Every wargaming exercise eventually encounters the same constraint: the scenario is only as complex as the human who designed it. A skilled exercise planner can author a rich operational problem, populate it with realistic adversary forces, and inject timed events to stress the players – but the entire exercise runs on a script. The adversary does what the script says. The events arrive when the planner scheduled them. When a player team makes an unexpected move that invalidates the original plan, the planner improvises, and the quality of the improvisation depends on that individual's experience and cognitive load at the moment.
WARG replaces this model with an AI engine that generates the scenario, operates the adversary, and coaches players simultaneously – all without a script. The system produces multi-domain wargaming scenarios spanning land, maritime, air, space, and cyberspace, adapts adversary behaviour to player tactics as the exercise progresses, and delivers move-by-move analytical feedback through a natural language interface. This article covers the technical architecture of how that works: the scenario generation pipeline, the adversary pattern model, the cross-domain event sequencing logic, the Action Card system, the adaptive difficulty algorithm, and the post-move analysis generation process.
Scenario generation pipeline
WARG's scenario generation begins with a structured parameter set: geographic context, operational phase, force composition, domain activation, training objectives, and difficulty tier. These parameters seed a generation graph that constructs the scenario in layers. The first layer defines the strategic situation – the overarching operational problem, the competing objectives of Blue and Adversary forces, and the constraints imposed by geography, logistics, and command authority. The second layer populates the operational domain with forces, assets, infrastructure nodes, and domain-specific objectives. The third layer generates the initial information picture – what each side knows, what they can observe, and what intelligence gaps exist.
Each domain has a dedicated generation module that produces domain-appropriate content: land generates ground force dispositions, terrain obstacles, and logistics lines; maritime generates naval force groupings, sea lane chokepoints, and port infrastructure; air generates airspace structure, threat envelopes, and air asset allocation; space generates satellite coverage windows, uplink dependencies, and adversary counter-space capabilities; cyber generates network architecture, vulnerability profiles, and adversary access vectors. The cross-domain integration layer then maps dependencies between domains – which ground force movements are gated by air superiority, which communication systems depend on space assets, which cyber vulnerabilities affect physical infrastructure – creating the multi-domain interdependency structure that players must navigate.
Key insight: The value of AI scenario generation is not that it produces more scenarios than a human could author – it is that it produces scenarios calibrated to the specific gap between what the player team currently understands and what they need to understand. A human planner authors the scenario they think is interesting. The AI authors the scenario that specifically challenges the weaknesses the player team has demonstrated in previous sessions.
Adversary pattern modeling and tactical adaptation
The adversary AI in WARG maintains a rolling tactical profile for each player or player team across turns. This profile tracks move history, domain preferences, resource allocation patterns, and response tendencies – which domains the player invests in, how aggressively they push in the early game, whether they prioritise kinetic or non-kinetic action, how they respond to adversary pressure in the cyber or space domains. The profile is built from the observed sequence of player Actions Cards and decisions, updated after every move.
The adversary model uses this profile to select its own moves. Rather than consulting a static decision tree, it evaluates candidate actions against the current board state and the player profile simultaneously: an action that would be suboptimal against an average opponent may be highly effective against a player who consistently neglects maritime domain awareness. The model identifies these individual vulnerabilities and exploits them – the same way a skilled human opponent would adapt their play to a specific opponent after observing a few moves.
Adaptation operates at multiple timescales. Within a session, the adversary responds to individual moves – if a player concentrates air assets for a strike operation, the adversary activates integrated air defence and shifts ground force posture in anticipation. Across sessions, if a player repeatedly uses the same opening sequence, the adversary model recognises the pattern and prepares a counter before the player has finished deploying the familiar opening. This cross-session adaptation is what prevents the pattern memorisation problem that undermines fixed-scenario training: the scenario that worked last time will not work this time, because the adversary has already accounted for it.
