Human-like agents are an increasingly important topic in games and beyond. Believable non-player characters enhance the gaming experience by improving immersion and providing entertainment. They also offer players the opportunity to engage with AI entities that can function as opponents, teachers, or cooperating partners. Additionally, in games where bots are prohibited -- and even more so in non-game environments -- there is a need for methods capable of identifying whether digital interactions occur with bots or humans. This leads to two fundamental research questions: (1) how to model and implement human-like AI, and (2) how to measure its degree of human likeness. This article offers two contributions. The first one is a survey of the most significant challenges in implementing human-like AI in games (or any virtual environment featuring simulated agents, although this article specifically focuses on games). Thirteen such challenges, both conceptual and technical, are discussed in detail. The second is an empirical study performed in a tactical video game that addresses the research question: "Is it possible to distinguish human players from bots (AI agents) based on empirical data?" A machine-learning approach using a custom deep recurrent convolutional neural network is presented. We hypothesize that the more challenging it is to create human-like AI for a given game, the easier it becomes to develop a method for distinguishing humans from AI-driven players.

Accurately estimating human skill levels is crucial for designing effective human-AI interactions so that AI can provide appropriate challenges or guidance. In games where AI players have beaten top human professionals, strength estimation plays a key role in adapting AI behavior to match human skill levels. In a previous state-of-the-art study, researchers have proposed a strength estimator trained using human players' match data. Given some matches, the strength estimator computes strength scores and uses them to estimate player ranks (skill levels). In this paper, we focus on the observation that human players' behavior tendency varies according to their strength and aim to improve the accuracy of strength estimation by taking this into account. Specifically, in addition to strength scores, we obtain policies for different skill levels from neural networks trained using human players' match data. We then combine features based on these policies with the strength scores to estimate strength. We conducted experiments on Go and chess. For Go, our method achieved an accuracy of 80% in strength estimation when given 10 matches, which increased to 92% when given 20 matches. In comparison, the previous state-of-the-art method had an accuracy of 71% with 10 matches and 84% with 20 matches, demonstrating improvements of 8-9%. We observed similar improvements in chess. These results contribute to developing a more accurate strength estimation method and to improving human-AI interaction.

Despite the growing interest in collaborative AI, designing systems that seamlessly integrate human input remains a major challenge. In this study, we developed a task to systematically examine human preferences for collaborative agents. We created and evaluated five collaborative AI agents with strategies that differ in the manner and degree they adapt to human actions. Participants interacted with a subset of these agents, evaluated their perceived traits, and selected their preferred agent. We used a Bayesian model to understand how agents' strategies influence the Human-AI team performance, AI's perceived traits, and the factors shaping human-preferences in pairwise agent comparisons. Our results show that agents who are more considerate of human actions are preferred over purely performance-maximizing agents. Moreover, we show that such human-centric design can improve the likability of AI collaborators without reducing performance. We find evidence for inequality-aversion effects being a driver of human choices, suggesting that people prefer collaborative agents which allow them to meaningfully contribute to the team. Taken together, these findings demonstrate how collaboration with AI can benefit from development efforts which include both subjective and objective metrics.

In the future, Microsoft suggests, you may be playing AI. No, not on the battlefield, but on games that actually use AI to simulate the entire game itself. As a first step, Microsoft has developed an AI model, called WHAM, that "beta tests" games early in the development cycle using AI instead of human players. Gamers know that realistic AI can turn a good game into something great, like how the older F.E.A.R. games would realistically model how soldiers might react to a hostile, armed player. Microsoft's World and Human Action Model (WHAM) takes the opposite approach -- it tries to figure out how human players will react in a given situation, right down to a specific frame or setup within the existing game world.

Achieving human-AI alignment in complex multi-agent games is crucial for creating trustworthy AI agents that enhance gameplay. We propose a method to evaluate this alignment using an interpretable task-sets framework, focusing on high-level behavioral tasks instead of low-level policies. Our approach has three components. First, we analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task space. This task space serves as a basis set for a behavior manifold capturing interpretable axes: fight-flight, explore-exploit, and solo-multi-agent. Second, we train an AI agent to play Bleeding Edge using a Generative Pretrained Causal Transformer and measure its behavior. Third, we project human and AI gameplay to the proposed behavior manifold to compare and contrast. This allows us to interpret differences in policy as higher-level behavioral concepts, e.g., we find that while human players exhibit variability in fight-flight and explore-exploit behavior, AI players tend towards uniformity. Furthermore, AI agents predominantly engage in solo play, while humans often engage in cooperative and competitive multi-agent patterns. These stark differences underscore the need for interpretable evaluation, design, and integration of AI in human-aligned applications. Our study advances the alignment discussion in AI and especially generative AI research, offering a measurable framework for interpretable human-agent alignment in multiplayer gaming.

AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination. LLM-powered agents typically require invoking LLM APIs and employing artificially designed complex prompts, which results in high inference latency. While this paradigm works well in scenarios with minimal interactive demands, such as code generation, it is unsuitable for highly interactive and real-time applications, such as gaming. Traditional gaming AI often employs small models or reactive policies, enabling fast inference but offering limited task completion and interaction abilities. In this work, we consider Overcooked as our testbed where players could communicate with natural language and cooperate to serve orders. We propose a Hierarchical Language Agent (HLA) for human-AI coordination that provides both strong reasoning abilities while keeping real-time execution. In particular, HLA adopts a hierarchical framework and comprises three modules: a proficient LLM, referred to as Slow Mind, for intention reasoning and language interaction, a lightweight LLM, referred to as Fast Mind, for generating macro actions, and a reactive policy, referred to as Executor, for transforming macro actions into atomic actions. Human studies show that HLA outperforms other baseline agents, including slow-mind-only agents and fast-mind-only agents, with stronger cooperation abilities, faster responses, and more consistent language communications.

The term "AI" has been used in video games since their inception, but it rarely means true artificial intelligence. Instead, it's a generic term to describe a preprogrammed opponent or character that feigns intelligence but is really just following a narrow set of instructions. This is slowly changing, though -- and the people who build video games are helping out. At GDC today, EA announced that it's been training AI agents in 2016's WWI shooter Battlefield 1.The company says it's the first time this sort of work has been done in a high-budget AAA title (which is disputable), but more importantly, it says the methods it's developing will help improve future games: providing tougher, more realistic enemies for human players and giving developers new ways to debug their software. EA's AI agents -- which, unlike bots, are expected to learn how to play instead of merely following instructions -- are being trained using a combination of two standard methods: imitation learning and reinforcement learning.

Below are three alternatives to Kaggle which I've become interested in: A fourth competition does exist -- HALITE by Two Sigma-- however, HALITE appears to have been discontinued…you're welcome to check it out on your own; only Battlecode, Terminal, and Lux are summarized below. Battle Code has been around since 2003; the description below is taken directly from the site. Battlecode is a real-time strategy game, for which you will write an AI player. Your AI player will need to strategically manage a robot army and control how your robots work together to defeat the enemy team. As a contestant, you will learn to use artificial intelligence, pathfinding, distributed algorithms, and communications to make your player as competitive as possible.

The Biden organization intends to use federal funding for U.S. research and development on artificial intelligence (AI) and other cutting-edge technologies. The United States and India are logical partners in diagramming the future development of AI, which guarantees economic growth and social benefits to the two nations in key areas like healthcare, education, energy, financial technology and retail. India is an all-around nation and set to be a fundamental part of these endeavors, as the world's biggest democracy, a vital supporter for the developing world, and the home of a huge informational technology (IT) sector effectively collaborated with the United States. Recently, the Department of Science and Technology announced that the Indo-US Science and Technology Forum-IUSSTF has introduced the US India Artificial Intelligence Initiative. It will concentrate on AI implementation in the significant areas that are priorities for the two countries. India-US partnership in the field of Science and Technology is exceptionally an old collaboration.

In this column, we recount the history of EAAI (Educational Advances in Artificial Intelligence) mentored undergraduate research challenges from 2014 through the present and share a vision of how such offerings may become more diverse and engage a broader range of faculty mentors and undergraduate researchers. Unlike many academic disciplines, Computer Science undergraduate majors currently are not usually required to take or even offered a research methods course. Even so, many graduate schools desire to admit graduate students with undergraduate research experience. The EAAI Symposium has historically affirmed the value of mentored undergraduate research as an important part of undergraduate AI education. It has expressed this value through the support of a number of mentored undergraduate research challenges, described below.