15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois Intelligent Agents in Design Zbigniew Skolicki Tomasz Arciszewski

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 2 Outline Agents and learning Attributes Statistical analysis Directed Evolution perspective Conclusions

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 3 Agents and learning

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 4 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

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 5 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

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 6 Attributes

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 7 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

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 8 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?

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 9 Agents in Design

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 10 Agents in Design Reemergence of interest First International Workshop on Agents in Design, MIT 2002

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 11 Conducted analysis 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

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 12 Results Gaussian distribution  attributes chosen independently? Expected mean attributes value: Simple agents Complex agents

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 13 Identified Agents Features (each attribute analyzed independently) 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

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 14 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 = 90% confidence

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 15 Directed Evolution

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 16 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

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 17 Current stage of IAs (according to Patterns of Evolution) 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

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois 18 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

15 th International Conference on Design Theory and Methodology 2-6 September 2003, Chicago, Illinois Thank you for your attention! Questions?