Explaining and Clustering Playtraces Using Temporal Logics

This paper addresses the challenge of explaining gameplay behaviours and traces in video games using methods based on linear temporal logics (LTL).Applications for this range from classifying a player's game-style to craft personalised user experiences, to exploring the most significant behaviour patterns within a set of trajectories, particularly in the context of data-driven design and quality control assisted by black-box algorithms.We divide the problem into two complementary tasks.First, to infer a temporal characterisation of a registered play-style by means of a predicate in LTL from a set of representative traces and potential counterexamples.Second, to classify a diverse set of traces into groups in order to identify behavioural patterns within the samples.The first problem focuses on recognising what makes a behaviour unique when compared to others, while the second problem seeks to detect meaningful patterns in groups of players.For the first task, we propose a series of heuristic search methods in the LTL predicate space, such as Monte Carlo Tree Search and Grammatical Evolution.For the second, we introduce a new algorithm that clusters traces based on predicates that split them into cohesive sets, demonstrating how the methods of the first problem can be extrapolated to the latter.Both approaches are evaluated with practical experiments on a 3D third-person stealth game developed in Unity 3D, showcasing how these techniques can be used for analysis.Preliminary results obtained with real player traces provide evidence that these methodologies can support a more comprehensive understanding of observed behaviours.