FalconerAI set out to create NPCs (Non Player Characters) that are more challenging, and balanced toward alternate play styles rather than pushing players into types of gameplay and behavior that they do not enjoy. We also designed them to be amenable to cooperation as much as combat, and really to just be more interesting to play with.

We are using AI (Artificial Intelligence) to create these smarter NPCs (Non-Player Character) in games by employing a variety of techniques, including machine learning, reinforcement learning, and deep learning.

We first collect data on the player's actions through a standard API (Automated Program Interface) that gathers player actions in real time during gameplay. This could include things like the strategies they use, the weapons they prefer, defences that they erect, their behavior towards other players and NPCs, their movement patterns, their reaction times, and so on.

Once enough data has been collected, machine learning algorithms are used to analyze it and identify patterns. For example, if the player often uses a certain strategy to defeat an enemy, the AI could learn to anticipate this strategy and develop countermeasures.

This data is then used to create an autonomous NPC that mimics the behavior of the player(s). These Player simulants are then used in reinforcement learning model where through many trials in a simulated game environment the NPCs are trained to achieve a goal. The player simulant receives rewards or penalties for its actions and learns over time to maximize its rewards. The NPC enemies are trained to defeat the player simulant as well as the other NPCs with unique constraints to produce multiple approaches to defeating the player simulant. The NPCs learn over time which actions (e.g., liaisons, strategies, tactics, and maneuvers) lead to success and which ones lead to failure.This is a type of machine learning where the NPCS learn to make decisions by taking actions in an environment to achieve a goal. Each NPC receives rewards or penalties for its actions and learns over time to maximize its rewards. In the context of the game world, the primary goal would be to defeat the player simulant but defeating the other NPCs would be a secondary goal but alliances are possible leading to other rewards. The constraints are designed to allow multiple possible player strategies to be successful including peaceful cooperation.

Once a sufficient amount of player data is collected, deep learning using neural networks with many layers is used to analyze data and make predictions. The deep learning algorithm is used to analyze the player's actions and predict their future behavior and chance of success. This allows the NPC to anticipate the player's moves and react accordingly. The chance of success is also fed back to the player.

Each NPC is also designed to continuously learn and adapt as the player changes their behavior. This means that the NPC's strategy evolves over time, keeping the game challenging and engaging for the player. The strength of each NPC can be adjusted dynamically to change the difficulty of the game. The player can also select which NPCs to include and exclude at play time.

These AI-driven NPCs not only make video games more challenging, but also make them more realistic. In real life, opponents learn and adapt to each other's strategies, and an AI NPC that can do the same brings a new level of realism to games.