UCT CS Research Document Archive

Evolving Generalised Maze Solvers

Shorten, David and Geoff Nitschke (2015) Evolving Generalised Maze Solvers. In Proceedings European Conference on the Applications of Evolutionary Computation (Evostar 2015), pages 783-794, Copenhagen, Denmark.

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Abstract

This paper presents a study of the efficacy of comparative controller design methods that aim to produce generalised problem solving behaviours. In this case study, the goal was to use neuro-evolution to evolve generalised maze solving behaviours. That is, evolved robot controllers that solve a broad range of mazes. To address this goal, this study compares objective, non-objective and hybrid approaches to direct the search of a neuro-evolution controller design method. The objective based approach was a fitness function, the non-objective based approach was novelty search, and the hybrid approach was a combination of both. Results indicate that, compared to the fitness function, the hybrid and novelty search evolve significantly more maze solving behaviours that generalise to larger and more difficult maze sets. Thus this research provides empirical evidence supporting novelty and hybrid novelty-objective search as approaches for potentially evolving generalised problem solvers.

EPrint Type:Conference Paper
Subjects:I Computing Methodologies: I.2 ARTIFICIAL INTELLIGENCE
ID Code:1197
Deposited By:Nitschke, Geoff
Deposited On:23 November 2017