Actively probing and modeling users in interactive co-evolution

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Schmidt, M., Lipson, H.: Actively probing and modeling users in interactive co-evolution. In: Keijzer, M., et al. (eds.) Proc. of the Genetic and Evolutionary Computation Conference, GECCO-2006, pp. 385–386. ACM Press, Seattle (2006)



A major challenge in interactive evolution is extracting user preferences with minimal probing. We introduce an interactive multi-objective coevolutionary algorithm that actively selects the most informative probes: We simultaneously coevolve a population of candidate models that explain users' selection so far, and a population of candidate probes that cause the most divergence among model predictions, thereby elucidating model uncertainties (divergence). As progress is made, we begin selecting for probes with the highest expected outcome averaged among different models, thereby exploiting model certainties (consensus). In the evolution of pen stroke drawings, we find this technique to be highly effective at extracting preference models from very limited human interaction. Using only pair-wise preference questions, strategy and preference in pen stroke drawings are extracted in fewer than ten user probes. Our results show that the optimal questions to probe the user need not include drawings similar to the target drawing. Instead, the user models converge on trends in the user responses, thereby extrapolating strong preference for target drawings which the models are never actually trained to prefer.

Extended Abstract


Used References

Ihsan Ecemis , Eric Bonabeau , Trent Ashburn, Interactive estimation of agent-based financial markets models: modularity and learning, Proceedings of the 2005 conference on Genetic and evolutionary computation, June 25-29, 2005, Washington DC, USA

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Viktor Zykov , Josh Bongard , Hod Lipson, Co-evolutionary Variance Can Guide Physical Testing in Evolutionary System Identification, Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware, p.213-220, June 29-July 01, 2005


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