Drake, John
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Publication Planning For Non-Player Characters By Learning From Demonstration(2018-01-01) Drake, JohnIn video games, state of the art non-player character (NPC) behavior generation typically depends on hard-coding NPC actions. In many game situations however, it is hard to foresee how an NPC should behave to appear intelligent or to accommodate human preferences for NPC behavior. We advocate the creation of a more flexible method to allow players (and developers) to train NPCs to execute novel behaviors which are not hard-coded. In particular, we investigate search-based planning approaches using demonstration to guide the search through high-dimensional spaces that represent the full state of the game. To this end, we developed the Training Graph heuristic, an extension of the Experience Graph heuristic, that guides a search smoothly and effectively even when a demonstration is unreachable in the search space, and ensures that more of the demonstrations are utilized to better train the NPC's behavior. To deal with variance in the initial conditions of such planning problems, we have developed heuristics in the Multi-Heuristic A* framework to adapt demonstration trace data to new problems. We evaluate our approach in the Creation Engine game engine by modifying The Elder Scrolls V: Skyrim (Skyrim) to accommodate our NPC behavior generators and experiments. In Skyrim, players are given "quests" which are composed of several objectives. NPCs in the game sometimes accompany the player on quests, but state-of-the-art companion NPC AI is not sophisticated enough to behave according to arbitrary player desires. We hope that our work will lead to the creation of trainable NPC AI. This will enable novel gameplay mechanics for video game players and may augment video game production by allowing developers to train NPCs instead of hard-coding complex behaviors.Publication CRAM It! A Comparison of Virtual, Live-Action and Written Training Systems for Preparing Personnel to Work in Hazardous Environments(2011-01-01) Stocker, Catherine; Sunshine-Hill, Ben; Drake, John; Kider, Joseph T; Badler, Norman I; Perera, IanIn this paper we investigate the utility of an interactive, desktopbased virtual reality (VR) system for training personnel in hazardous working environments. Employing a novel software model, CRAM (Course Resource with Active Materials), we asked participants to learn a specific aircraft maintenance task. The evaluation sought to identify the type of familiarization training that would be most useful prior to hands on training, as well as after, as skill maintenance. We found that participants develop an increased awareness of hazards when training with stimulating technology – in particular (1) interactive, virtual simulations and (2) videos of an instructor demonstrating a task – versus simply studying (3) a set of written instructions. The results also indicate participants desire to train with these technologies over the standard written instructions. Finally, demographic data collected during the evaluation elucidates future directions for VR systems to develop a more robust and stimulating hazard training environment.Publication Egress Online: Towards Leveraging Massively, Multiplayer Environments for Evacuation Studies(2012-01-01) Normoyle, Aline; Drake, John; Safonova, AllaLarge datasets of real human behaviors are of huge benefit across numerous domains, including evacuation safety, urban planning, marketing, and ergonomics. However, because large-scale experiments involving real human subjects are expensive and prohibitively difficult to organize, such datasets are scarce. Thus in this paper, we propose the use of massively multiplayer online (MMO) communities as an inexpensive and innovative way to capture datasets of large numbers of people under different conditions. We describe our implementation of an online data collection system, based on games, inside the popular massively multiplayer, online environment of Second Life. We evaluate the use of this system for performing evacuation experiments using a mix of Second Life residents and players recruited on campus. Our system was able to draw online participants, support data collection needs, and provide potential insights into high-level evacuation behaviors such as the choices of exit, effects of building debris, and the use-patterns of a building. Through experiments performed using our system, we found that Second Life residents found the game controls and environment to be significantly more compelling than lab participants; that players unfamiliar with our office building tended to evacuate primarily via the front entrance; and that in-game debris significantly increased the numbers of participants who failed to exit a building safely.Publication Game-Based Data Capture for Player Metrics(2012-10-01) Normoyle, Aline; Drake, John; Safonova, Alla; Likhachev, MaximPlayer metrics are an invaluable resource for game designers and QA analysts who wish to understand players, monitor and improve game play, and test design hypotheses. Usually such metrics are collected in a straightforward manner by passively recording players; however, such an approach has several potential drawbacks. First, passive recording might fail to record metrics which correspond to an infrequent player behavior. Secondly, passive recording can be a costly, laborious, and memory intensive process, even with the aid of tools. In this paper, we explore the potential for an active approach to player metric collection which strives to collect data more efficiently, and thus with less cost. We use an online, iterative approach which models the relationship between player metrics and in-game situations probabilistically using a Markov Decision Process (MDP) and solves it for the best game configurations to run. To analyze the benefits and limitations of this approach, we implemented a system, called GAMELAB, for recording player metrics in Second Life.