Terry Soule

This systematic literature review examines the evolving landscape of deception in video games and artificial intelligence (AI). The integration of deceptive strategies in AI, particularly within gaming environments, represents a growing area of interest with significant implications for both gameplay and broader applications, such as cybersecurity. Through a systematic review of 97 papers, 79 were excluded after introduction analysis revealed focus on deception outside gaming contexts (e.g., advertising, propaganda, movement detection), leaving 18 papers directly applicable to game-based deception. Of these 18, 61% provided formal or contextual definitions while 39% relied on assumed understanding. The review categorizes the current body of research into three primary areas: definitions of deception, methods for implementing and mitigating deception, and the frameworks used to analyze these strategies. The review highlights the diversity in the conceptualization of deception, ranging from formal definitions grounded in game theory, to more context-specific operational definitions. Key models such as signaling games (information asymmetry scenarios), Stackelberg games (leader–follower dynamics), and hypergames (perception-based interactions) are explored alongside AI-driven approaches like reinforcement learning (trial-and-error learning) and generative neural networks, which simulate and detect deception in complex environments. The review identifies significant gaps in the standardization of definitions and the practical implementation of deceptive strategies, calling for further interdisciplinary research to address these challenges. The ethical implications of deploying deceptive AI systems are discussed, emphasizing the need for comprehensive frameworks that balance innovation with responsible usage. Future research must prioritize the standardized definitions and interdisciplinary collaboration across ethics, law, and social sciences to address the expanding applications and ethical implications of deceptive AI technologies.

Serious games that integrate educational content with engaging gameplay mechanics hold promise for reducing cognitive load and increasing student motivation in STEM and health science education. This preliminary study presents the development and evaluation of the Medimon NASA Demo, a game-based learning prototype designed to teach undergraduate students about the musculoskeletal and visual systems—two critical domains in space medicine. Participants (n = 23) engaged with the game over a two-week self-regulated learning period. The game employed mnemonic-based characters, visual storytelling, and turn-based battle mechanics to reinforce medical concepts. Quantitative results demonstrated significant learning gains, with posttest scores increasing by an average of 23% and a normalized change of c = 0.4. Engagement levels were high across multiple dimensions of situational interest, and 74% of participants preferred the game over traditional formats. Qualitative analysis of open-ended responses revealed themes related to intrinsic appeal, perceived learning efficacy, interaction design, and cognitive resource management. While the game had minimal impact on short-term STEM career interest, its educational potential was clearly supported. These findings suggest that mnemonic-driven serious games like Medimon can effectively enhance engagement and learning in health science education, especially when aligned with real-world contexts such as space medicine.

This paper proposes using a text-based dungeon crawler adventure as a case study to explore the methods to implement deception in video games. The study proposes a framework for integrating deception into gameplay, leveraging the alignment system from Dungeons and Dragons to define character behavior and motivation. The proposed approach would create an environment that allows researchers to observe AI-controlled characters in a dynamically generated environment that leverages LLMs. The framework is designed to address the issue of monotony in current games by training a deceptive agent, or villain, to recognize and exploit player beliefs and intentions. This adds complexity and depth to the gaming experience, making it more engaging and dynamic. Future research directions include integrating human players into the game environment and transitioning to 3-D gaming platforms, potentially leading to more immersive experiences, particularly in massive multiplayer online role-playing games (MMORPGs). By exploring the intersection of AI, deception, and gaming, this paper contributes to the evolving interactive entertainment landscape, paving the way for more sophisticated and captivating game experiences.

