An artistic image of a brain representing the concept of adversarial games with AI

Adversarial Games with SHapley Additive exPlanations: Synthetic Grey

Military Simulations, AI

Adversarial games are an effective way to test the capabilities and defenses of AI systems. They can also reveal how AI makes decisions, allowing military leaders to have more confidence in the decision making abilities of AI. Sentient Digital has developed Synthetic Grey, a platform that incorporates adversarial games and SHapley Additive exPlanations (SHAP) to facilitate AI research, testing, and transparency.

An Introduction to Sentient Digital’s Synthetic Grey

Sentient Digital, Inc. unveils Synthetic Grey, a sophisticated research platform designed to test and fortify AI systems against a spectrum of adversarial scenarios, thereby bolstering their resilience against cyber threats and intentional manipulations.  With the Navy’s objectives and challenges as a guiding light, Synthetic Grey is poised to revolutionize AI application in naval strategy, decision-making, and the safeguarding of AI systems in complex and demanding military environments.  

Introduction to Adversarial Games with AI

The theory and mechanics behind the development of adversarial games involves the combination of artificial intelligence and strategic decision-making with a clear objective to define the limits of intelligent systems. In general, adversarial games involve situations where multiple players engage in antagonistic interactions, each vying to gain some level of dominance over the other players. Success relies upon the player’s ability to conduct strategic planning, exploit their opponent’s weaknesses, and adapt to dynamic environments. These same players can be AI agents, seeking to learn from each new scenario that is presented via machine learning capabilities. These AI agents commonly utilize reinforcement learning techniques to make decisions based on an action-reward process. Additionally, adversarial games that exclusively use AI agents focus on discarding the typical rule-based or pre-defined scenario and instead use an ambiguous competitive environment. In this environment, the AI agents learn and evolve through interactions with their opponents and gaming conditions, often having to discover both the nature of the game and its overall objective.


The need for transparency in AI has given rise to explainable AI (XAI), which describes ways that humans can begin to understand how AI makes decisions. The better humans understand these decisions, the more trust they will have in AI systems. Additionally, the better the functioning of the system is understood, the better humans can anticipate how it might be exploited by bad actors, and endeavor to counteract those possibilities.

XAI has been gradually evolving within the AI research community for several years. Recently the increased attention to XAI has been driven by concerns related to the adoption of AI systems in critical domains and the need for systems used in those domains to provide accountability.

Adversarial games that enlist AI systems provide an ideal platform for researching and identifying the precepts of XAI because the AI agents need to manipulate and/or exploit their opponents to arrive at their goal conditions. In this competitive process, the games provide detailed insight into the decision-making processes of the AI. The more transparent the AI system is designed, the better its actions can be understood, and its weaknesses corrected.


At Sentient Digital, Inc., we are developing a research platform known as Synthetic Grey which seeks to define the critical aspects of AI model implementation by formally defining the foundational concepts inherent in sophisticated AI system design. This formal definition codifies the abstract principles and mathematical frameworks that should govern the system’s architecture, ultimately providing a possible component-by-component basis for AI development.  Concurrently with codifying the theory, Synthetic Grey seeks to map the mechanics that govern AI’s perception, decision-making, and action. The added understanding of the mechanics allows for a replicable foundation of AI functionality, which has the potential to lead to AI systems that can create other AI systems. Sentient Digital sees Synthetic Grey as a research platform that serves as a robust test bed for conducting rigorous and scalable research experiments in this field of study.

Incorporating cutting-edge methodologies like exploitability descent and SHapley Additive exPlanations (SHAP), Synthetic Grey is engineered to provide in-depth analysis and explanations of AI decision-making processes. 

SHapley Additive exPlanations are a method of determining how an AI model makes decisions, by assigning percentage values to the model features that together produced the decision. These values indicate how important a given feature was to the final decision. Exploitability descent is a method of calculating equilibrium in adversarial games. Incorporating these concepts into Synthetic Grey furthers the objectives of transparency and accountability in AI.

