Achieving balance in competitive games is extremely difficult: even small design asymmetries can create unfair advantages that undermine gameplay quality. We show that co-evolutionary algorithms can automatically detect and quantify game imbalance, providing multiple objective metrics that complement traditional manual design methods. Our approach employs competitive co-evolution between two populations representing opposing players' strategies, using alternating multi-objective evolution cycles, to prevent dominance and promote balanced competition. The key innovation is a novel hypervolume-based "movement" metric that measures how much each player sacrifices due to competitive pressure, directly quantifying the game balance. We illustrate our approach through three comprehensive case studies spanning unbalanced and balanced game configurations. Results demonstrate that our proposed multi-objective coevolutionary search approach quantifies imbalance in terms of unequal movement from cooperative to competitive Pareto fronts by both agents, and can successfully identifies when a balanced game has occurred. This work establishes co-evolutionary optimization as a practical tool for automated game balance assessment, with applications extending beyond games to generic competitive multi-agent system requiring fairness guarantees.

Many practical problems involve multiple interdependent agents, each aiming to optimize its own objectives. Wargame strategy optimization, which requires optimizing strategies for at least two agents—attackers and defenders—presents unique challenges due to the interdependence of the agents’ strategies. This characteristic necessitates a co-evolutionary approach, where each agent’s strategy is continually adjusted in response to the other’s. The complexity increases when each agent pursues multiple conflicting objectives, resulting in Pareto-optimal strategy sets that require sequential decision-making (DM). To address these challenges, we introduce a novel multi-objective competitive co-evolutionary optimization (MoCCoEv) framework, specifically tailored for wargame strategy optimization. This framework integrates regularity-based search with an iterative and interactive DM approach, fostering a continuous interplay between co-evolving agents. Additionally, we introduce the concept of progressive shrinking, which interactively reduces the dimensions of the agents’ strategy parameters to mirror real-world decision-making by enforcing commitment to earlier moves and facilitating effective strategic choices. Our flexible and adaptable framework also supports alternative strategies, such as deception, and can be applied to other multi-agent optimization problems.

Wargame strategy optimization involves two competing agents—offense and defense—and requires extensive simulations of wargame tools, resulting in high computational costs for evaluating potential strategies. To reduce these costs, surrogate models are employed to approximate the objective functions, with their accuracy directly affecting the optimization process. This paper proposes a novel framework for progressive surrogate modeling to improve the quality of surrogate models while addressing the wargame strategy optimization problem. Our approach utilizes a Multi-objective Competitive Co-Evolutionary (MoCCoEv) algorithm, which iteratively refines the surrogate models. The process begins by using MoCCoEv algorithm to optimize wargame strategies generating new strategies. The new strategies are then replaced in the training data to update and improve the surrogate models. This cyclical process ensures that the models are continuously refined in the regions of interest, leading to more accurate predictions and enhanced optimization results.

Pilots operating modern cockpits often face high cognitive demands due to complex interfaces and multitasking requirements, which can lead to overload and decreased performance. This study introduces AdaptiveCoPilot, a neuroadaptive guidance system that adapts visual, auditory, and textual cues in real time based on the pilot’s cognitive workload, measured via functional Near-Infrared Spectroscopy (fNIRS). A formative study with expert pilots (N=3) identified adaptive rules for modality switching and information load adjustments during preflight tasks. These insights informed the design of AdaptiveCoPilot, which integrates cognitive state assessments, behavioral data, and adaptive strategies within a context-aware Large Language Model (LLM). The system was evaluated in a virtual reality (VR) simulated cockpit with licensed pilots (N=8), comparing its performance against baseline and random feedback conditions. The results indicate that the pilots using AdaptiveCoPilot exhibited higher rates of optimal cognitive load states on the facets of working memory and perception, along with reduced task completion times. Based on the formative study, experimental findings, qualitative interviews, we propose a set of strategies for future development of neuroadaptive pilot guidance systems and highlight the potential of neuroadaptive systems to enhance pilot performance and safety in aviation environments.

The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reason about the environment state in relation to a given task, and seamlessly interact with the task performer. These interactions typically involve an AR headset equipped with sensors which capture video, audio, and haptic feedback. Previous works have sought to facilitate the development of intelligent AR assistants by visualizing these sensor data streams in conjunction with the assistant's perception and reasoning model outputs. However, existing visual analytics systems do not focus on user modeling or include biometric data, and are only capable of visualizing a single task session for a single performer at a time. Moreover, they typically assume a task involves linear progression from one step to the next. We propose a visual analytics system that allows users to compare performance during multiple task sessions, focusing on non-linear tasks where different step sequences can lead to success. In particular, we design visualizations for understanding user behavior through functional near-infrared spectroscopy (fNIRS) data as a proxy for perception, attention, and memory as well as corresponding motion data (acceleration, angular velocity, and gaze). We distill these insights into embedding representations that allow users to easily select groups of sessions with similar behaviors. We provide two case studies that demonstrate how to use these visualizations to gain insights about task performance using data collected during helicopter copilot training tasks. Finally, we evaluate our approach through an in-depth examination of a think-aloud experiment with five domain experts.