Complexity Science Hub, Metternichgasse 8, 1030, Vienna, Austria Strategic decision-making involves managing the tension between immediate opportunities and long-term objectives. We study this trade-off in chess by characterizing and comparing dynamics between human vs. human and AI vs. AI games. We propose a network-based metric of piece-to-piece interaction to quantify the ongoing strategic tension on the board. Its evolution in games reveals that the most competitive AI players sustain higher levels of strategic tension for longer durations than elite human players. Cumulative tension varies with algorithmic complexity for AI and correspondingly in human-played games increases abruptly with expertise at about 1600 Elo and again at 2300 Elo. The profiles reveal different approaches. Highly competitive AI tolerates interconnected positions balanced between offensive and defensive tactics over long periods. Human play, in contrast, limits tension and game complexity, which may reflect cognitive limitations and adaptive strategies. The difference may have implications for AI usage in complex, strategic environments. The aphorism that one may have won the battle but lost the war is encapsulated in the notion of a "Pyrrhic victory." Costly short-term wins must be balanced against the longer-term uncertainties, opportunities, or challenges that may emerge in competitive environments.

Way back in 1985, a team of researchers at Carnegie Mellon University developed a computer purely to play games of chess. After moving to IBM, the computer was further developed, culminating in the obvious test – a match against then-world champion Garry Kasparov. However, the computer known as Deep Blue at this point wasn't enough for Kasparov; it lost four games to two. But like any good underdog, the computer was down but not out. It came back a year later to beat Kasparov in a narrow victory, winning by a single game.

As artificial intelligence continues its rapid progress, equaling or surpassing human performance on benchmarks in an increasing range of tasks, researchers in the field are directing more effort to the interaction between humans and AI in domains where both are active. Chess stands as a model system for studying how people can collaborate with AI, or learn from AI, just as chess has served as a leading indicator of many central questions in AI throughout the field's history. AI-powered chess engines have consistently bested human players since 2005, and the chess world has undergone further shifts since then, such as the introduction of the heuristics-based Stockfish engine in 2008 and the deep reinforcement learning-based AlphaZero engine in 2017. The impact of this evolution has been monumental: chess is now seeing record numbers of people playing the game even as AI itself continues to get better at playing. These shifts have created a unique testbed for studying the interactions between humans and AI: formidable AI chess-playing ability combined with a large, growing human interest in the game has resulted in a wide variety of playing styles and player skill levels.

Automated game design is the problem of automatically producing games through computational processes. Traditionally these methods have relied on the authoring of search spaces by a designer, defining the space of all possible games for the system to author. In this paper we instead learn representations of existing games and use these to approximate a search space of novel games. In a human subject study we demonstrate that these novel games are indistinguishable from human games for certain measures.

The South Korean Go champion Lee Se-dol has retired from professional play, telling Yonhap news agency that his decision was motivated by the ascendancy of AI. "With the debut of AI in Go games, I've realized that I'm not at the top even if I become the number one through frantic efforts," Lee told Yonhap. "Even if I become the number one, there is an entity that cannot be defeated." For years, Go was considered beyond the reach of even the most sophisticated computer programs. The ancient board game is famously complex, with more possible configurations for pieces than atoms in the observable universe. This reputation took a knock in 2016 when the Google-owned artificial intelligence company DeepMind shocked the world by defeating Se-dol four matches to one with its AlphaGo AI system.

This is more of an opinion piece. This is a topic that pops up time and again – we should modify the Brood War API to do things in a certain way, so bots will behave more like humans! Bots are having unfair advantages! Let me provide an example: the handling of invisible units. A bot can see an invisible unit if it moves, or attacks.

Games have always been a popular domain of AI research, and they have been used for many recent competitions. However, reaching human-level performance often either focuses on comprehensive world knowledge or solving decision-making problems with unmanageable solution spaces. Building on the popular Taboo board game, the Taboo Challenge Competition addresses a different problem — that of bridging the gap between the domain knowledge of heterogeneous agents trying to jointly identify a concept without making reference to its most salient features. The competition, which was run for the first time at IJCAI 2017, aims to provide a simple testbed for diversity-aware AI where the focus is on integrating independently engineered AI components, while offering a scenario that is challenging yet simple enough to not require mastering general commonsense knowledge or natural language understanding. We describe the design and preparation of the competition, discuss results, and lessons learned.