AI in video games has come a long way from still enemies in the House of the Dead arcade game to the adaptive enemies in Dragon’s Dogma II. The adaptive enemies usually learn from the player’s interactions with them and adjust strategies accordingly. These examples of adaptive learning AI are examples of revolutionary modern game design.
This blog will explore the concept of adaptive learning models and how they are revolutionizing modern game design, as well as personalizing gameplay experience.
What Are Adaptive Learning Models in the Context of Gaming?
Adaptive learning models are models of education where the learning experiences are customized on the basis of the needs, capabilities and strengths of an individual. In video games, these models work as frameworks that adjust the strategies and decision-making of players based on their previous responses, experiences, and outcomes.
From the definition, it can be stated that this concept started with education and has now been modified to support gaming experiences.
The key components that adaptive learning models use in the context of gaming are:
Data Collection: Real-time data collection based on decisions taken by players and their responses to certain scenarios. This real-time data collection also leads to adjustments in real-time to cater the scenarios to lead to varying player experiences.
Pattern Recognition: The AI learns from recognizing the patterns of players and these patterns are used to make changes to the situation and scenarios, to add a dynamic challenge or progression approaches to players.
For example, while playing rummy online against bot opponents, the bot identifies your tactics and responds to them, that is using an adaptive learning model.
How They Are Applied in Modern Games
Adaptive learning models are being used in gaming using the concept of how the enemy AI and the AI of your companions learn alongside the player. How does this work in practice? It represents how different approaches to gameplay taken by players can create a variety of gameplay outcomes. Thus, the experience of two gamers can be completely different.
Returning to the example of Dragon’s Dogma II, if you are using a melee build, the enemies start carrying and wearing shields, and if you are a ranged build, they start carrying torches to throw at you to do damage from a distance.
Thus, the enemy AI in the game is using certain tracking metrics such as your reaction times, the decisions that you tend to make and your usual strategy. These dynamic situations lead to varying stimuli that you have to adjust to on-the-go.
Some games also include the concept of Dynamic Difficulty Adjustment or DDA. This often works in games such as Fallout 3, where player level has a significant impact on the damage output against certain enemies. In response to that, the DDA system scales enemies up to the level of the player character. This prevents the player from having a disappointingly easy experience when they are at a higher level.
Another technology that modern games use to provide an adaptive experience is using procedurally generated levels. In games like No Man’s Sky or Borderlands, the levels are not specifically designed manually; instead, they are designed by a learning algorithm. These imply that when you replay the same level, you might find that the loot is different, the enemies might be different, and in some cases, you might end up in a completely different map layout.
Here is a table explaining some of the elements of adaptive learning in gameplay.
| Game Feature | Adaptation Triggered By | Player Impact |
| Difficulty Levels | Response and performance patterns | A balanced experience at all levels. |
| NPC Behavior | Players’ in-game actions | Realistic and evolving friendly and enemy NPCs. |
| Game Hints & Tutorials | Inactivity or errors | Dynamic and catered assistance. |
| Level Design/Obstacles | Player progression speed | A smoother progression. |
How Adaptive Models Benefit Gamers?
Gamer experience is adjusted based on adaptive models and the following benefits are most pronounced-
Personalized experiences:
The essential aspect of adaptive models that benefits the gamers is how the experience is personalized. Games are growing alongside the players. Thus, there is no point of saturation, where the player cannot experience anything new from the game. Dynamically adaptive games also ensure that both beginners and experienced players can have a balanced experience.
Emotional and cognitive engagement:
Emotional and cognitive engagement is also a relevant impact of adaptive learning in games. When the player’s intelligence as well as emotional responses can change the outcome of a game, it causes the player’s experience to feel catered.
Accessibility at all skill levels:
Some games, with dynamic difficulty, also offer higher engagement for all players. If you are struggling with an area, the enemies start doing less damage, and if you are doing well, the enemy difficulty starts scaling. Thus, while a beginner can have a less-frustrating learning experience, a veteran can enjoy a challenge to match their skills.
If your rummy app caters to your skill level by matching you with other beginners as opponents, the game will become more accessible to you.
Use Cases in Popular Games
While different examples of the usage of adaptive learning systems in gaming have been provided throughout this blog, here are some notable examples which have made waves across the global gaming community:
| Game Name | Notable Use of Adaptive AI |
| Left 4 Dead |
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| Middle-Earth: Shadow of Mordor |
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| Resident Evil 4: Remake |
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| Metal Gear Solid V: The Phantom Pain |
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Challenges and Limitations
While adaptive AI in gaming has been a huge hit among game developers as well as gamers, even to this day, only a limited number of games have used this system. The limitations are less related to technology and more related to long-term implications.
The Ethics of Data-driven AI learning:
Adaptive AI and learning systems involve data tracking by AI. AI-tracking might have ethical concerns, especially considering that they gather player data without explicit permission.
Development Complexities and Costs:
Some of these adaptive AI systems are patented. Warner Bros., the publisher of Middle-Earth: Shadow of Mordor has patented the Nemesis system. Thus, other game studios will not be able to replicate such a system. Costs also hinder many developers from implementing such systems.
Conclusion
Adaptive learning models have led to an immense leap in gaming, in terms of replayability, catered experience, and player progression.
Many developers who have implemented these models have created unique and revolutionary gaming experiences for players. Adaptive AI, Dynamic Difficulty, and Procedural level generation have led to a personalized and dynamic experience that emotionally resonates with gamers.
