Revisiting MAB based approaches to recursive delegation

Open multi-agent systems (MAS) are composed of agents under different organisational control, and whose internal goals and mental states cannot be observed. In such systems, agents often have differing capabilities, and must rely on each other when pursuing their goals, making task delegation commonplace. This delegation occurs when one agent (the delegator) requests that another (the delegatee), execute a task. A fundamental problem faced by the delegator involves selecting the most appropriate delegatee to whom the task should be delegated, and a significant body of work centred around trust and reputation systems has examined how such a delegation decision should take place [6, 12, 13]. At their heart, trust and reputation systems associate a rating with each potential delegatee, and select who to delegate a task to based on this rating. Following task execution, the rating is updated based on how well the task was completed. Different systems compute the ratings differently, for example incorporating indirect information from other agents in the system [8, 14], or utilising social and cognitive concepts as part of the computation process [4]. Trust and reputation systems can also differ in the way they select a delegatee, for example by using the rating to weigh the likelihood of selection. While trust and reputation systems seek to satisfy many properties including resistance to different types of attacks by malicious agents [7], at their heart, they balance the exploration of delegatee behaviour with the exploitation of high quality delagatees.