Contribution: This full research paper presents an innovative parsing technique that aims to improve syntax error messages for undergraduate students. The quality of syntax error messages generated by the new parsing technique was evaluated and compared with the messages produced by mainstream compilers. Background: Unfortunately, compiler error messages are often unhelpful. The study explains some intrinsic challenges faced in generating good syntax error messages and presents a global, local, and expression-level (GLE) parsing technique to overcome some of these challenges. GLE is a 3-phase parsing that prioritizes the parsing of the large code components over diving into all the details. The first phase parses the functional structures and ignores errors in the syntax of the smaller constructions. The second phase parses the control structures and ignores errors in the expressions and other statements. The third phase parses the expressions and statements excluded from phase two. Research Question: Can GLE parsing techniques help generate better syntax error messages? Methodology: The study evaluated the quality of syntax error messages generated by the proposed GLE parsing technique. The evaluation was done in a controlled experiment and within-group design where participants found and fixed errors in erroneous programs using accompanying error messages from different compilers. The independent variable is the compiler type. The dependent variable is the quality of syntax error messages. The quality of syntax error messages is measured by three factors: the success rate of finding errors in erroneous programs, the success rate of fixing syntax errors in erroneous programs, and mean-time-to-find and -fix erroneous programs. Three questions were used to evaluate the "helping in finding errors" quality of the error message: 1) what is the error in the program? 2) in which line is the error? 3) what is the cause of the error? One question was used to evaluate the quality of "helping in fixing errors": "how to fix the error?" The time that participants used to find and fix a program was calculated. The participants were 51 undergraduate students in the Computer Science and Engineering department at the New Mexico Institute of Mining and Technology. Findings: The results show there is a significant statistical difference in finding errors and fixing erroneous programs using messages generated by the proposed GLE parsing technique and two mainstream compilers: GNU GCC and Microsoft Visual C++. No significant difference exists in the time-to-find and -fix. The result indicates that the proposed GLE parsing technique can help generate better error messages for undergraduate students.

Video games have the potential to help teach evolutionary biology, but most commercial games misrepresent evolutionary principles by allowing player choice to dictate evolutionary trajectories. Our game studio aims to incorporate scientifically accurate evolutionary models into gameplay mechanics. In our previous games Darwin’s Demons and Project Hastur, we designed digital genomes and implemented evolutionary models to create enemy populations that adapt to player strategies. However, accurately simulating evolution can sometimes conflict with crafting an enjoyable game. Here we examine balancing scientific realism with fun in the game design process. Using experimental data from Project Hastur, we show enemies evolve increased size and sensory abilities to counter player defenses, demonstrating the game mechanic’s adaptive capabilities. We discuss how mutation rates, population sizes, generation times and other parameters can be adjusted to balance accuracy and enjoyment, with the goal of creating engaging games that reinforce and demonstrate, rather than misrepresent, evolutionary principles.

It is widely assumed that evolution has the potential to make better video games. However, relatively few commercial games have been released that use evolution as a core game mechanic, and of these games only a very small sub-set have shown that evolution occurs as expected and improves game play as intended. Thus, there remains a critical gap between studies showing the clear potential of evolution to improve video games and studies showing that evolution did improve game play in a commercially released game. We have developed Darwin's Demons, a space shooter inspired by old style arcade games, with the added feature of evolving enemies. In August, 2016 Darwin's Demons was Green-lit for sale on Steam, a standard benchmark for commercialization of games. In this paper we present and test four hypotheses that form the basis for the claim that evolution occurs and improves game play in Darwin's Demons. More generally, these hypotheses can be used to confirm that evolution meets the intended design goals for other evolutionary games. Our results support the hypotheses that evolution makes Darwin's Demons get progressively more difficult over the course of a game, and that the fitness function, player choices, and player strategy all affect the evolutionary trajectory during a single game. This suggests that in Darwin's Demons, the enemies adapt to the player's decisions and strategy, making the game interesting and increasing its replayability.

This research tests the co-evolvability of sensor morphology and behavior as a function of the sensing modality (scent versus touch). The results show that the evolved sensor morphology and behavior are tightly coupled and influenced by sensor type and environment. Implying that co-evolution of physical morphology, behavior, and sensor morphology can generate better performance than using fixed sensor morphologies, but that this is often a more difficult task than evolving physical morphology for a fixed behavior or vice versa.