This paper describes entries to the third Playable Experiences track to be held at the AIIDE conference. We discuss the five entries accepted to the track for 2015, as well as the ongoing development of the track as part of AIIDE.

Creating reasonable AI for sidekicks in games has proven to be a difficult challenge synthetizing player modelling and cooperative planning, both being problems hard by themselves. In this paper, we experiment with designing around these problems: we propose a cooperative puzzle-platformer game that was designed to look similarly to the mainstream of the genre, but to allow for an easy implementation of a quality sidekick AI, letting us test player reactions to the AI. The game was designed so that it is easy for the AI to find optimal solutions while the problem is relatively hard for a human player. We gathered survey responses from players who played the game online (N=28). While the AI sidekick was reported as likeable and helpful, players still reported greater enjoyment of the game when they were allowed to control the sidekick themselves. These findings indicate that the AI itself is not the only obstacle to truly enjoyable gameplay with an AI sidekick.

With more computing power available, video games may spare increasing amounts of processing time for AI. One prospective application of the newly available resources is the simulation of large amounts of non-player characters (NPCs) in open world games. While it is relatively easy to simulate simple behaviours of individual NPCs it is much more difficult to create meaningful interactions between the NPCs. However, without interaction, the world cannot look very alive. In this paper we present a technique that enriches the NPC simulation with pre-scripted situations - short sketches involving coordinated interaction between several NPCs that do not substantially alter the state of the game world but increase the appeal of the world to the player. We use constraint satisfaction techniques to find NPCs suitable to enact the situations at runtime. We have implemented situations on top of the AI system for an upcoming AAA open-world game and show that this approach satisfies functional and computational requirements for practical deployment in the final version of the game.

In recent years, computer games have reached unprecedented level of graphical fidelity to the real world. As the non-player characters (NPCs) in the game world look more and more realistic, players expect them to manifest believable behavior as well. This is accented especially in games that feature large open worlds, which players may explore freely and it is thus not possible to explicitly account for all possible player interactions. In this paper we focus mainly on ambient AI - the logic behind day to day behaviors of NPCs as they sleep, work and entertain themselves in the virtual world. In this context, it is of great importance to build a system that handles many NPCs (up to several hundreds) quickly. In this paper we report on an implementation of a particular AI system that was approved for deployment in an upcoming high-budget game. The system features a hierarchy of control similar to the subsumption architecture and a visual agent-based language inspired by behavior trees. We describe the challenges involved in building such a system and specific design decisions we have made that let us achieve a level of behavioral fidelity unmatched by existing games. Finally we evaluate the performance of the system in a realistic setting.

Recent development in game AI has seen action planning and its derivates being adapted for controlling agents in classical types of games, such as FPSs or RPGs. Complementary, one can seek new types of gameplay elements inspired by planning. We propose and formally define a new game "genre" called anticipation games and demonstrate that planning can be used as their key concept both at design time and run time. In an anticipation game, a human player observes a computer controlled agent or agents, tries to predict their actions and indirectly helps them to achieve their goal. The paper describes an example prototype of an anticipation game we developed. The player helps a burglar steal an artifact from a museum guarded by guard agents. The burglar has incomplete knowledge of the environment and his plan will contain pitfalls. The player has to identify these pitfalls by observing burglar's behavior and change the environment so that the burglar replans and avoids the pitfalls. The game prototype is evaluated in a small-scale human-subject study, which suggests that the anticipation game concept is promising.