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The face of video games has changed dramatically over the years, advancing from straightforward, solitary journeys into complex, immersive worlds that mimic real life. Algorithms and Random Number Generators (RNG) lie at the center of this evolution and provide the foundation for more engaging, dynamic, and unpredictable gameplay. In this guide, we explore RNGs and algorithms in video games, their applications, and their impact on the gaming industry.
Algorithms in Video Games
An algorithm is a sequence of instructions to solve problems or accomplish a particular task. In gaming, they are necessary for simulating the intelligent behavior of non-playable characters (NPCs), game content generation, and player experience. Below are examples of important algorithms used in video games:
1. Pathfinding Algorithms
These algorithms allow NPCs to find their way through game environments efficiently. Beacons help characters navigate between points while avoiding obstacles. Some popular pathfinding algorithms are A* (A-Star) and Dijkstra’s Algorithm.
- A* Algorithm: A* calculates a route based on the shortest distance from the start position and factoring in an estimated cost to the endpoint. This is frequently utilized in real-time strategy and open-world games where an NPC must cross a complex terrain.
- Dijkstra’s Algorithm: Unlike A*, Dijkstra’s algorithm examines every possible route to discover the shortest one. It works best in instances requiring accuracy, like puzzle games or tactical RPGs.
2. Finite State Machines (FSMs)
Finite state machines are used to manage NPC behavior, consisting of a set of states (e.g., idle, patrol, combat) and transitions between these states based on specific conditions. This gives the NPCs a predictable but exciting response to different circumstances. In stealth games such as Assassin’s Creed, guards implement FSMs to patrol, investigate, and fight.
3. Monte Carlo Tree Search (MCTS)
MCTS is a more recent algorithm for games with a high degree of strategic planning and complex decision-making. It assesses many potential moves by simulating a huge number of potential outcomes and selecting the most probable path to victory. MCTS is widely used for card games and board games, where any single move can greatly change the state of the game.
4. Procedural Content Generation (PCG)
PCG algorithms dynamically create game content like levels, characters, and environments. This approach provides players with fresh experiences each time they play. Techniques like Perlin Noise are used to generate natural-looking terrain, making games like Minecraft endlessly varied.
5. Steering Algorithms
The steering algorithms that direct characters in a computer game towards their goals rely on rapidly changing priorities between seeking rewards, fleeing, and threatening obstacles. This is a fundamental algorithm for generating realistic NPC behaviors for action games.
6. Goal-Oriented Action Planning Algorithms
These algorithms assist in making non-playable characters decide on their actions as well as how to perform them in the most refined way possible to reach their goals. They make sure that all of the other algorithms coordinate to meet a common goal, like getting in position to pass a player in a racing game.
RNGs in video games
Random Number Generators bring in the element of surprise and uncertainty to a game, providing players with a more enjoyable experience, as the outcomes are no longer completely predictable. This is how RNGs work and what they are used for in video games:
How RNGs Work
RNGs use algorithms to create numbers that seem random. These algorithms generate a sequence of numbers based on a “seed” value and a formula. Since the sequence is always the same for a given seed, it is common for systems to use a dynamic seed, like the system clock or user input, to produce a random sequence.
Applications of RNGs
- Loot and Rewards: RNGs randomly determine what loot/items you get from chests, defeated enemies, etc.
- Open World Random Encounters: RNGs determine which enemies or events players are likely to come across as they explore the game world, creating a unique experience each playthrough.
- Mechanics Changer: RNGs are responsible for many of the game features, which allow for variability, such as critical hits, skill effects, and environmental conditions, which makes the game more dynamic and challenging.
- Procedural Generation: Not strictly RNG, but very often uses randomized elements to design unique environments/levels so that no two playthroughs are alike
Algorithms and RNGs: The Fundamentals of Gaming
The interplay of algorithms and RNGs is comprehensive in its implications for the video game industry, with the broad result being further depth to the games, more engagement for players, and increased replayability.
Enhanced Player Engagement
- Dynamic Environments: Many algorithms, such as PCG, can help to create a dynamic environment and change things each time a player plays a game. This keeps players entertained and interested.
- Smart NPCs: Pathfinding and FSM algorithms help NPCs act seamlessly and create better immersion.
Realism and Immersion
- NPC Movement: Steering and pathfinding algorithms give NPCs a realistic way to move about, making games feel much more realistic.
- Randomized Results: RNGs help create unpredictable outputs, which build excitement and challenge in games.
Replayability
- Unique Experiences: RNGs and PCG algorithms ensure that each playthrough is unique, encouraging players to replay games multiple times.
- Strategic Depth: MCTS and other strategic algorithms provide depth to games, requiring players to adapt their strategies based on changing conditions.
Algorithms and RNG in Crash Games
Crash games, popular in online gambling, rely on algorithms and Random Number Generators (RNGs) to ensure fairness and unpredictability. These games involve betting on a multiplier that increases until it “crashes,” with players aiming to cash out before the crash occurs.
How Do Crash Game Algorithms Work
- Random Number Generation: For each round of play, the RNGs generate a random number that decides where the crash point is, making it unpredictable when it will occur.
- Multiplier Calculation: A mathematical equation then converts this crash point into a multiplier.
- Provably Fair systems: Some games rely on provably fair systems where the server and client seeds combine to create a final hash—and the results are determined by that hash. The final output can independently represent the match based on the seeds; players can confirm that all is fair if they check this.
Types of Crash Game Algorithms
- Simple RNG Algorithms: Low-level algorithms based on RNGs may have vulnerabilities if not properly protected.
- Certified Fair Algorithms: Tested for randomness by third-party labs.
- Provably fair algorithms: Provide real-time proof of verification through cryptographic methods.
Predictability And Manipulation
Even though algorithms should be unpredictable, finding the seed could, in theory, help us predict them. Provably fair systems eliminate the risk of seed tampering after its generation, resulting in desired outcomes.
What The Future Holds for AI and RNG in Gaming
With the further development of technology, we can only expect to see even more complex algorithms and RNGs in games. Here are some future trends:
Advanced AI Algorithms
- Reinforcement Learning (RL): RL lets AI agents learn from trial and error, which could create more intelligent and adaptable NPCs.
- Neural Networks: Neural networks might allow AI to identify complicated patterns and react with human-like intuition, making the gameplay even
Enhanced RNGs
- More Complex Seeds: Future RNGs could integrate more dynamic and less predictable seeds like user behavior or even real-world data to enhance randomness.
- Hybrid Approaches: Mixing RNGs with other algorithms might produce more diverse, realistic game landscapes.
Conclusion
Modern games rely heavily on algorithms and random number generators (RNGs) to provide players the intelligence, diversity, and unpredictability they so desire. In line with technological advancement, these tools would become more complex and result in richer, deeper gaming experiences. The strategic planning of MCTS, the dynamic environments of PCG, and the unpredictability of RNGs are all central to the future of gaming.
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Sources:
- https://applydata.io/why-are-algorithms-so-good-at-playing-games/
- https://www.blogs.opengrowth.com/which-algorithm-is-used-in-games
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