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Cognitively-Inspired Computational Design Methods

Cognitively-Inspired Computational Design Methods. Jonathan Cagan Dept of Mechanical Engineering and Kenneth Kotovsky Dept of Psychology Carnegie Mellon. Context. We can improve the design process by using teachings from cognition within an algorithmic framework

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Cognitively-Inspired Computational Design Methods

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  1. Cognitively-Inspired Computational Design Methods Jonathan Cagan Dept of Mechanical Engineering and Kenneth Kotovsky Dept of Psychology Carnegie Mellon

  2. Context • We can improve the design process by using teachings from cognition within an algorithmic framework • Create new breed of design automation tools • Test design strategies • Secondary benefits include • Test bed to study effect of cognitive decision making on design process • Formal representation of aspects of cognition

  3. Agent-Based Design Methods • Inspiration • Multiple members of interdisciplinary team work together to contribute to design solution • Individuals are independent at the micro scale but • Coordinate • Are directed by manager at the macro scale • Enable investigation of effectiveness and efficiency of using cognitive-based strategies in design search • Computation vs thinking

  4. Increased use of knowledge/cognition Agent-Based Design History(1995-2006) A-Design (w/ Campbell, 1999, 2000) Discovery vs Inventive Design (w/ Simon, 2001) Feedback learning in A-Design (w/ Campbell, 2003) Coordinated vs Collaborative Cooperation (w/ Olson, 2004) Using A-Design for Mfg Process Planning Optimization (w/ Deshpande, 2004) Cognitively-based learning in A-Design (w/ Moss, 2004) Large-scale Teams w/ collaborative agents (w/ Olson, 2006) Chunking (and LSA) in expertise in Design (w/ Moss, 2006)

  5. A-Design(w/ M. Campbell) • Dynamic design across multiple design objectives • Deep functional reasoning • Initially explore how much can be gained through computation cycles • Agents represent individual contributions to coupled and non-trivial problem

  6. Computer catalog M-agents Adjust agent behaviors Poor Designs Preserve Designs F-agents Designs are fragmented Pareto Good Designs Designs A-Design Flowchart Input and Output Specifications C-agents Creation of designs by maker-agents Designs are created Extract equations I-agents Designs are instantiated Designs are evaluated Designs are sorted Modification of designs by modification-agents Pareto Designs returned

  7. Evolutionary Design from Multiple Objectives

  8. y x z Weighing Machine Battery Capacitor Inductor coil Motor Pulley Shaft Stopper Transistor Rotational Bearing Rotational Damper Lever (class 1) Pipe Rack Solenoid Switch Electrical valve Translational Bearing Translational Damper Lever (class 2) Piston Relay Spring Tank Rotational valve Cable Gear Lever (class 3) Potentiometer Resistor Sprocket Torsional Spring Worm gear Output: Angle = [0, 5 rad.] Position : (2, 5, 0) Orientation : (-1, 0, 0) Interface = dial Input: Downward Force = [0, 300 lbs.] No Displacement Position = (0, 0, 0) Orientation : (0, -1, 0) Interface = footpad

  9. dial shaft lever gear rack bearing spring dial cylinder-2 lever gear cylinder-1 rack linear spring bearing dial lever-2 lever-4 lever-1 rack gear lever-3 spring motor resistor FP output FP input FP FP FP FP FP FP g FP g Design objectives: cost = $46.82, mass = 0.2kg, input dx = 4.1mm, accuracy = 0.4rad. FP FP output input FP FP FP FP FP FP FP FP g g Design objectives: cost = $616.18, mass = 1.3kg, input dx = 0.5mm, accuracy = 0.4 rad. FP input FP output FP FP FP FP FP FP FP FP FP FP g Design objectives: cost = $90.20, mass = 0.5kg, input dx = 0.7mm, accuracy = 0.2 rad. Weighing Machine Results

  10. --None --TODO Learning --TABOO Learning iterations iterations Taboo/Todo Effectiveness:Learning Trends w/in Runs –None --TODO --TABOO --BOTH --KICK --UP-DOWN

  11. Cognitive-based learning in A-Design: Across Problems(w/ J. Moss) • More extensive cognitive reasoning based on Soar-like chunking in taboo/todo session and ACT-R memory model • Learn chunks and their functional interface • Apply probabilistically based on effectiveness and frequency

  12. Cognition for Transfer Across Problems • Both across runs within a problem and across problem descriptions

  13. In-Problem Transfer

  14. Across-Problem Transfer

  15. ground ground ground ground pressure source pressure source cylinder cylinder large gear large gear spring spring bearing bearing lever lever lever lever shaft shaft rack rack torsion spring torsion spring Expert/Novice Studies on Functional Chunking 10 11 10 Freshmen 10 10 9 14 15 11 15 10 Seniors 11

  16. Extension to Distributed Collaboration in Design(w/ J. Olson)

  17. Distributed Collaboration in Design Olson, J., Cagan, J. (2004). Inter-agent ties in team-based computational configuration design. AIE EDAM (18) 135-152. Deshpande, S. and Cagan, J. (2003). An Agent Based Optimization Approach to Manufacturing Process Planning,. ASME Mech. Design.

