In this work, we examine Monte Carlo tree search (MCTS) as an adaptive benchmark for competitive coevolution, both as a method for characterizing individual solutions, and as a measure of global progress throughout a coevolutionary run. We find that MCTS provides a much more practical measurement of solution quality than existing methods of comparing to randomly-sampled solutions. Additionally, we introduce a class of n-player games for which MCTS can be shown to have comparable performance for different n, allowing for the characterization of how coevolution performs in games with different numbers of players. We demonstrate these techniques for n-player Nim and an n-player variant of Othello.

Immersive virtual reality (VR) offers affordances that may reduce cognitive complexity in binary reverse engineering (RE), enabling embodied and external cognition to augment the RE process through enhancing memory, hypothesis testing, and visual organization. In prior work, we applied a cognitive systems engineering approach to identify an initial set of affordances and implemented a VR environment to support RE through spatial persistence and interactivity. In this work, we extend that platform with an integrated large language model (LLM) agent capable of querying binary analysis tools, answering technical questions, and dynamically generating immersive 3D visualizations in alignment with analyst tasks. We describe the system architecture and our evaluation process and results. Our pilot study shows that while LLMs can generate meaningful 3D call graphs (for small programs) that align with design principles, output quality varies widely. This work raises open questions about the potential for LLMs to function as visualization agents, constructing 3D representations that reflect cognitive design principles without explicit training.

Given recent large investments in space-based businesses, proliferated low Earth orbit (P-LEO) satellite constellations offering broadband internet services have become an active area of research and development. The landscape of satellite communications (SATCOM) has radically changed: unknown dynamics now enter the marketplace and interactions between constellation operators are undetermined. Although there has been extensive work comparing the performance of individual P-LEO constellations and their broad economic impacts, the temporal effects and competitive dynamics of this nascent marketplace have not been modeled. To address this need, we have created a tabletop board game that simulates both the system design of P-LEO constellations and the economic dynamics between constellation operators (players within the board game). By representing market events as game mechanics and incrementally developing the game complexity, we purged the majority of trivial game strategies and edge cases while simultaneously improving playability and engagement. This process has led to the development of a robust, multi-player game that can be used to examine and test high-level P-LEO constellation development strategies while reducing the time required for intensive software development and play-testing.

Through its affordances, immersive virtual reality (VR) offers a means to apply embodied and external cognition from the physical realm to solving analytical problems that are typically only conceptual. We present an example of executing a structured analysis following the tenets of cognitive systems engineering to derive immersive affordances applicable to a difficult analytical problem, in our case, reverse engineering (RE) binary programs. We conducted a basic cognitive task analysis of the problem to reveal features of its cognitive model and their associated fundamental cognitive phenomena, and then we mapped those concepts to immersive affordances associated with those concepts. We implemented a subset of those affordances in a VR system facilitating discovery of features of a binary program. Feedback from RE practitioners drove the initial development of the system and we are preparing for a formal effectiveness study to inform the direction of future research.

The study of strategic interaction among a society of agents is often handled using the machinery of game theory. This research examines how a Markov decision process (MDP) model may be applied to an important element of repeated game theory: the iterated prisoner's dilemma. Our study uses a Markovian approach to the game to represent the problem of in a computer simulation environment. A pure Markov approach is used on a simplified version of the iterated game and then we formulate the general game as a partially observable Markov decision process (POMDP). Finally, we use a cellular structure as an environment for players to compete and adapt. We apply both a simple replacement strategy and a cellular neural network to the environment.

The traveling salesman problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-complete, and is an often-used benchmark for new optimization techniques. One of the main challenges with this problem is that standard, non-AI heuristic approaches such as the Lin-Kernighan algorithm (LK) and the chained LK variant are currently very effective and in wide use for the common fully connected, Euclidean variant that is considered here. This paper presents an algorithm that uses adaptive resonance theory (ART) in combination with a variation of the Lin-Kernighan local optimization algorithm to solve very large instances of the TSP. The primary advantage of this algorithm over traditional LK and chained-LK approaches is the increased scalability and parallelism allowed by the divide-and-conquer clustering paradigm. Tours obtained by the algorithm are lower quality, but scaling is much better and there is a high potential for increasing performance using parallel hardware.