Learning Comparative User Models for Accelerating Human-Computer Collaborative Search
Interactive Evolutionary Algorithms (IEAs) are a powerful explorative search technique that utilizes human input to make subjective decisions on potential problem solutions. But humans are slow and get bored and tired easily, limiting the usefulness of IEAs. Here we describe our system which works toward overcoming these problems, The Approximate User (TAU), and also a simulated user as a means to test IEAs. With TAU, as the user interacts with the IEA a model of the user’s preferences is constructed and continually refined and this model is what is used as the fitness function to drive evolutionary search. The resulting system is a step toward our longer term goal of building a human-computer collaborative search system. In comparing the TAU IEA against a basic IEA it is found that TAU is 2.5 times faster and 15 times more reliable at producing near optimal results.
KeywordsEvolutionary Design Interactive Evolutionary Algorithm
Unable to display preview. Download preview PDF.
- 1.Barnum, G.J., Mattson, C.A.: A computationally assisted methodology for preference-guided conceptual design. Journal of Mechanical Design 132 (2010)Google Scholar
- 4.Bridle, J.: Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Fogelman-Soulie, H. (ed.) Neurocomputing: Algorithms, Architectures and Applications. NATA ASI Series. Springer (1990)Google Scholar
- 5.Caldwell, C., Johnston, V.S.: Tracking a criminal suspect through ’face-space’ with a genetic algorithm. In: Booker, R.K.B.L.B. (ed.) Proc. of the Fourth Intl. Conf. on Genetic Algorithms, pp. 416–421. Morgan Kaufmann, San Mateo (1991)Google Scholar
- 6.Campbell, M.I., Rai, R., Kurtoglu, T.: A stochastic graph grammar algorithm for interactive search. In: 14th Design for Manufacturing and the Life Cycle Conference, pp. 829–840. ASME (2009)Google Scholar
- 7.Clune, J., Lipson, H.: Evolving Three-Dimensional Objects with a Generative Encoding Inspired by Developmental Biology. In: Proc. European Conference on Artificial Life, pp. 144–148. Springer (2011)Google Scholar
- 9.Dawkins, R.: The Blind Watchmaker. Harlow Longman (1986)Google Scholar
- 11.Secretan, J., Beato, N., Ambrosio, D.B.D., Rodriguez, A., Campbell, A., Folsom-Kovarik, J.T., Stanley, K.O.: Picbreeder: A case study in collaborative evolutionary exploration of design space. Evolutionary Computation (2011)Google Scholar
- 12.Sims, K.: Artificial Evolution for Computer Graphics. In: SIGGRAPH 1991 Conference Proceedings. Annual Conference Series, pp. 319–328 (1991)Google Scholar
- 13.Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 1275–1296 (2001)Google Scholar