Artificial agents will need to be aware of human moral and social norms, and able to use them in decision-making. In particular, artificial agents will need a principled approach to managing conflicting norms, which are common in human social interactions. Existing logic-based approaches suffer from normative explosion and are typically designed for deterministic environments; reward-based approaches lack principled ways of determining which normative alternatives exist in a given environment. We propose a hybrid approach, using Linear Temporal Logic (LTL) representations in Markov Decision Processes (MDPs), that manages norm conflicts in a systematic manner while accommodating domain stochasticity. We provide a proof-of-concept implementation in a simulated vacuum cleaning domain.
The combination of moral and social norms with Markov Decision Processes is not new. Much of this work, e.g. @cite_12 , tends to emphasize norm emergence, thus lacks explicit representations of norms. Other work @cite_10 considers incorporating deontic logic norms using an agent architecture that reasons about the consequences (in the environment) of violating norms.
@cite_10 @cite_12
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Rational self-interested agents, which act so as to achieve the best expected outcome, should violate the norms if the expected rewards obtained with the defections from the norms surpass the expected rewards obtained by being norm-compliant. It means they should estimate the earnings brought about by the violations and the losses caused by their respective reactions. In this paper, we present a rational self-interested agent model that takes into account the possibility of breaking norms. To develop such model, we employ Markov Decision Processes (MDPs). Our approach consists of representing the reactions for norm violations within the MDPs in such a way that the agent is able to reason about how those violations affect her expected utilities and future options. Finally, we perform an experiment in order to establish comparisons between the model presented in this work and its norm-compliant version. Behavioral norms are key ingredients that allow agent coordination where societ al laws do not sufficiently constrain agent behaviors. Whereas social laws need to be enforced in a top-down manner, norms evolve in a bottom-up manner and are typically more self-enforcing. While effective norms can significantly enhance performance of individual agents and agent societies, there has been little work in multiagent systems on the formation of social norms. We propose a model that supports the emergence of social norms via learning from interaction experiences. In our model, individual agents repeatedly interact with other agents in the society over instances of a given scenario. Each interaction is framed as a stage game. An agent learns its policy to play the game over repeated interactions with multiple agents. We term this mode of learning social learning, which is distinct from an agent learning from repeated interactions against the same player. We are particularly interested in situations where multiple action combinations yield the same optimal payoff. The key research question is to find out if the entire population learns to converge to a consistent norm. In addition to studying such emergence of social norms among homogeneous learners via social learning, we study the effects of heterogeneous learners, population size, multiple social groups, etc.