15th International Conference on Design Theory and Methodology
Intelligent Agents in Design
Zbigniew Skolicki
Tomasz Arciszewski
2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Outline
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Agents and learning
Attributes
Statistical analysis
Directed Evolution perspective
Conclusions
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Agents and learning
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Agent versus Intelligent Agent
• Agent
– Autonomous
– Active
– Takes initiative
– Repeatedly interacts with the environment
(and the user of other agents)
– Mobile?
• Intelligent Agent:
– Adaptive
– Learning
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Our definition of
Intelligent Agent (IA)
An intelligent agent is an autonomous
system situated within an environment, it
senses its environment, maintains some
knowledge and learns upon obtaining new
data and, finally, it acts in pursuit of its own
agenda to achieve its goals, possibly
influencing the environment
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Attributes
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Attributes
• 27 binary attributes
For example:
Information – whether agents store information locally or
in some shared memory
Stability – whether agents can reconfigure in the runtime
Swarm? – whether there is enough number of agents
interacting to create macroscopic effects
• Values for attributes:
0 = simple behavior
1 = complex behavior
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
5 classes of attributes
• Sensing & Acting
What the pattern of interaction is, is it dynamic, what causes action, what
resources are available?
• Reasoning
What is communicated, how deep and fast and prompt the reasoning is?
• Learning & Knowledge
How adaptable the agent is, where is the knowledge stored, is it consistent,
what can an agent learn?
• Structure
How is an agent built, is it reusable, can it reconfigure, can new
agents be added?
• Quantity
How many agents interact and can it lead to an emergent behavior?
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Agents in Design
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Agents in Design
• Reemergence of interest
• First International Workshop on
Agents in Design, MIT 2002
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Conducted analysis
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17 agents or agent systems
27 binary attributes
17 x 27 = 459 values assessed
Statistical analysis of the values
– Mean attribute value
– Each attribute analyzed separately
– Swarm systems analyzed separately
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Results
Expected mean attributes value:
Simple
agents
Complex
agents
Gaussian distribution  attributes chosen independently?
2-6 September 2003, Chicago, Illinois
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15th International Conference on Design Theory and Methodology
Identified Agents Features
(each attribute analyzed independently)
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
Emergence
Amount
Swarm?
Agent flex
Reusable
System flex
Layers
Diversity
Stability
Know others
Adaptiveness
Naïve
Ontologies
Language
Transparency
Speed
Empathy
Roles
Reaction
Mobility
Benevolence
Goal
Autonomy
0
Commitment
0.1
Resources
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•
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•
average attribute
Information
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Act locally
Cooperate
Are sophisticated
Are not real-time
Do not model other
agents
Do not show internal
state
Are trustful
Acquire knowledge
Have stable architecture
Work in groups
Perspective
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Swarm agents unique features
Act more locally
Share resources
Have less autonomy
Are more competitive
Are more mobile
May discover roles in runtime
React more directly
Are not real-time…
Are less transparent
Use fixed language
Assume information to be true
Are less reusable
Swarms vs. Individuals
swarms
individuals
1.2
1
0.8
average value
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0.6
0.4
0.2
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• = 90% confidence
2-6 September 2003, Chicago, Illinois
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15th International Conference on Design Theory and Methodology
Directed Evolution
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Directed evolution
• Evolution of engineering systems occurs
according to Patterns of Evolution
• Directed Evolution enables planning and
development of future generations of
engineering systems.
• Theory of Inventive Problem Solving
(TRIZ)
• Line of evolution
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Current stage of IAs
(according to Patterns of Evolution)
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Run-time acquisition of knowledge
Growing number of features
Growing flexibility and controlability
Starting simplification
Component architecture ?
Common-day use
Automation and decreased human
involvement
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Conclusions
• Learning and adaptability important
• Agents in Design still in early evolution
stages
• Directed Evolution approach premature
• Research niches: simple, reconfigurable,
reusable, competing, real-time, modeling
others, non-naïve agents
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2-6 September 2003, Chicago, Illinois
15th International Conference on Design Theory and Methodology
Thank you for your attention!
Questions?
2-6 September 2003, Chicago, Illinois