Evolutionary Testing of Autonomous Software Agents

A system built in terms of autonomous agents may require even greater correctness assurance than one which is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents’ developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimization to generate demanding test cases.

Measuring Ultrasonic Communication Between Mouse Pups and Adult Mother Mice

Measuring ultrasonic communication provides us with a way to study parental influence on animals. In this study I measured the ultrasonic communication between mouse pups and two maternal females, one of which who had given birth to the pups and the other had raised them. I found that there was no significant difference between the amount of noise expressed by pups in response to each the biological mother and foster mother test groups. Mouse pups call to maternal females regardless of genetic relatedness. Communication in mice may be a more complicated model because of their communal nature.