Learning Comparative User Models for Accelerating Human-Computer Collaborative Search

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Gregory S. Hornby, Josh Bongard: Learning Comparative User Models for Accelerating Human-Computer Collaborative Search. In: EvoMUSART 2012, S. 117-128.




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.

Extended Abstract


booktitle={Evolutionary and Biologically Inspired Music, Sound, Art and Design},
series={Lecture Notes in Computer Science},
editor={Machado, Penousal and Romero, Juan and Carballal, Adrian},
title={Learning Comparative User Models for Accelerating Human-Computer Collaborative Search},
url={http://dx.doi.org/10.1007/978-3-642-29142-5_11 http://de.evo-art.org/index.php?title=Learning_Comparative_User_Models_for_Accelerating_Human-Computer_Collaborative_Search },
publisher={Springer Berlin Heidelberg},
keywords={Evolutionary Design; Interactive Evolutionary Algorithm},
author={Hornby, GregoryS. and Bongard, Josh},

Used References

Barnum, G.J., Mattson, C.A.: A computationally assisted methodology for preference-guided conceptual design. Journal of Mechanical Design 132 (2010)

Bongard, J., Lipson, H.: Nonlinear system identification using coevolution of models and tests. IEEE Transactions on Evolutionary Computation 9, 361–384 (2005)

Bongard, J., Lipson, H.: Automated reverse engineering of nonlinear dynamical systems. Proceedings of the National Academy of Sciences 104, 9943–9948 (2007)

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)

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)

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)

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)

Cybenko, G.: Approximations by superpositions of a sigmoidal function. Math. Contrl., Signals, Syst. 2, 303–314 (1989)

Dawkins, R.: The Blind Watchmaker. Harlow Longman (1986)

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)

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)

Sims, K.: Artificial Evolution for Computer Graphics. In: SIGGRAPH 1991 Conference Proceedings. Annual Conference Series, pp. 319–328 (1991)

Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 1275–1296 (2001)

Wannarumon, S., Bohez, E.L.J., Annanon, K.: Aesthetic evolutionary algorithm for fractal-based user-centered jewelry design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22, 19–39 (2008)


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