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Test moment determination design in active robot learningIn recent years, service robots have been increasingly used in people's daily live. These robots are autonomous or semiautonomous and are able to cooperate with their human users. Active robot learning (ARL) is an approach to the development of beliefs for the robots on their users' intention and preference, which is needed by the robots to facilitate the seamless cooperation with humans. This approach allows a robot to perform tests on its users and to build up the high-order beliefs according to the users' responses. This study carried out primary research on designing the test moment determination component in ARL framework. The test moment determination component is used to decide right moment of taking a test action. In this study, an action plan theory was suggested to synthesis actions into a sequence, that is, an action plan, for a given task. All actions are defined in a special format of precondition, action, post-condition and testing time. Forward chaining reasoning was introduced to establish connection between the actions and to synthesis individual actions into an action plan, corresponding to the given task. A simulation environment was set up where a human user and a service robot were modelled using MATLAB. Fuzzy control was employed for controlling the robot to carry out the cooperative action. In order to examine the effect of test moment determination component, simulations were performed to execute a scenario where a robot passes on an object to a human user. The simulation results show that an action plan can be formed according to provided conditions and executed by simulated models properly. Test actions were taken at the moment determined by the test moment determination component to find the human user's intention.