Multi-domain event sequencing and cross-domain effects
In real multi-domain operations, actions in one domain produce effects in others. A successful cyber operation against an adversary air defence network degrades the effectiveness of ground-based air defence assets. The loss of a space-based communication relay compresses the bandwidth available to maritime forces. A ground offensive that seizes a logistics hub removes the forward supply capacity that enables air operations from that sector. WARG models these cross-domain effects as a dependency graph, where each domain asset and capability node has typed dependency links to nodes in other domains.
When a player action or adversary action modifies a node – degrading a capability, destroying an asset, or capturing an objective – the event sequencing engine propagates effects through the dependency graph. Some effects are immediate and deterministic: destroying a ground-based radar eliminates the coverage sector it provided. Others are probabilistic and delayed: a cyber intrusion into a command network degrades communication reliability over the following turns, with the magnitude depending on the adversary's remediation capability and the depth of the intrusion. The engine calculates propagation paths, applies effect magnitude and timing models, and injects the resulting changes into the game state at the appropriate turns.
Players observe these effects through the information picture – degraded sensors, reduced communication capacity, constrained logistics – but may not immediately understand the cross-domain causal chain that produced them. Diagnosing what happened and why is itself a training objective. The natural language coaching interface can explain the causal chain on request, connecting observable effects back to the original action that triggered them and identifying what a player could have done to prevent the cascade or exploit an equivalent cascade against adversary assets.
Key insight: Cross-domain effect propagation is deliberately asymmetric. Adversary forces in WARG are designed to invest more heavily in space and cyber domain actions than most player teams initially expect, reflecting the actual asymmetric emphasis in peer competitor doctrine. Players who focus exclusively on kinetic domain play will find their operational capacity degrading without obvious explanation until they learn to monitor and contest the non-kinetic domains proactively.
Action cards: translating decisions into simulation events
WARG uses an Action Card mechanic as the primary interface for player decisions. Each Action Card represents a discrete operational action – an airstrike, a cyber operation, a naval interdiction, a space-based sensor tasking, a special operations mission, a diplomatic signal. Cards are drawn from domain-specific decks calibrated to the player's current force composition and available resources. Playing a card commits resources, generates a game event, and triggers the adversary's response cycle.
The card mechanic serves two engineering purposes. First, it discretises the decision space into a manageable action vocabulary, which allows the adversary AI and the coaching system to reason precisely about what the player chose and why. An open-form command interface would produce ambiguous player intent; the card mechanic makes intent explicit. Second, the card draws produce natural pacing for the AI coaching system – between draws, the system analyses the previous move, updates the adversary profile, propagates cross-domain effects, and generates the learning points that will accompany the next move analysis. The turn structure provides the computational time budget the AI processing pipeline requires.
Action Cards also encode the resource economics of multi-domain operations. High-effect cards – a coordinated multi-domain strike, a space denial operation, a strategic cyber campaign – require significant resource investment and have longer cooldown periods. This forces players to make genuine trade-off decisions rather than always selecting maximum-effect options, which is the core cognitive challenge of operational planning. The AI adversary tracks the player's resource allocation across cards played and exploits periods when the player's high-value cards are on cooldown – creating time-pressure dynamics that reflect the operational tempo challenges of real multi-domain campaigns.
Natural language coaching interface architecture
The natural language interface in WARG serves two functions: it accepts player queries during play and delivers post-move analysis after each turn. The underlying architecture is a context-aware inference system that maintains a structured representation of the current game state – force positions, domain status, resource levels, event history, and the active training objectives – as a continuously updated context that accompanies every natural language interaction.
Player queries are interpreted in context. A question like "why is my air support less effective this turn?" is resolved against the current game state, where the AI can identify that the player's air base suffered a logistics degradation from an adversary cyber action two turns prior, reducing sortie generation rate. The response is specific to the current situation, not a generic explanation of how air logistics works. This context-grounding is what distinguishes coaching from FAQ lookup: the system knows what is actually happening in the player's scenario and connects its guidance to those specific circumstances.
