A Mixed Method Approach for Evaluating and Improving the Design of Learning in Puzzle Games

Despite the acknowledgment that learning is a necessary part of all gameplay, the area of Games User Research lacks an established evidence based method through which designers and researchers can understand, assess, and improve how commercial games teach players game-specific skills and information. In this paper, we propose a mixed method procedure that draws together both quantitative and experiential approaches to examine the extent to which players are supported in learning about the game world and mechanics. We demonstrate the method through presenting a case study of the game Portal involving 14 participants, who differed in terms of their gaming expertise. By comparing optimum solutions to puzzles against observed player performance, we illustrate how the method can indicate particular problems with how learning is structured within a game. We argue that the method can highlight where major breakdowns occur and yield design insights that can improve the player experience with puzzle games

Heuristic Evaluation for Gameful Design

© Lennart Nacke, 2016. This is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in CHI PLAY Companion '16 Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, https://doi.org/10.1145/2968120.2987729Despite the emergence of many gameful design methods in the literature, there is a lack of evaluation methods specific to gameful design. To address this gap, we present a new set of guidelines for heuristic evaluation of gameful design in interactive systems. First, we review several gameful design methods to identify the dimensions of motivational affordances most often employed. Then, we present a set of 28 gamification heuristics aimed at enabling experts to rapidly evaluate a gameful system. The resulting heuristics are a new method to evaluate user experience in gameful interactive systemsNatural Sciences and Engineering Research Council of Canada Social Sciences and Humanities Research Council of CanadaPeer-reviewe

Efficacy and safety of givosiran for acute hepatic porphyria: 24-month interim analysis of the randomized phase 3 ENVISION study

Background & Aims Upregulation of hepatic delta-aminolevulinic acid synthase 1 with accumulation of potentially toxic heme precursors delta-aminolevulinic acid and porphobilinogen is fundamental to the pathogenesis of acute hepatic porphyria. Aims: evaluate long-term efficacy and safety of givosiran in acute hepatic porphyria. Methods Interim analysis of ongoing ENVISION study (NCT03338816), after all active patients completed their Month 24 visit. Patients with acute hepatic porphyria (≥12 years) with recurrent attacks received givosiran (2.5 mg/kg monthly) (n=48) or placebo (n=46) for 6 months (double-blind period); 93 received givosiran (2.5 mg or 1.25 mg/kg monthly) in the open-label extension (continuous givosiran, n=47/48; placebo crossover, n=46/46). Endpoints included annualized attack rate, urinary delta-aminolevulinic acid and porphobilinogen levels, hemin use, daily worst pain, quality of life, and adverse events. Results Patients receiving continuous givosiran had sustained annualized attack rate reduction (median 1.0 in double-blind period, 0.0 in open-label extension); in placebo crossover patients, median annualized attack rate decreased from 10.7 to 1.4. Median annualized days of hemin use were 0.0 (double-blind period) and 0.0 (open-label extension) for continuous givosiran patients and reduced from 14.98 to 0.71 for placebo crossover patients. Long-term givosiran led to sustained lowering of delta-aminolevulinic acid and porphobilinogen and improvements in daily worst pain and quality of life. Safety findings were consistent with the double-blind period. Conclusions Long-term givosiran has an acceptable safety profile and significantly benefits acute hepatic porphyria patients with recurrent attacks by reducing attack frequency, hemin use, and severity of daily worst pain while improving quality of life

Combining influence maps and cellular automata for reactive game agents

Agents make up an important part of game worlds, ranging from the characters and monsters that live in the world to the armies that the player controls. Despite their importance, agents in current games rarely display an awareness of their environment or react appropriately, which severely detracts from the believability of the game. Some games have included agents with a basic awareness of other agents, but they are still unaware of important game events or environmental conditions. This paper presents an agent design we have developed, which combines cellular automata for environmental modeling with influence maps for agent decision-making. The agents were implemented into a 3D game environment we have developed, the EmerGEnT system, and tuned through three experiments. The result is simple, flexible game agents that are able to respond to natural phenomena (e.g. rain or fire), while pursuing a goal

Creating engaging artificial characters for games

Game developers and researchers aim to model human behaviour in order to create more engaging, entertaining and satisfying artificial characters for computer games. It is a popular belief that intelligent behaviour is the key to creating better game AI. However, as yet there is no empirical evidence to support this theory or to indicate whether other attributes, such as social interaction, realistic behaviour and communication, should also be considered. This study aimed to find out which attributes people desire in team members and opponents in computer games. The study employed a questionnaire, administered to a group of university students, directed towards ascertaining the importance of different aspects of player behaviour in games. It was found that there are two different, non-homogenous groups, each with separate needs and wants that game developers should consider. Firstly, it was found that people who prefer playing computer games with other humans tend to value intelligent behaviour and social interaction more than people who prefer computer players. Secondly, it was found that people who prefer computer players do so for convenience, practice and a preference for games that can only be played individually. It is recommended that game developers should aim to model intelligent behaviour for the first group and that the second group require an in-game learning environment for skill-development

Scripting versus emergence : issues for game developers and players in game environment design

This paper defines and discusses two contrasting approaches to designing game environments. The first, referred to as scripting, requires developers to anticipate, hand-craft and script specific game objects, events and player interactions. The second, known as emergence, involves defining general, global rules that interact to give rise to emergent gameplay. Each of these approaches is defined, discussed and analyzed with respect to the considerations and affects for game developers and game players. Subsequently, various techniques for implementing these design approaches are identified and discussed. It is concluded that scripting and emergence are two extremes of the same continuum, neither of which are ideal for game development. Rather, there needs to be a compromise in which the boundaries of action (such as story and game objectives) can be hardcoded and non-scripted behavior (such as interactions and strategies) are able to emerge within these boundaries

Current AI in games : a review

As the graphics race subsides and gamers grow weary of predictable and deterministic game characters, game developers must put aside their “old faithful” finite state machines and look to more advanced techniques that give the users the gaming experience they crave. The next industry breakthrough will be with characters that behave realistically and that can learn and adapt, rather than more polygons, higher resolution textures and more frames-per-second. This paper explores the various artificial intelligence techniques that are currently being used by game developers, as well as techniques that are new to the industry. The techniques covered in this paper are finite state machines, scripting, agents, flocking, fuzzy logic and fuzzy state machines decision trees, neural networks, genetic algorithms and extensible AI. This paper introduces each of these technique, explains how they can be applied to games and how commercial games are currently making use of them. Finally, the effectiveness of these techniques and their future role in the industry are evaluated