The VGC AI Competition is a unified vehicle for designing and testing AI agents in game design and gameplayrelated tasks, specifically within Pokémon. In this competition, players must both strategically battle to maximize their win rate and anticipate their opponents' teams to improve their chances of victory before the battles. In this paper, we expand the existing competition by introducing a new track centered on a novel game balance issue, Rules Balance. Rather than modifying game units to encourage specific usage rates of Pokémon or teams during a competitive season, the focus is on reconfiguring the Pokémon battle rules to incentivize non-damaging moves (moves with delayed rewards) by rational agents. This AI assistant tool relieves game designers of the costly and time-consuming task of testing numerous parameter combinations to achieve desired move usage patterns. This allows designers to allocate more time to the creative process of developing new game mechanics. We present and evaluate an initial agent based on a genetic search that competes against a tree-search battle agent. While the battle agent is motivated to devise the most efficient plan of action to win, the balance agent adjusts the rule parameters to encourage the rational agent to use specific desired moves.

Acquired brain injury (ABI) is a condition that causes damage to brain tissues and consequently leads to physical, behavioral and cognitive issues. One of the impairments observed after the injury is in the realm of social cognition (SC). This impairment leads these patients to lose their ability to interact socially in a positive and effective manner. Consequently, these patients may socially isolate themselves and lose interpersonal relationships. One of the methods used for cognitive rehabilitation that has proven to be effective is the use of serious games (SG). SG are video games with the purpose of learning and improving people’s live. Thus, in this work, a set of SG will be developed for the cognitive and psychosocial rehabilitation of people with ABI, focusing on socio-emotional skills training. To ensure their success, a co-design methodology will be carried out during the conception and implementation of the game. This approach will involve patients, healthcare professionals, and caregivers to obtain suggestions and feedback to ensure that the most suitable solutions are implemented in the game. At the end of this work, we hope to have developed a method for training socio-emotional skills that can motivate patients more effectively than traditional methods.

This paper introduces an ongoing project focused on co-designing a serious game (SG) to explore emotion regulation (ER) strategies with parents. The game’s main goal is for parents to learn to regulate their emotions, enabling them to support their adolescents’ emotional development. Semi-structured interviews were conducted to pinpoint the SG’s key motivating and learning features. The participants include four healthcare professionals with an average of 14.75 years of experience. The insights from these interviews show promising concepts that were integrated into the SG. The innovative nature of this project is highlighted by the fact that, to our knowledge, this will be the first SG focusing on training ER skills in parents.

Meta-game balance is a crucial task in game development, and automation of this process could assist game developers by vastly reducing time costs. We explore and evaluate a meta-game balance model over the recently proposed VGC AI Competition Framework. We propose an adversarial model where team builder agents try to maximize their win rate by narrowing to the most optimal team configurations, resulting in a reduction of the diversity of Pokémon employed, while a balancing agent re-adapts the Pokémon inner attributes to incentive the team builder agents to incorporate a greater variety of Pokémon into their teams increasing the meta-game's overall diversity and balance. one Furthermore, we developed multiple team builder agents divided into two groups: the first group assumes that individual Pokémon advantages are the primary factor to determine the outcome of game matches; the second group also exploits the implicit synergy between teammates. These agents make use of meta-gaming, linear optimization, and evolutionary search to find strong combinations against the current meta-game. The strongest team builder is faced against the team meta-game balance agent for its evaluation. Deep learning is also employed to predict the outcome of matches and recommend constructive elements of teams.

Regardless of the goal of a game, it should be a pleasant and fun experience for its players. For some games to be enjoyable, the level of difficulty must be carefully calibrated, otherwise, players will feel bored or frustrated. Multiplayer scenarios in particular, where one player’s satisfaction might not translate to the enjoyment of other players and poses extra challenges in balancing the difficulty. The performance of one player is relative to the opponent, versus single-player scenarios where we can fully control the environment. We propose an AI automation framework for difficulty balancing in two-player games, where balancing is seen as a Reinforcement Learning task. A Game Master (GM) agent learns how to use handicap game mechanics, signaled by a reward function that evaluates a weighted combination of aesthetic criteria that encourages dramatization and allows a player in the lead to go back and a player in the rear to catch up, creating the desired rubber banding effect that balances out skill gaps. The quality of the games with the trained GM embedded is examined by measuring the same aesthetic criteria on the resulting games, and by analyzing the resulting changes in the game.

This work presents a framework for a new type of meta-game balance AI Competition based on Pokémon, Pokémon battles can be viewed as adversarial games played by AIs. Around these games, there is also a meta-game: which Pokémon to include in a team for battles, which moves to pick for every Pokémon in the team, etc. This meta-game is itself a game with a set of rules that govern which Pokémon and which moves are available in the roster that can be selected from, or which attributes (health points, damage, etc.) a Pokémon or moves should have. The aim of the framework is to facilitate competitions in creating the most balanced meta-game possible; one where there is a large variety of Pokémon and moves to choose from, and many possible combinations that are effective. AI agents could assist human designers in achieving strategically expressive meta-games, and this type of benchmark could incentivize game designers and researchers alike to advance knowledge on this type of domain.