Board games have often been recognised as a tool to model complex concepts in abstract environments for entertainment, education, and research in fields such as military and artificial intelligence. With more board games being designed and published, it is timely to draw attention towards board game design strategies and mechanics which capture the attributes that drive game play. The game design and the mechanics used define the structure, functionality and play experience of these games. Towards this end, this paper presents a data driven review of board game mechanics and play-related attributes, their interactions and relationships. The analysis expects to draw insights into how board games can be utilised across diverse domains as a tool to understand and explore complex concepts through abstract models. The investigations focus on identifying the trends and patterns of board games being published and their individual mechanics over time. Moreover, the correlation between mechanics and play-related attributes such as game complexity, rating and duration are explored. The interactions and similarities between individual mechanics based on co-occurrence, mutual information and clustering based approaches are also illustrated. The results show that the level of complexity and engagement of a game is not a simple function of the set of mechanics used, but rather the interactions that exist between mechanics, and the nature of their specific implementation are the critical factors in determining play experience of a board game.
This paper presents a novel Flow-based reinforcement learning strategy to model agent systems that can adapt to complex and dynamic problem environments by incrementally mastering their skills. It is inspired by the psychological notion of Flow that describes the optimal mental state experienced by an individual when they are fully immersed in a task and find it intrinsically rewarding to engage with. The proposed model presents an algorithm to describe the Flow experience such that agents can be trained through finer distinctions to the challenges across training time to maintain them in the Flow zone. In contrast to the traditional and incremental learning approaches that suffer from limitations associated with overfitting, the Flow-based model drives agent behaviours not simply through external goals but also through intrinsic curiosity to improve their skills and thus the performance levels. Experimental evaluations are conducted across two simulation environments on a maze navigation task and a reward collection task with comparisons against a generic reinforcement learning model and an incremental reinforcement learning model. The results reveal that these two models are prone to overfit under different design decisions and loose the ability to perform in dynamic variations of the tasks in varying degrees. Conversely, the proposed Flow-based model is capable of achieving near optimal solutions with random environmental factors, appropriately utilising the previously learned knowledge to identify robust solutions to complex problems.
This paper presents an evolutionary computing based approach to automatically synthesise swarm behavioural rules from their atomic components, thus making a step forward in trying to mitigate human bias from the rule generation process, and leverage the full potential of swarm systems in the real world by modelling more complex behaviours. We identify four components that make-up the structure of a rule: control structures, parameters, logical/relational connectives and preliminary actions, which form the rule space for the proposed approach. A boids simulation system is employed to evaluate the approach with grammatical evolution and genetic programming techniques using the rule space determined. While statistical analysis of the results demonstrates that both methods successfully evolve desired complex behaviours from their atomic components, the grammatical evolution model shows more potential in generating complex behaviours in a modularised approach. Furthermore, an analysis of the structure of the evolved rules implies that the genetic programming approach only derives nonreusable rules composed of a group of actions that is combined to result in emergent behaviour. In contrast, the grammatical evolution approach synthesises sound and stable behavioural rules which can be extracted and reused, hence making it applicable in complex application domains where manual design is infeasible.
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