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.

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Psychological models have been used to simulate emotions within agents as part of the decision-making process. The body of this work has focussed on applying the process of decision making using emotions to social dilemmas, notably the Prisoner's Dilemma. Previous work has focussed on agents which do not move around, with an initial analysis on how mobility and the environment can affect the decisions chosen. Additionally simulated mood has been introduced to the decision-making process. Exploring simulated emotions and mood to inform the decision-making process in multi-agent systems allows us to explore in further detail how outside influences can have an effect on different strategies. We expand and clarify aspects of how agents are affected by environmental differences. We show how emotional characters settle on an outcome without deviation by providing a formal proof. We validate how the addition of mood increases cooperation, while also showing how small groups achieve this quicker than large groups. Once pure defectors are added, to test the resilience of the cooperation achieved, we see that while agents with a low starting mood achieve a payoff closest to the pure defectors, they are reduced in numbers the most by the pure defectors.

Abstract. We report on experiences in the development of hybrid autonomous systems where high-level decisions are made by a rational agent. This rational agent interacts with other sub-systems via an abstraction engine. We describe three systems we have developed using the EASS BDI agent programming language and framework which supports this architecture. As a result of these experiences we recommend changes to the theoretical operational semantics that underpins the EASS framework and present a fourth implementation using the new semantics.