I have long been intrigued by archaeogaming--an academic discipline that explores the fusion of archaeological objects, methods, and characters into video games. So I was thrilled when the video game company Ubisoft released Assassin's Creed: Origins, set in Egypt during Cleopatra's reign. The designers collaborated with Egyptologists to ensure everything from the architecture to the hieroglyphics created an accurate, immersive world. Unexpectedly, this partnership inspired a machine-learning spinoff that changed the course of my early career. While working with Egyptologists, the game developers learned that translating and interpreting ancient hieroglyphic texts is time-consuming, and the process has changed little in the last century.
This week, host Karen Han talks to voice actor and performer Erika Ishii, whose very long resume includes video games, animated series, and live action projects. In the interview, Erika explains their process of bringing video game characters to life–characters like Valkyrie in the game Apex Legends. Then Erika discusses diversity among both characters and performers in the video game industry and the ability to say no to projects that aren't the right fit. After the interview, Karen and co-host Isaac Butler talk about diversity in entertainment and the progress that has yet to be made. In the exclusive Slate Plus segment, Erika lists some of the voice actors and performances that have inspired them over the years.
Do you love artificial intelligence games? Artificial intelligence (AI) has played an increasingly important and productive role in the gaming industry since IBM's computer program, Deep Blue, defeated Garry Kasparov in a 1997 chess match. AI is used to enhance game assets, behaviors, and settings in various ways. According to some experts, the most effective AI applications in gaming are those that aren't obvious. Every year, AI games come in a variety of forms. Games will utilize AI differently for each kind. It's more than likely that artificial intelligence is responsible for the replies and actions of non-playable characters. Because these characters must exhibit human-like competence, it is essential there. AI was previously used to foretell your next best move. AI enhances your game's visuals and solves gameplay issues (and for) you in this age of gaming. AI games, on the other hand, are not reliant upon AI. AI technologies improved significantly as a result of research for game development.
Many games often share common ideas or aspects between them, such as their rules, controls, or playing area. However, in the context of General Game Playing (GGP) for board games, this area remains under-explored. We propose to formalise the notion of "game concept", inspired by terms generally used by game players and designers. Through the Ludii General Game System, we describe concepts for several levels of abstraction, such as the game itself, the moves played, or the states reached. This new GGP feature associated with the ludeme representation of games opens many new lines of research. The creation of a hyper-agent selector, the transfer of AI learning between games, or explaining AI techniques using game terms, can all be facilitated by the use of game concepts. Other applications which can benefit from game concepts are also discussed, such as the generation of plausible reconstructed rules for incomplete ancient games, or the implementation of a board game recommender system.
In this work, we presented a study regarding two important aspects of evolving feature-based game evaluation functions: the choice of genome representation and the choice of opponent used to test the model. We compared three representations. One simpler and more limited, based on a vector of weights that are used in a linear combination of predefined game features. And two more complex, based on binary and n-ary trees. On top of this test, we also investigated the influence of fitness defined as a simulation-based function that: plays against a fixed weak opponent, plays against a fixed strong opponent, and plays against the best individual from the previous population. For a testbed, we have chosen a recently popular domain of digital collectible card games. We encoded our experiments in a programming game, Legends of Code and Magic, used in Strategy Card Game AI Competition. However, as the problems stated are of general nature we are convinced that our observations are applicable in the other domains as well.
We present a new concept called Game Mechanic Alignment theory as a way to organize game mechanics through the lens of environmental rewards and intrinsic player motivations. By disentangling player and environmental influences, mechanics may be better identified for use in an automated tutorial generation system, which could tailor tutorials for a particular playstyle or player. Within, we apply this theory to several well-known games to demonstrate how designers can benefit from it, we describe a methodology for how to estimate mechanic alignment, and we apply this methodology on multiple games in the GVGAI framework. We discuss how effectively this estimation captures intrinsic/extrinsic rewards and how our theory could be used as an alternative to critical mechanic discovery methods for tutorial generation.
Rating systems play an important role in competitive sports and games. They provide a measure of player skill, which incentivizes competitive performances and enables balanced match-ups. In this paper, we present a novel Bayesian rating system for contests with many participants. It is widely applicable to competition formats with discrete ranked matches, such as online programming competitions, obstacle courses races, and some video games. The simplicity of our system allows us to prove theoretical bounds on robustness and runtime. In addition, we show that the system aligns incentives: that is, a player who seeks to maximize their rating will never want to underperform. Experimentally, the rating system rivals or surpasses existing systems in prediction accuracy, and computes faster than existing systems by up to an order of magnitude.
Recent progress in Game AI has demonstrated that given enough data from human gameplay, or experience gained via simulations, machines can rival or surpass the most skilled human players in classic games such as Go, or commercial computer games such as Starcraft. We review the current state-of-the-art through the lens of wargaming, and ask firstly what features of wargames distinguish them from the usual AI testbeds, and secondly which recent AI advances are best suited to address these wargame-specific features.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
This paper investigates the geometrical properties of real world games (e.g. Tic-Tac-Toe, Go, StarCraft II). We hypothesise that their geometrical structure resemble a spinning top, with the upright axis representing transitive strength, and the radial axis, which corresponds to the number of cycles that exist at a particular transitive strength, representing the non-transitive dimension. We prove the existence of this geometry for a wide class of real world games, exposing their temporal nature. Additionally, we show that this unique structure also has consequences for learning - it clarifies why populations of strategies are necessary for training of agents, and how population size relates to the structure of the game. Finally, we empirically validate these claims by using a selection of nine real world two-player zero-sum symmetric games, showing 1) the spinning top structure is revealed and can be easily re-constructed by using a new method of Nash clustering to measure the interaction between transitive and cyclical strategy behaviour, and 2) the effect that population size has on the convergence in these games.