With the continuous evolution and refinement of LLMs, they are endowed with impressive logical reasoning or vertical thinking capabilities. But can they think out of the box? Do they possess proficient lateral thinking abilities? Following the setup of Lateral Thinking Puzzles, we propose a novel evaluation benchmark, LatEval, which assesses the model's lateral thinking within an interactive framework. In our benchmark, we challenge LLMs with 2 aspects: the quality of questions posed by the model and the model's capability to integrate information for problem-solving. We find that nearly all LLMs struggle with employing lateral thinking during interactions. For example, even the most advanced model, GPT-4, exhibits the advantage to some extent, yet still maintain a noticeable gap when compared to human. This evaluation benchmark provides LLMs with a highly challenging and distinctive task that is crucial to an effective AI assistant.

A Times journalist on location in Qatar photographs the match, capturing images in high-speed bursts, trying to anticipate important moments with a broad view of the field. In collaboration with The Times's newsroom in the U.S., they identify a key moment in the match and transmit the photograph of that moment. From that photo, we calculate the 3D position of the photographer's camera. We can do so mathematically using the standard size of the goal and penalty area as geometric guides. Once we know the camera position, we project the image onto 3D geometry that represents the field and stands where the game was played.

Data is becoming an important central point for making design decisions for most software. Game development is not an exception. As data-driven methods and systems start to populate these environments, a good question is: can we make models developed from this data transparent to users? In this paper, we synthesize existing work from the Intelligent User Interface and Learning Science research communities, where they started to investigate the potential of making such data and models available to users. We then present a new area exploring this question, which we call Open Player Modeling, as an emerging research area. We define the design space of Open Player Models and present exciting open problems that the games research community can explore. We conclude the paper with a case study and discuss the potential value of this approach.

This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge about the environment, the difference in each player's style and preferences represents a challenge when trying to make an intelligent system mimic their actions. Based on observations from players interactions with an extract from the interactive fiction Anchorhead, we created a player profile to guide the behaviour of a generic player model based on the BDI (Belief-Desire-Intention) model of agency. We evaluated our approach using qualitative and quantitative methods and found that the player profile can improve the performance of the BDI player model. However, we found that players self-assessment did not yield accurate data to populate their player profile under our current approach.

We've seen strong efforts from almost every signature franchise in 2016. The success and quality of the products have raised the anticipation for next year's crop of releases, and it's also caused the sports gaming community to become even more demanding. While the thirst for sports gaming titles shows no sign of tailing off, the fire that burns in our guts for the best products available is still alive. After spending countless hours with just about every sports game released in 2016, I've selected the standout games and modes in a variety of categories. All three of these games delivered ultra-realistic renders, though the dynamics and structure of their sports create a diverse circumstance. Most often, player faces aren't visible in an American football game, but EA Sports' Madden 17 had as many scanned-in head models as the series has ever had.

Around the same time, a system called Player-Specific From Shakespeare's "Romeo and Juliet" to George Lucas' Stories via Automatically Generated Events (PaSSAGE) "Star Wars" to BioWare's "Jade Empire" to campfire stories (Thue et al. 2007) was proposed, which used AI techniques to baseball commentary, story-telling is a fundamental to model the player as he/she experiences a narrative-rich part of entertainment. A strong narrative resonates with our video game. Such a continuously updated player model was minds, hearts and souls and keeps us engaged. We remember used to dynamically adapt the story, tailoring it to the current the stories of our childhood and retell them to our own player. Unlike, ASD, PaSSAGE did not have any automation children. Story-telling has delighted and saddened the human at the design stage and relied on a human designer to race since the beginning of time and shows no signs of foresee all possible ways of a player breaking the story and slowing down. But can it be improved with technology?

Many computer games of all genres pit the player against a succession of increasingly difficult challenges such as combat with computer-controlled enemies and puzzles. Part of the fun of computer games is to master the skills necessary to complete the game. Challenge tailoring is the problem of matching the difficulty of skill-based events over the course of a game to a specific player's abilities. We present a tensor factorization approach to predicting player performance in skill-based computer games. Our tensor factorization approach is data-driven and can predict changes in players' skill mastery over time, allowing more accurate tailoring of challenges. We demonstrate the efficacy and scalability of tensor factorization models through an empirical study of human players in a simple role-playing combat game. We further find a significant correlation between these performance ratings and player subjective experiences of difficulty and discuss ways our model can be used to optimize player enjoyment.

In this paper, we present the Procedural Game Adaptation (PGA) framework: a designer-controlled way to adapt the Changing the dynamics of a video game (i.e., how the dynamics of a given video game during end-user play. When player's actions affect the game world) is a fundamental tool implemented, this framework produces a deterministic, online of video game design. In Pac-Man, eating a power pill allows adaptation agent (called an experience manager (Riedl the player to temporarily defeat the ghosts that pursue et al. 2011)) that automatically performs two tasks: 1) it and threaten her for the vast majority of the game; in Call gathers information about a game's current player, 2) it of Duty 4, taking the perk called "Deep Impact" allows the uses that information to estimate which of several different player's bullets to pass through certain walls without being changes to the game's dynamics will maximize some playerspecific stopped. The parameters of such changes (e.g., how much value (e.g., fun, sense of influence, etc.). the ghosts slow down while vulnerable) are usually determined by the game's designers long before its release, with

My research involves creating and evaluating adaptive gameenvironments using player models created using data-driventechniques and algorithms. I hypothesize that I will be able tochange parts of a game to elicit certain behaviors from players,and that these changes will also result in an increase ofengagement and/or intrinsic motivation.

Agency, being one's ability to perform an action and have some influence over the world, is fundamental to interactive entertainment. Although much of the games industry is concerned with providing more agency to its players, what seems to matter more is how much agency each player will actually perceive. In this paper, we present a computational model of this phenomena, based on the notion that the amount of agency that one perceives depends on how much they desire the outcomes that result from their decisions. Using a structure for high-agency stories that we designed specifically for this intent, we present the results of a 141-participant user study that tests our model's ability to select subsequent events in an original interactive story. Using a newly validated survey instrument for measuring both agency and fun, we found with a high degree of confidence that event sequences selected by our model result in players perceiving more agency than players who experience event sequences that our model does not recommend.