Abstract
Achieving social order in societies of self-interested autonomous agents is a difficult problem due to lack of trust in the actions of others and the temptation to seek rewards at the expense of others. In human society, social norms play a strong role in fostering cooperative behaviour-as long as the value of cooperation and the cost of defection are understood by a large proportion of society. Prior work has shown the importance of both norms and metanorms (requiring punishment of defection) to produce and maintain norm-compliant behaviour in a society, e.g. as in Axelrod's approach of learning of individual behavioural characteristics of boldness and vengefulness. However, much of this work (including Axelrod's) uses simplified simulation scenarios in which norms are implicit in the code or are represented as simple bit strings, which limits the practical application of these methods for agents that interact across a range of real-world scenarios with complex norms. This work presents a generalisation of Axelrod's approach in which norms are explicitly represented and agents can choose their actions after performing what-if reasoning using a version of the event calculus that tracks the creation, fulfilment and violation of expectations. This approach allows agents to continually learn and apply their boldness and vengefulness parameters across multiple scenarios with differing norms. The approach is illustrated using Axelrod's scenario as well as a social dilemma from the behavioural game theory literature.