Summerville, Adam (California State Polytechnic University, Pomona ) | Martens, Chris (North Carolina State University) | Samuel, Ben (University of New Orleans) | Osborn, Joseph (Pomona College) | Wardrip-Fruin, Noah (University of California, Santa Cruz) | Mateas, Michael (University of California, Santa Cruz)
Current approaches to game generation don't understand the games they generate. As a result, even the most sophisticated systems in this regard, e.g., Game-o-Matic, betray this problem — generating games with goals that are at odds with their mechanics. We describe Gemini, the first bidirectional game generation and analysis system. Gemini is able to take games as input, perform a proceduralist reading of them, and produce possible interpretations that the games might afford. By utilizing the declarative nature of Answer Set Programming (ASP), this analysis pathway opens up generation of games targeting specific interpretations and makes it possible to ensure the generated games are consistent with the desired reading. For Gemini, we developed a game specification language capable of expressing a larger domain of games than is possible with VGDL, the most widespread representation. We demonstrate the generality of our approach by generating games in a series of domains. These domains are based on prototypes hand-created by a team without knowledge of the constraints and capabilities of Gemini.
This report presents a tool developed for the analysis and visualisation of Rolling Horizon Evolutionary Algorithms, featuring a GUI which allows integration within the General Video Game AI Framework. Users are able to easily customize the parameters of the agent between runs and observe an in-depth analysis of its performance through various visual information extracted from gameplay data, live while playing the game. This visualisation aims to inform a deeper analysis into algorithm behaviour, in an attempt to justify why they make the decisions they do and improve their performance based on this knowledge.
Modelling a player's understanding of NPC movements can be useful for adapting gameplay to different play styles. For stealth games, what a player knows or suspects of enemy movements is important to how they will navigate towards a solution. In this work, we build a uniform abstraction of potential player path knowledge based on their partial observations. We use this representation to compute different path estimates according to different player expectations. We augment our work with a user study that validates what kinds of NPC behaviour a player may expect, and develop a tool that can build and explore appropriate (expected) paths. We find that players prefer short simple paths over long or complex paths with looping or backtracking behaviour.
3D construction sandbox games such as Minecraft have provided new opportunities for people to express their creativity. However, individual players have few tools to help them learn about architectural style or how to improve the structure they are building. Ideally, players could utilize tools that capitalize on the large numbers of 3D models built by others to offer guidance for their particular project. We trained a neural network to classify a large collection of Minecraft models from various websites in terms of style (Ancient, Asian, Medieval, or Modern). We present experimental results demonstrating that our model can classify the user-indicated style of a structure with 55% accuracy. We further demonstrate use of this model to highlight nearest neighbors to a specific query structure. We have integrated these tools into a Minecraft Mod that allows players to classify their structure's style and view nearest neighbors in real-time.
Wiggins, Joseph B. (University of Florida) | Kulkarni, Mayank (University of Florida) | Min, Wookhee (North Carolina State University) | Mott, Bradford (North Carolina State University) | Boyer, Kristy Elizabeth (University of Florida) | Wiebe, Eric (North Carolina State University) | Lester, James (North Carolina State University)
Player affect is a central consideration in the design of game-based learning environments. Affective indicators such as facial expressions exhibited during gameplay may support building more robust player models and adaptation modules. In game-based learning, predicting player mental demand and engagement from player affect is a particularly promising approach to helping create more effective gameplay. This paper reports on a predictive player-modeling approach that observes player affect during early interactions with a game-based learning environment and predicts selfreports of mental demand and engagement at the conclusion of gameplay sessions. The findings show that automatically detected facial expressions such as those associated with joy, disgust, sadness, and surprise are significant predictors of players' self-reported engagement and mental demand at the end of gameplay interactions. The results suggest that it is possible to create affect-based predictive player models that can enable proactively tailored gameplay by anticipating player mental demand and engagement.
In this paper we tackle a problem of tile-based combat in the turn-based strategy (space 4X) video game Children of the Galaxy (CotG). We propose an improved version of Monte Carlo tree search (MCTS) called MCTS considering hit points (MCTS HP). We show MCTS HP is superior to Portfolio greedy search (PGS), MCTS and NOKAV reactive agent in small to medium combat scenarios. MCTS HP performance is shown to be stable when compared to PGS, while it is also more time-efficient than regular MCTS. In smaller scenarios, the performance of MCTS HP with 100 millisecond time limit is comparable to MCTS with 2 seconds time limit. This fact is crucial for CotG as the combat outcome assessment is precursor to many strategical decisions in CotG game. Finally, if we fix the amount of search time given to the combat agent, we show that different techniques dominate different scales of combat situations. As the result, if searchbased techniques are to be deployed in commercial products, a combat agent will need to be implemented with portfolio of techniques it can choose from given the complexity of situation it is dealing with to smooth gameplay experience for human players.
Authoring in the context of Interactive Storytelling (IS) is inherently difficult, and there is a need for authoring tools that both enable and assist authors in the creation of new content. In this paper, we discuss our approach for creating an AI-assisted authoring tool via the concept of mixed-initiative systems. We introduce our tool, Mimisbrunnur, which uses this concept to assist authors in the creation of story content. We explain how the tool functions and introduce its fundamental components, including Natural Language Processing, a Suggestion Generator, and three authoring modules.
First we compare the believability of agent plans taken from the spaces of valid classical plans, intentional plans, and belief plans. We show that the plans that make the most sense to humans are those in the overlapping regions of the intentionality and belief spaces. Second, we validate the model's approach to representing anticipation, where characters form plans that involve actions they expect other characters to take. Using a short interactive scenario we demonstrate that players not only find it believable when NPCs anticipate their actions, but sometimes actively anticipate the actions of NPCs in a way that is consistent with the model.
My research aims to contribute to research in the narrative authoring domain by using cognitive models in narrative plan generation. These cognitive models determine how actions and events in narrative affect the audience. My research intends to leverage these models in narrative planning and use them to provide intelligent narrative plans that are structured to invoke specific responses from audiences when they experience the narrative. This sort of approach would greatly benefit the enrich growing set of variables of narrative planning. My research is in the nascent field of the computational modeling of narrative, work that seeks to enable computerassisted authoring of stories by modeling the cognitive processes of both author and audience. I intend to extend work on narrative generation that uses planning algorithms to create stories that are consistent and complete (Young 2007). Previous work in narrative planning has been effective at borrowing policy planning and state-space search algorithms from AI in order to generate plot (Riedl and Young 2014). However, the majority of this work focuses on structural properties of a story (e.g., causal consistency (Li et al. 2012), intentionality (Riedl and Young 2010), conflict between characters (Ware et al. 2014)) but does not address the impact that the story has on the cognitive and affective response of its audience (e.g., tension, suspense). The goal of my work is to leverage models of author and audience to address these types of limitations.