The field of General Game Playing (GGP) usually yearns to create agents that are able to win any game as efficiently as possible. Utility functions are then easy to find: winning the game, or achieving a better score, nets you better results. This resulted in game playing becoming a testbed for artificial intelligence. New techniques are often tested on games such as Chess, Go or Checkers where the environments are easy to formally define, while the task is still being tied closely to human intelligence. Superhuman-level has been achieved in a great number of games past the 2010s. The f irst notable instance was Chess world champion Garry Kasparov losing to DeepBlue, a chess playing computer, in 1997 during a six-game match. Researchers became interested in games where heuristics were harder to encode, such as Go. Only in 2016 did AlphaGo become the first computer Go program to beat a 9-dan professional player, Lee Sedol. It did so using novel techniques, such as Deep Neural Networks (DNN) trained using self-play to evaluate positions and Monte-Carlo Tree Search (MCTS) to search the game tree, which are now staples of superhuman-level game playing agents. Such level of play was only achieved in agents playing specific games. In such instances, an optimized game representation and evaluation functions can be built by leveraging domain-specific knowledge. General Game Playing (GGP) aims to generalize game playing agents so that they can pick up any game. In this more general setting, domain specific knowledge can only be used scarcely, leading to more polyvalent yet weaker agents. Research in the domain of GGP has been kicked off by Stanford’s GDL (Game Description Language), a standard language based on first-order logic used to describe games. While superhuman-level game playing is still a goal of GGP, this report investigates another approach: agents that mimic human behavior in games. Game playing agents are often described as "black boxes", human players cannot comprehend what their plans are when playing against them. This makes such agents unable to provide: • an enjoyable yet challenging experience for opponents • realistic game playing data • deeper understanding of games through studying their insights The first point would be of interest to online platforms. While tentatives have been made to limit the strength of AI opponents through artificial means, like limiting search depth. This indeed reduces the overall strength of the agent, but does not make their playing less convoluted. The second point would help game designers test their creations cheaply and efficiently using human-like agents as gatherers of experience. The third point would be of utmost importance to professional and competitive players trying to better understand the game they play to make a living. This would place AI agents as companions to gain deeper insights, rather than unbeatable opponents used as a basis for the quality of actions played. The later points are of particular interest to the Digital Ludeme Project (DLP) and GameTable COST Action, which aimed to model, reconstruct and map ancient games and their transmission across history and cultures. Modelling and reconstructing such games was done using the Ludii Game Playing System. It features a description language for games that is easier to use than Stanford’s GDL (1300 games implemented in Ludii for only 52 in Stanford’s GDL) for modelling games. Reconstruction is important because ancient games are often only partially recovered. Rules, equipment, or records of playing data can be missing. In order to fill this gap, Ludii uses artificial agents to generate, test, and analyze plausible rulesets. This methodology can be criticized because of one key flaw: agents used in this process do not mimic human behavior. Human motivations for playing games can be vastly more diverse than simply winning. From getting into tricky situations for educational purposes, to asserting strength over a weaker player by showcasing skillful plans, or losing on purpose to avoid upsetting the opponent, there are many situations where winning is not everything. The general theory of cognition of humans playing games is also vague and thus difficult to formalize. The literature on these questions is sparse and difficult to get into. It encompasses research domains such as psychology and cognitive science at the frontier of game theory. This report aims to provide a survey of the current techniques, roadblocks and concepts that have been explored to create more human general game playing agents.