Cognitively-Inspired
Computational Design Methods
Jonathan Cagan
Dept of Mechanical Engineering
and
Kenneth Kotovsky
Dept of Psychology
Carnegie Mellon
©2006, Cagan and Kotovsky
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
©2006, Cagan and Kotovsky
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
©2006, Cagan and Kotovsky
Agent-Based Design History
(1995-2006)
A-Design
(w/ Campbell, 1999, 2000)
Feedback learning
in A-Design
Discovery vs
Inventive Design
(w/ Campbell, 2003)
(w/ Simon, 2001)
Coordinated vs
Collaborative
Cooperation
Using A-Design for Mfg (w/ Olson, 2004)
Cognitively-based
Process Planning
Large-scale
learning in A-Design Optimization
(w/ Moss, 2004)
Teams w/
(w/ Deshpande, 2004)
collaborative
Chunking (and LSA) in
agents
expertise in Design
(w/ Olson, 2006)
(w/ Moss, 2006)
Increased use of knowledge/cognition
©2006, Cagan and Kotovsky
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
©2006, Cagan and Kotovsky
A-Design Flowchart
Input and Output
Specifications
C-agents
Designs are
created
Creation of
Computer
catalog
Extract
equations
designs by
M-agents
I-agents
maker-agents
Adjust agent
behaviors
Designs
are instantiated
Designs are
evaluated
Designs are
sorted
Pareto
Designs
Preserve
Designs
Pareto Designs
returned
Good
Designs
Poor
Designs
Modification of designs by
modification-agents
F-agents
Designs are
fragmented
©2006, Cagan and Kotovsky
Evolutionary Design from
Multiple Objectives
©2006, Cagan and Kotovsky
Weighing Machine
Battery
Translational Bearing
Capacitor
Translational Damper
Inductor coil
Lever (class 2)
Motor
Piston
Pulley
Relay
Shaft
Spring
Stopper
Tank
Transistor
Rotational valve
Rotational Bearing Cable
Rotational Damper Gear
Lever (class 1)
Lever (class 3)
Pipe
Potentiometer
Rack
Resistor
Solenoid
Sprocket
Switch
Torsional Spring
Electrical valve
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
y
x
z
©2006, Cagan and Kotovsky
Weighing Machine Results
FP output
FP input
Design objectives:
cost = $46.82, mass = 0.2kg,
input dx = 4.1mm, accuracy = 0.4rad.
dial
FP
FP
FP
shaft
FP
FP
lever
gear
rack
bearing
spring
FP g
FP g
FP output
FP input
dial
cylinder-2
FP
FP
lever
FP
FP
FP
Design objectives:
cost = $616.18, mass = 1.3kg,
input dx = 0.5mm, accuracy = 0.4 rad.
FP
gear
cylinder-1
rack
linear
bearing
FP g
spring
FP g
FP input
FP output
dial
lever-2
FP
FP
lever-4
FP
FP
FP
Design objectives:
cost = $90.20, mass = 0.5kg,
input dx = 0.7mm, accuracy = 0.2 rad.
FP
lever-1
rack
FP
gear
lever-3
spring
motor
FP
FP g
FP
resistor
©2006, Cagan and Kotovsky
Taboo/Todo Effectiveness:
Learning Trends w/in Runs
--None
–None
--TODO
--TABOO
--BOTH
--KICK
--UP-DOWN
Learning
--TABOO Learning
Utility
Utility
--TODO
iterations
iterations
©2006, Cagan and Kotovsky
Cognitive-based learning in ADesign: 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
©2006, Cagan and Kotovsky
Cognition for Transfer Across Problems
• Both across runs within a problem and across
problem descriptions
QuickTime™ and a
None decompressor
are needed to see this picture.
©2006, Cagan and Kotovsky
In-Problem Transfer
Punch Press - Within Problem
10000
9000
8000
6000
5000
4000
3000
2000
1000
60
55
50
45
40
35
30
25
20
15
10
0
5
Evaluation
7000
Iteration
No chunks
Punch press chunks
©2006, Cagan and Kotovsky
Across-Problem Transfer
Pressure Gauge - Between Problems
10000
9000
8000
6000
5000
4000
3000
2000
1000
60
55
50
45
40
35
30
25
20
15
10
0
5
Evaluation
7000
Iteration
No chunks
Weighing machine chunks
Punch press chunks
©2006, Cagan and Kotovsky
Expert/Novice Studies on
Functional Chunking
10
pressure
source
11
cylinder
lever
lever
rack
Freshmen
spring
large
gear
10
shaft
10
torsion spring
bearing
10
ground
9
pressure
source
14
cylinder
lever
ground
lever
10
Seniors
rack
large
15 gear
shaft
11
15
torsion spring
spring
bearing
11
ground
ground
©2006, Cagan and Kotovsky
Extension to Distributed
Collaboration in Design
(w/ J. Olson)
©2006, Cagan and Kotovsky
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.
©2006, Cagan and Kotovsky
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.
©2006, Cagan and Kotovsky
Average objective function in final list
Objective function
22000
18000
14000
10000
6000
0.5
18
35.5
53
70.5
88
105.5
Number of “good” designs identified
Number of designs
Results
26000
1000
800
600
400
200
0
0.5
18
35.5
53
70.5
88
105.5
Time (s)
Collaborative
New Agent Method
Separable
Original
Agent Method
©2006, Cagan and Kotovsky
NASA/JPL’s Team X and the conceptual design of space missions
Courtesy JPL/NASA-Caltech
©2006, Cagan and Kotovsky
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
©2006, Cagan and Kotovsky
Model General Structure
X
X
X
X
-
X
X
-
X
Owner
-
Team Leader
X
-
Trajectory Visualization
X
Mission Design
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
-
X
X
-
X
X
ACS
Telecom
X
CDS
Propulsion
X
Science
Systems
Programmatics
Thermal
X
Ground
Power
-
-
Cost
Structure
Software
Configuration
Structure
Power
Thermal
Cost
Ground
Programmatics
Systems
Propulsion
Telecom
Science
Instrumentation
CDS
ACS
Mission Design
Trajectgory Visualization
Team Leader
Owner
Configuration
Software
>100,000 lines of code (java)
Instrumentation
17 Agents, Iterative design loop
X
X
X
X
-
X
-
X
X
-
X
X
X
X
X
X
X
X
-
X
-
X
X
-
X
X
-
X
-
X
X
-
-
•Team environment achieved through distribution and
interaction of social and task niches
©2006, Cagan and Kotovsky
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
©2006, Cagan and Kotovsky
Model Agent Interactions
Represented types:
- Information updates
- Collaboration: direct agreement
- Collaboration: iterative negotiation
General structure
- Facilitation
©2006, Cagan and Kotovsky
Individual Rates of Progression
©2006, Cagan and Kotovsky
Model verification: Task
©2006, Cagan and Kotovsky
Conclusions
• Agents provide platform to explore cognitivebased 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
©2006, Cagan and Kotovsky
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. 135152, 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.
©2006, Cagan and Kotovsky
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
©2006, Cagan and Kotovsky