This paper presents a grammar-based evolutionary approach that incorporates abstractions to learn complex collective behaviours through their simpler representations. We propose modifications to the grammar syntax design and genome structure to facilitate evolution of abstractions in separate genome partitions. Two abstraction techniques based on behavioural decomposition and environmental scaffolding are presented to derive these simpler representations. Parallel and incremental learning architectures incorporated with grammatical evolution (GE) are investigated with three complex problems to evaluate their potential in generating collective multi-agent behaviours. The results infer that both learning architectures surpass a generic GE model in performance for evolving complex behaviours. Furthermore, using environmental scaffolding reduces the robustness of the model than when only the behavioural decomposition technique is used. However, it has more potential to generate solutions with better fitness than when scaffoldings are not used. The evaluations suggest that, by incorporating abstraction learning architectures with grammar-based evolution can significantly improve the performance of an agent system in complex problem domains.

Trust has been widely recognized as particularly significant among the factors influencing team performance. Trust directly impacts team performance as it plays a pivotal role in the decisions that each team member (each agent) makes regarding their own actions and their interactions with fellow team members (other agents). Hence, determining the appropriate actions of each agent in the team based on perceived trust information is critical to ensure optimal team performance. However, there is no generic mechanism dedicated to such a problem. This paper addresses this problem by proposing a novel trust architecture which integrates differentiated trust with response strategies. Differentiated trust is multidimensional trust with each trust dimension representing trust in an agent's abilities to perform the task associated with that dimension. With differentiated trust, an agent can differentiate the trustworthiness of another agent in performing different subtasks (a secondary task or a portion of the primary task). To further fulfill the transition from perceptual trust to practical actions, responses strategies are introduced. Each response strategy associates trust levels with the available actions through a distinct deterministic strategy. The high dimensional trust enabled by the differentiated trust is used as the input of a response strategy for more nuanced manipulation of the interactions. The impact of the proposed trust architecture is demonstrated through an experimental investigation. A platform simulating team performance is built based on a food foraging task. Scenarios embodying different types and proportions of a priori flawed agents are introduced to enable the system to distinguish between agents in terms of trust; thus, providing a potential to optimize team performance through the trust architecture. It is demonstrated that the proposed trust architecture enables optimized team performance in various scenarios involving agents with different trustworthiness by the appropriate determination of each agent's actions in the interactive teamwork.