Separately, as a long-term goal, Sentient Digital aims to expand Synthetic Grey to accept human input, with an objective to seek greater understanding of the optimal interface between AI systems and humans in cooperative situations.


Within the game environment, the AI player is a reinforcement learning agent that contains both a long and short-term memory structure. This agent navigates the game environment, constantly adapting and learning from its experiences. The game map, which is partitioned into discrete nodes, represents distinct obstacles for the agent on each move it makes.

Guided by a self-created and adaptable reward system, the agent seeks to overcome challenges to reach its objective. Through a process of ‘learning by experience’, the agent refines its decision-making strategy, developing a policy that maps states to optimal actions.


The core of experimentation, in Synthetic Grey, is to place the AI players in a challenging environment, observe their action, and allow the AI model to provide an explanation for those actions. Evaluation occurs when the explanation is examined against human understanding.


Synthetic Grey provides an analysis of the importance of different situations affecting the actions of the AI agent using Partial Dependence Plots (PDP). PDPs visualize the relationship between a specific AI agent action and the agent’s current situation and state. Additionally, it attempts to quantify the importance of state attributes in the AI agent’s decision process. The aggregation of these two techniques allows for the possible extraction of interpretable rules in the form of decision tree models that are easily human-readable.


Synthetic Grey offers transformative capabilities in key naval operational areas through the integration of XAI. Its impact is particularly pronounced in the following domains:

Mission Planning and Decision Support

Strategic Recommendations: The platform revolutionizes mission planning by providing AI-driven, optimal strategic recommendations. This includes sophisticated analyses for route optimization, resource allocation, and operational strategy development.

Rational Explanation: Beyond mere suggestions, Synthetic Grey offers comprehensive explanations for each recommendation. This transparency in AI reasoning enhances the decision-making process, enabling naval officers to understand and trust AI-derived strategies.

Target Identification and Threat Assessment

Precise Identification: In critical scenarios like target identification, Synthetic Grey’s XAI capabilities play a pivotal role. The platform ensures high accuracy in identifying potential threats, utilizing advanced AI algorithms for detailed analysis and recognition.

Decision Transparency: The clarity in AI decision-making processes is paramount in threat assessment. Synthetic Grey ensures that the rationale behind each identified threat is clearly communicated, reducing the risk of errors, and enabling more informed operational responses.

Logistics and Supply Chain Management

AI-Driven Decisions: Synthetic Grey applies XAI to revolutionize logistics and supply chain management. This includes optimizing inventory management, determining efficient resupply schedules, and planning distribution routes using AI.

Enhanced Accountability: The platform elevates transparency in decision-making, which is crucial for logistics management. By providing clear explanations for AI-driven decisions, Synthetic Grey ensures that logistical operations are both efficient and aligned with the Navy’s strategic goals.

Through these applications, Synthetic Grey demonstrates its capacity to significantly advance naval operations, aligning perfectly with ONR’s strategic priorities and contributing robustly to the Navy’s long-term objectives.


Sentient Digital’s Synthetic Grey research platform can serve as a robust tool for enhancing the Navy’s artificial intelligence (AI) capabilities in a dynamic and contested environment. Synthetic Grey would enable the Navy to rigorously test and improve the robustness of its AI systems by simulating diverse adversarial scenarios. Military planners could use the platform to assess the resilience of AI models against intentional manipulations and cyber threats, ensuring reliable decision-making under adverse conditions.

Additionally, the platform could contribute to the evaluation of AI model explainability under adversarial pressures, supporting ethical and accountable AI deployment. Ultimately, Synthetic Grey would empower the U.S. Navy to stay ahead in AI innovation, providing a strategic advantage in decision-making, autonomous operations, and the security of AI-driven systems in complex military engagements.

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Sentient Digital’s scientists are always developing new AI-based technology to assist the military in maintaining technological superiority. Our work includes creating highly advanced military simulations that help military personnel to prepare for the new challenges of modern warfare. Get the latest updates from our blog and if you’re interested in working with us, contact us here.