  18. Distributed Collaboration in Design Olson, J., Cagan, J. (2004). Inter-agent ties in team-based computational configuration design. AIE EDAM (18) 135-152. Deshpande, S. and Cagan, J. (2003). An Agent Based Optimization Approach to Manufacturing Process Planning,. ASME Mech. Design.

  19. Average objective function in final list Results Number of designs Objective function Number of “good” designs identified Collaborative Separable

  20. NASA/JPL’s Team X and the conceptual design of space missions Courtesy JPL/NASA-Caltech

  21. Simulating Collaborative Design • Multiagent simulation of Team X and conceptual space mission plan • Patterned with social and domain definitions analogous to those populating the actual design environment • Designed to give rise to a collection of interrelationships comparable to those occurring in Team X design sessions

  22. 17 Agents, Iterative design loop >100,000 lines of code (java) Model General Structure • Team environment achieved through distribution and interaction of social and task niches

  23. Model Task Definition Mission scope: interplanetary orbiter to Enceladus to determine the geological history of the moon Rich models: 1120 domain methods, 1000 variables • Team environment achieved through distribution and interaction of social and task niches

  24. General structure • Represented types: • Information updates • Collaboration: direct agreement • Collaboration: iterative negotiation • Facilitation Model Agent Interactions

  25. Individual Rates of Progression

  26. Model verification: Task

  27. Conclusions • Agents provide platform to explore cognitive-based design automation • Balance of computation with collaboration and process knowledge most effective • Cognitive reasoning critical to recognizing and advancing computational creativity • Smart agents are emerging as effective design tool

  28. Publications • Campbell, M., J. Cagan, and K. Kotovsky, “A-Design: An Agent-Based Approach to Conceptual Design in a Dynamic Environment”, Research in Engineering Design, Vol. 11, pp. 172-192, 1999. • Campbell, M., J. Cagan, and K. Kotovsky, “Agent-based Synthesis of Electro-Mechanical Design Configurations”, ASME Journal of Mechanical Design, Vol. 122, No. 1, pp. 61-69, 2000. • Cagan, J., K. Kotovsky, and H.A. Simon, “Scientific Discovery and Inventive Engineering Design: Cognitive and Computational Similarities.” in: Formal Engineering Design Synthesis, E.K. Antonsson and J. Cagan, eds., Cambridge University Press, Cambridge, UK, 2001. • Campbell, M., J. Cagan, and K. Kotovsky, “The A-Design Approach to Managing Automated Design Synthesis”, Research in Engineering Design, Vol. 14, No. 1, pp. 12-24, 2003. • Deshpande, S., and J. Cagan, "An Agent Based Optimization Approach to Manufacturing Process Planning", ASME Journal of Mechanical Design, Vol. 126, No. 1, pp. 46-55, 2004. • Moss, J., J. Cagan, and K. Kotovsky, “Learning from Design Experience in an Agent-Based Design System”, Research in Engineering Design, Vol. 15, pp. 77-92, 2004. • Olson, J. T., and J. Cagan, “Inter-Agent Ties in Computational Configuration Design”, Artificial Intelligence in Engineering Design, Analysis and Manufacturing, (Special Issue on Agent-Based Design), Vol. 18, No. 2, pp. 135-152, 2004. • Moss, J., K. Kotovsky, and J. Cagan, “Expertise Differences in the Mental Representation of Mechanical Devices in Engineering Design”, Cognitive Science, Vol. 30, No. 1, pp. 65-93, 2006. • Olson, J., J. Cagan, and K. Kotovsky, “Unlocking Organizational Potential: A Computational Platform for Investigating Structural Interdependencies in Design,” Proceedings of the 2006 ASME Design Engineering Technical Conferences: Design Theory and Methodology Conference, DETC2006-99464, September, Philadelphia, 2006.

  29. Support • National Science Foundation under grant EID-9256665 • Defense Advanced Research Projects Agency (DARPA) and Rome Laboratory, Air Force Materiel Command, USAF, under agreement number F30602-96-2-0304 • National Defense Science and Engineering Graduate Fellowship • Air Force Office of Scientific Research, Air Force Material Command, USAF, under grant numbers F49620-01-1-0050 and FA9620-04-1-0201

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