It is well documented that cooperation may not be achieved in societies where self-interested agents are engaging in Prisoner’s Dilemma scenarios. In this paper we demonstrate, in contrast, that agent societies that use human-inspired emotions within their decision making, can reach stability in cooperation. Our work makes use of the Ortony, Clore, and Collins (OCC) model of emotions and we analyse the evolutionary stability of two different implementations that make use of key emotions from this model. Firstly, we consider an agent society that solely make use of this model of emotions for the agents’ decision making. Secondly we look at a model that extends the emotional agents with a model for representing mood. We set out a proof that shows that our emotional agents are an evolutionarily stable strategy when playing against a worst-case scenario strategy. The proof demonstrates that our established model of emotional agents enables evolutionary stability to be achieved, without modification to this model. In contrast, the model of moody agents was shown not to be an evolutionarily stable strategy. Our analysis sheds light on the nature of cooperation within agent societies and the useful role that simulated emotions can play in the agents’ decision making and the society as a whole.

We study the evolution of fairness in a multiplayer version of the classical Ultimatum Game in which a group of N Proposers offers a division of resources to M Responders. In general, the proposal is rejected if the (average) proposed offer is lower than the (average) response threshold in the Responders group. A motivation for our work is the exchange of flexibilities between smart energy communities, where the surplus of one community can be offered to meet the demand of a second community. In the absence of any Responder selection criteria, the co-evolving populations of Proposers and Responders converge to a state in which proposals and acceptance thresholds are low, implying an unfair exchange that favors Proposers. To circumvent this, we test different rules which determine how Responders should be selected, contingent on their declared acceptance thresholds. We find that selecting moderate Responders optimizes overall fairness. Selecting the lowest-demanding Responders maintains unfairness, while selecting the highest-demanding individuals yields a worse outcome for all due to frequent rejected proposals. These results provide a practical message for institutional design and the proposed model allows testing policies and emergent behaviors on the intersection between social choice theory, group bargaining, competition, and fairness elicitation.

Numerous studies have developed and analysed strategies for maximising utility in social dilemmas from both an individual agent’s perspective and more generally from the viewpoint of a society. In this paper we bring this body of work together by investigating the success of a wide range of strategies in environments with varying characteristics, comparing their success. In particular we study within agent-based simulations, different interaction topologies, agents with and without mobility, and strategies with and without adaptation in the form of reinforcement learning, in both competitive and cooperative settings represented by the Prisoner’s Dilemma and the Stag Hunt, respectively. The results of our experiments show that allowing agents mobility decreases the level of cooperation in the society of agents, due to singular interactions with individual opponents that limit the possibility for direct reciprocity. Unstructured environments similarly support a greater number of singular interactions and thus higher levels of defection in the Prisoner’s Dilemma. In the Stag Hunt, strategies that prioritise risk taking show a greater level of success regardless of environment topology. Our range of experiments yield new insights into the role that mobility and interaction topologies play in the study of cooperation in agent societies.

Simulating mood within a decision making process has been shown to allow cooperation to occur within the Prisoner’s Dilemma. In this paper we propose how to integrate a mood model into the classical reinforcement learning algorithm Sarsa, and show how this addition can allow self-interested agents to be successful within a multi agent environment. The human-inspired moody agent will learn to cooperate in social dilemmas without the use of punishments or other external incentives. We use both the Prisoner’s Dilemma and the Stag Hunt as our dilemmas. We show that the model provides improvements in both individual payoffs and levels of cooperation within the system when compared to the standard Sarsa model. We also show that the agents’ interaction model and their ability to differentiate between opponents influences how the reinforcement learning process converges.

In this paper we explore a novel perspective on surveillance robotics, which is based on a coordination principle of honeybees, and on the integration of an autonomous telepresence robot in such system. Coordination principles, based on biological systems such as ant, bee and termite colonies, show several properties which are essential to multi-robot surveillance, including low computation load, robustness, scalability and adaptability. In this paper we aim to improve on the efficiency of such a robotic swarm by taking a human in the loop by means of a telepresence robot. The human operator controlling the telepresence robot will aim to speed up the convergence of the swarm. The experiments, which evaluate the proposed multi- robot coordination system both in simulation and on real robots, show how the telepresence robot substantially increases the efficiency of the process.