Post-move analysis is generated after every player move. The analysis pipeline evaluates the player's decision against a set of doctrinal assessment criteria relevant to the active training objectives, identifies the most significant learning point from the move, and generates a concise annotation. The annotation is displayed alongside the move record and accumulated into the session debrief. For complex moves – particularly those that trigger cross-domain effect chains or that represent significant departures from doctrinal practice – the analysis includes a counterfactual: what would have happened if the player had chosen the doctrinally preferred alternative, and why the outcome would have differed.
Adaptive difficulty algorithm
WARG's adaptive difficulty system operates on a continuous assessment of player performance relative to the current difficulty tier. Performance is measured on three dimensions: decision quality (whether player moves are consistent with doctrinal best practice for the training objectives), resource efficiency (whether the player is achieving objectives without unnecessary resource expenditure), and cross-domain integration (whether the player is actively managing all active domains or neglecting some). Each dimension is scored after every move and aggregated into a session performance index.
The adaptive algorithm compares the session performance index against difficulty tier thresholds. When performance consistently exceeds the upper threshold for the current tier, the algorithm increases adversary sophistication on the next turn – improving adversary reaction time, increasing the depth of adversary multi-domain integration, introducing more complex cross-domain attack chains, and activating higher-tier adversary capabilities that were dormant at the lower difficulty setting. When performance drops below the lower threshold, the algorithm reduces adversary pressure to keep the exercise productive: a player who is overwhelmed is not learning, they are surviving.
Difficulty adjustments are applied gradually and across multiple parameters simultaneously to avoid the perceptibility problem: a player who notices the adversary suddenly becoming less capable will correctly conclude the system has reduced difficulty and may adjust their behaviour to game the adaptive mechanism rather than developing genuine proficiency. Distributing adjustments across multiple parameters at small magnitudes per turn keeps the adjustment below conscious perception thresholds while accumulating to a meaningful difficulty change over several turns.
Key insight: Adaptive difficulty in wargaming has a different goal than adaptive difficulty in consumer games. Consumer adaptive difficulty aims to keep the player engaged and feeling successful. Military training adaptive difficulty aims to keep the player in the optimal learning zone – which means maintaining a moderate failure rate. Players who win every exercise are not being challenged at the level that produces skill development. The system is calibrated to produce roughly equal win rates across all skill levels, not to produce consistent wins.
Move-by-move analysis generation
The post-exercise debrief in WARG is generated from the session's accumulated move analysis, structured into a coherent narrative that identifies the key decision points of the exercise, assesses each against the training objectives, and produces prioritised learning recommendations. The debrief generation pipeline processes the session record – the complete sequence of player moves, adversary responses, and coaching annotations – and identifies the three to five decisions that most significantly affected the exercise outcome.
For each key decision, the debrief presents the information available to the player at the moment of the decision, the decision made, the doctrinal alternative, and a game tree analysis of how the exercise would have unfolded under the alternative. This counterfactual structure is essential for doctrinal learning: players need to understand not just that a decision was suboptimal but specifically how the outcome would have been different, connected to the causal chain that makes the doctrinal approach superior in the given context.
The debrief concludes with skill gap annotations mapped to the training objective taxonomy. Each identified gap is linked to a specific training objective and a recommended remediation approach – which scenario types would most efficiently develop the identified skill, what domain emphasis the next session should prioritise, and whether the gap is individual or collective. For joint multi-force exercises, the debrief can be segmented by player role, providing each participant with a personalised analysis of their domain-specific decisions while also covering the collective coordination decisions that cut across all participants.
How to set up a custom multi-domain training scenario in WARG
The following steps describe the process for configuring a new custom scenario from the WARG scenario builder interface:
- Define the operational context and training objectives. Select geographic region and operational phase. Specify the training objectives – the decision skills or doctrinal tasks the exercise should develop – so the AI coaching layer calibrates its feedback throughout the session.
- Configure the domain mix and force ratio. Choose which of the five domains are active. Assign relative force weights per domain. WARG's scenario engine generates domain-appropriate forces, assets, and objectives consistent with the selected context and force balance.
