Designing fitness functions for odour source localisation

ABSTRACTLocating odour sources is a hard task that has been addressed with a large variety of AI methods to produce search strategies with different levels of efficiency and robustness. However, it is still not clear how to evaluate those strategies. Simply evaluating the robot's ability to reach the goal may produce deceptive fitness values, favouring poor strategies that do not generalise. Conversely, including prior knowledge may bias the learning process. This work studies the impact of evaluation functions with various degrees of prior knowledge, in evolving search strategies. The baseline is set by performing multiple evaluations of each strategy with a function that only evaluates the task efficiency. A function was found that is able to produce strategies with equivalent performance to those of the baseline, whilst performing a single evaluation.