abstract- Various research studies, with contradictory results, indicate that tempo synchronization affects player performance differently depending on whether it is aligned with gameplay or player actions. To test this hypothesis, this demo presents a customized 3D video game with a dynamic audio system, allowing separate synchronization with game states or player decisions. Empirical tests have shown that the audio is well synchronized with in-game events. Moreover, the created game is intended to offer an immersive setting suitable for examining the diverse effects of musical alignment on player abilities.

We are interested in realistic planning problems to model the behavior of Non-Playable Characters (NPCs) in video games. Search-based action planning, introduced by the game F.E.A.R. in 2005, has an exponential time complexity allowing to control only a dozen NPCs between two frames. A close study of the plans generated in first-person shooters shows that: (1) actions are unary, (2) actions are contextually post-unique and (3) there is no two instances of the same action in an NPC’s plan. By considering (1), (2) and (3) as restrictions, we introduce new classes of problems with the Simplified Action Structure formalism which indeed allow to model realistic problems and whose instances are solvable by a linear-time algorithm. We also experimentally show that our algorithm is capable of managing millions of NPCs per frame.

As music tempo can influence human actions pace, we use tempi variations to vary the difficulty in gaming situations based on the synchronization of events, like actions on moving objects in arcade games or waves of enemies in FPS. In this work, musical tempi are exploited to hinder or help Tetris players. Over the first phase of this work involving 44 players and more than 230 Tetris games we discovered surprising interactions between different tempo characteristics influencing the player’s performance. The positive or negative effects of specific tempi settings we discovered were validated in a second phase involving 19 players and 50 Tetris games. Once the effect was chosen, it was dynamically triggered according to certain conditions that were validated during the first phase of this work. Results show that a transition from a staircase increase to a more gradual increase in tempo significantly hinders Tetris players when both tempi are synchronous with the gameplay whereas the same transition help players when both tempi are not synchronized with game actions. Our approach provides new and valuable insight to varying video-game difficulty when gaming situations ask the player to increasingly synchronize with the pace of the game.

Game Artificial Intelligence Engines of commercial games run on CPUs and not on GPUs. With more and more powerful GPUs and Cloud gaming, our vision is that GPUs will become the dedicated Game AI hardware, just as it now provides computing power for Game Physics (e.g. nVidia PhysX). In particular, we believe GPUs can run Game AI Planning, which computes plans in order to control the behaviors of Non-Player Characters (NPCs) in video-games. Our objective is an efficient online GPU-based Game AI Planning component with Cloud gaming as a target. We here report on our most recent implementation which can control thousands of NPCs each frame with only one GTX 1080 on the AI server, pushing thousands of plans to the client (PC or console).

It has been shown that music influences human behavior and that it can influence the sports endurance or cognitive performance (memory, reflexes) of individuals. However, little research exists on its influence on the performance of video game players. Music is used to create a particular atmosphere in a game but can it be used to change the difficulty of the game? In this paper, we examine the effects of musical tempo on the performance of players in the Tetris game. By experimenting with different tempo settings for the same music -perfectly synchronous with the game, gradually accelerating to prepare for speed steps, slightly out of sync or in total opposition to the tension of the game- we have shown a significant influence of tempo on the performance of novice Tetris players and even more so for those who have a neutral evaluation of the music. By increasing the tension of the game, a synchronous and increasing tempo can increase the player's stress and degrade his performance, while a tempo that is out of sync with the actions of the game or slowed down regularly can defuse this stress.

In this demo paper, we present Sonotris, a Tetris game with a dynamic soundtrack specifically designed to experiment with the influence of musical tempo on player performance. The different stages of the game use music whose tempo is either perfectly synchronous with the game, or accelerates progressively to prepare the speed steps, or is slightly out of sync, or in total opposition to the tension of the game. Our first tests on a group of 41 players showed a significant influence of tempo on the performance of novice players in Tetris and even more for those with a neutral evaluation of the music.

In this paper, we propose a comprehensive study of second-order consistencies (i.e., consistencies identifying inconsistent pairs of values) for constraint satisfaction. We build a full picture of the relationships existing between four basic second-order consistencies, namely path consistency (PC), 3-consistency (3C), dual consistency (DC) and 2-singleton arc consistency (2SAC), as well as their conservative and strong variants. Interestingly, dual consistency is an original property that can be established by using the outcome of the enforcement of generalized arc consistency (GAC), which makes it rather easy to obtain since constraint solvers typically maintain GAC during search. On binary constraint networks, DC is equivalent to PC, but its restriction to existing constraints, called conservative dual consistency (CDC), is strictly stronger than traditional conservative consistencies derived from path consistency, namely partial path consistency (PPC) and conservative path consistency (CPC). After introducing a general algorithm to enforce strong (C)DC, we present the results of an experimentation over a wide range of benchmarks that demonstrate the interest of (conservative) dual consistency. In particular, we show that enforcing (C)DC before search clearly improves the performance of MAC (the algorithm that maintains GAC during search) on several binary and non-binary structured problems.