Self-Learning Algorithms for the Personalised Interaction with People with Dementia

The number of people with dementia (PwD) residing in nursing homes (NH) increases rapidly. Behavioural disturbances (BDs) such as wandering and aggressions are the main reasons to hospitalise these people. Social robots could help to resolve these BDs by performing simple interactions with the patients. This paper examines whether self-learning algorithms could be designed to select the robotic interactions, preferred by the patients, during an intervention. K-armed bandit algorithms were compared in simulated environments for single and multiple patients to find the beneficial learning agents and action selection policies. The single patient tests show the advantages of selecting actions according to an Upper Confidence Bound (UCB) policy, while the multi-patient tests analyse the benefits of using additional, contextual information. Afterwards, the learning application was provided with a framework to operate in more realistic situations. We expect that this framework can be used for personalised interactions in many different healthcare domains.