- Set the adversary AI profile and difficulty tier. Select the adversary AI doctrine template (peer competitor, near-peer, non-state actor, or hybrid threat). Set the initial difficulty tier. The adaptive engine adjusts adversary sophistication in real time based on player performance during the session.
- Configure Action Card availability and coalition parameters. Choose which Action Card categories are available to each player. If running a joint multi-force exercise, assign force elements to each player and configure coalition friction parameters.
- Review the AI-generated scenario brief and begin play. WARG generates a scenario brief summarising the operational situation, force disposition, commander's intent, and key constraints. Review the brief, address questions to the AI coaching interface in natural language, then begin play.
- Use the natural language interface for real-time guidance. Query the AI coaching interface at any point for doctrinal guidance, domain-specific advice, or explanation of adversary behaviour. Responses are calibrated to the specific current game state, not generic training material.
- Review the post-exercise analysis and export the session record. Access the move-by-move AI analysis in the debrief screen. Export the session record for integration with external AAR tools or share the debrief with participants for individual review.
Frequently asked questions
+How does WARG's AI engine model adversary tactics without pre-scripted behaviour trees?
WARG's AI adversary models build a rolling tactical pattern profile for each player across multiple turns, tracking move sequences, preferred domains, and resource allocation priorities. Rather than consulting a fixed decision tree, the adversary model uses a reinforcement-learning-derived policy trained against a corpus of human wargaming records and doctrinal manuals. At runtime the model receives the current game state as a structured context window and samples its next action from the learned policy distribution, weighted by a difficulty parameter that controls how closely the model tracks its estimated optimal play.
+Can WARG scenarios be replayed or exported for after-action analysis?
Yes. WARG logs every game event – player decisions, AI adversary moves, domain-effect triggers, Action Card plays, and natural language exchanges – to a structured session record. This record can be replayed turn-by-turn in the interface for after-action review, exported as a JSON event log for integration with external AAR tools, or passed to the AI analysis module to generate a narrative debrief with specific learning points annotated against each decision point.
+What are the hardware requirements for running WARG's AI inference at the edge?
WARG's AI inference engine is designed for edge deployment without persistent cloud connectivity. The scenario generation and adversary models are quantised to run on mid-range GPU hardware – a single NVIDIA RTX-class GPU with 8 GB VRAM is sufficient for the default configuration. The natural language coaching interface requires somewhat more compute; the recommended edge configuration is a 24 GB VRAM GPU or a two-GPU setup. All models are packaged in a containerised runtime that handles resource scheduling across available hardware automatically.
+How does WARG prevent experienced players from memorising scenario patterns?
WARG's scenario generation uses a seeded procedural approach: the scenario structure is generated fresh for each session from a parameter space large enough to make repetition statistically negligible. The AI adversary model's stochastic sampling further ensures that even identical starting conditions produce different adversary play. Adaptive difficulty also prevents pattern exploitation: a player who consistently defeats a particular adversary tactic will see that tactic abandoned and replaced with a different approach targeting observed weaknesses.
+Does WARG support joint multi-force exercises with players from different nations?
Yes. WARG's joint exercise mode allows multiple players to take control of different force elements – land, maritime, air, special operations, and cyber – with the AI managing any unassigned elements. The natural language interface is available in all supported languages, and the AI coaching system contextualises its feedback to each player's assigned domain and role. Coalition friction – information-sharing delays, command authority boundaries, interoperability constraints – is modelled as a configurable parameter set that exercise designers can tune to reflect realistic multinational coordination challenges.
Related reading: AI OpFor in Wargaming and Training Simulation covers the broader design principles for doctrine-grounded adversary AI across simulation platforms. After-Action Review Software: Design and Implementation examines the AAR generation pipeline in depth, including event log structuring and LLM summarisation approaches applicable beyond WARG. AI-Adaptive Military Training provides the theoretical framework for performance modeling and adaptive difficulty that underpins WARG's training design.