. J Integrated Computer-Aided Engineering, 14 (2007) 73-91
3. Ciftcioglu O.: A fuzzy neural tree for possibilistic reliability. J. Advanced Computational Intelligence and Intelligent Informatics (JACIII) 13 (2009)
4. Ciftcioglu, Ö.: Shaping the Perceptual Robot Vision and Multiresolutional Kalman Filtering Implementation. Int. Journal Factory Automation, Robotics and Soft Computing (2008)
5. Bittermann, M.S., Ciftcioglu, O.: A cognitive system based on fuzzy information processing and multi-objective evolutionary algorithm. IEEE Conference on Evolutionary Computation - CEC 2009. IEEE, Trondheim, Norway (2009)
6. Ciftcioglu Ö., Bittermann M.S.: Multiobjective Optimization for Cognitive Design. Joint 4th Int. Conf. on Soft Computing and Intelligent Systems (SCIS & ISIS), Nagoya, Japan (2008)
7. Ciftcioglu, Ö., Bittermann, M.S., and Sariyildiz, I.S.: Visual perception theory underlying perceptual navigation. Int. J. Factory Autom., Robotics and Soft Comp. (2007) 171-185
8. Ciftcioglu Ö.,  Bittermann M.S. and Sariyildiz I.S. Multiresolutional fusion of perceptions for perceptual robotics. Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII) 11 (2007) 688-700

Conference papers 2009

1. Bittermann, M.S., Ciftcioglu, O.: A cognitive system based on fuzzy information processing and multi-objective evolutionary algorithm. IEEE Conference on Evolutionary Computation - CEC 2009. IEEE, Trondheim, Norway (2009)

Conference papers 2008

1. Ciftcioglu Ö., Bittermann M.S.: Solution Diversity in Multi-Objective Optimization: A study in Virtual Reality. World Congress on Computational Intelligence WCCI 2008, Hong Kong (2008)
2. Ciftcioglu Ö.: A fuzzy neural tree for possibilistic reliability. Joint 4th Int. Conf. on Soft Computing and Intelligent Systems (SCIS & ISIS), Nagoya, Japan (2008)
3. SCIS paper nr.2
4. Sariyildiz I.S., Bittermann M.S., Ciftcioglu Ö.: Multi-objective optimization in the construction industry. AEC 2008, Antalya, Turkey (2008)
5. Sariyildiz, I.S., Bittermann, M.S., Ciftcioglu, Ö.: Performance-based Pareto optimal design. In: Rusák, I.H.a.Z. (ed.): TMCE 2008, Izmir, Turkey (2008)

Conference papers 2007

1. Ciftcioglu, O., Bittermann, M.S., and Sariyildiz, I.S. Building performance analysis supported by GA. Proc. 2007 IEEE Congress on Evolutionary Computation, Singapore (2007) 489-495
2. Ciftcioglu, Ö. and Sariyildiz, I.S.: Further studies on visual perception for perceptual robotics. Proc. Fourth Int. Conf. Informatics in Control, Automation and Robotics - ICINCO2007, Angers, France (2007) 468-744
3. Ciftcioglu, Ö., Bittermann, M.S., and Sariyildiz, I.S.: Sensor data fusion in autonomous robotics. Proc. The 2nd Int. Conf. Innov. Comp., Inf. and Contr. - ICICIC 2007, Kumamoto, Japan (2007)
4. Ciftcioglu, Ö., Bittermann, M.S., and Sariyildiz, I.S.: Fuzzy neural tree for knowledge driven design. Proc. The 2nd Int. Conf. Innov. Comp., Inf. and Contr. - ICICIC 2007, Kumamoto, Japan (2007)
5. Ciftcioglu, Ö., Bittermann, M.S., and Sariyildiz, I.S.: A neural fuzzy system for soft computing. Proc. NAFIPS 2007, San Diego, USA (2007) 489-495

Conference papers 2006

1. Ciftcioglu Ö, Bittermann M.S and Sariyildiz I.S Towards computer-based perception by modeling visual perception: a probabilistic theory. Proc. IEEE International Conference on Systems, Man and Cybernetics, October 8-11, 2006, Taipei, Taiwan.
2. Ciftcioglu Ö, Bittermann M.S and Sariyildiz I.S Fusion of perceptions for perceptual robotics. Proc. NAFIPS’06, June 3-6, 2006, Montréal, Québec, Canada.
3. Ciftcioglu Ö, Bittermann M.S, and Sariyildiz I.S Studies on visual perception for perceptual robotics. Proc. ICINCO 2006, 3rd Int. Conference on Informatics in Control, Automation and Robotics, August 1-5, 2006, Setubal, Portugal.
4. Bittermann M.S and Ciftcioglu Ö Real-time measurement of perceptual qualities in conceptual design. Proc. 6-th Internationl Symposium on Tools and Methods of Competitive Engineering TMCE 2006, April 18-22, 2006, Ljubljana, Slovenia.
5. Bittermann M.S, Sariyildiz I.S, and Ciftcioglu Ö Visual space perception model Identification by evolutionary search. Proc. 9-th International Design Conference - Design 2006, May 15-18, 2006, Dubrovnik, Croatia.
6. Ciftcioglu Ö, Bittermann M.S and Sariyildiz I.S Application of a visual perception model in virtual reality (poster). Proc. ACM SIGGRAPH Symposium on Applied Perception in Graphics and Visualization, APGV’2006, July 28-30, Boston, USA, 2006.
7. Bittermann M.S and Ciftcioglu Ö. Validation of a visual perception model. Proc. Joint International Conference on Construction Culture, Innovation, and Management (CCIM), 26-29 November 2006, Dubai, United Arabian Emirates.
8. Ciftcioglu Ö and I. Sevil Sariyildiz Knowlegde model for knowledge managememnt in the construction industry. Proc. Joint International Conference on Construction Culture, Innovation, and Management (CCIM), 26-29 November 2006, Dubai, United Arabian Emirates.
9. Ciftcioglu Ö and Sariyildiz S.I On the efficiency of multivariable TS fuzzy modeling. Proc. 2006 IEEE International Conference on Fuzzy Systems, July 16-21, 2006, Vancouver, BC, Canada.
10. Ciftcioglu Ö and Sariyildiz S.I On the efficiency of fuzzy logic for stochastic modeling. Proc. NAFIPS’06, June 3-6, 2006, Montréal, Québec, Canada.
11. Ciftcioglu Ö, Bittermann M.S and Sariyildiz I.S Autonomous robotics by perception. Proc. ISCIS & ISIS 2006, Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on Advanced Intelligent Systems, September 20-24, 2006, Tokyo, Japan.
12. Ciftcioglu Ö, Bittermann M.S and Sariyildiz I.S Fuzzy ARX modeling of dynamic systems. Proc. ISCIS & ISIS 2006, Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on Advanced Intelligent Systems, September 20-24, 2006, Tokyo, Japan.

Implementation

Design by Genetic Algorithm

To execute the software your internet browser needs to have a plug-in installed called Virtools Web Player. If you wish to install it, please visit the website http://virtual.tudelft.nl/ and click on "Visit the 3D model of Mekelpark 2008" in the middle of the screen. Please note that you need administrator rights on the machine you perform the installation.