Deterministic Thief Simulation & Test Harness Guide

Hey guys! Let's dive into creating a deterministic simulation and test harness for thief probabilities. This is super important because we need to ensure our thief behaviors are predictable and testable, especially in games or simulations where randomness can make things a bit chaotic. We're going to break down the background, design, and how to make sure it all works perfectly. So, buckle up, and let’s get started!

Background and Context

So, why is this even necessary? Well, think about it: when you're dealing with probabilities, especially in something like a game where a thief might decide to steal something based on a certain percentage, you need to be able to test that behavior reliably. If the thief's actions are completely random every time, it's almost impossible to write good tests. We need a way to make those probabilistic actions deterministic – meaning, given the same starting conditions, the thief will always do the same thing. This is crucial for making sure our game logic works as expected and doesn't have any sneaky bugs lurking around. We aim to make every probabilistic thief action testable. This means that for any scenario, we can predict the thief's behavior with certainty, allowing for comprehensive testing and debugging. This deterministic approach ensures that our tests are reproducible and reliable. By injecting a seed into the RandomService, we can control the sequence of random numbers generated, making the behavior predictable. Furthermore, this setup allows us to cover a wide range of probability scenarios, including edge cases (0% and 100% probabilities) and everything in between. Such thorough testing ensures that the thief's actions are not only predictable but also behave correctly under various circumstances. The end goal is to create a system where tests can be written to verify the thief's behavior in a controlled environment. This level of control is vital for identifying and fixing any unexpected issues that might arise due to the probabilistic nature of the thief's actions. By establishing a robust test harness, we provide a means to confidently assess and refine the thief's AI, leading to a more polished and reliable game or simulation experience.

Think about scenarios like THIEF-VS-ADVENTURER or I-THIEF. These are situations where the thief's actions significantly impact the game. We want to make sure that the thief's behavior is consistent and fair, so players don't feel like the game is cheating them with random events. This involves setting up a robust testing environment where we can run controlled experiments. We need to be able to simulate various situations and see how the thief responds. This includes situations where the thief has a high probability of success, a low probability, and everything in between. The ability to test these scenarios repeatedly and consistently is what allows us to fine-tune the thief's AI and ensure it behaves as expected. For instance, we might want to test how the thief reacts in different environments, such as a crowded marketplace versus a secluded alleyway. Or we might want to see how the thief's behavior changes based on the actions of the adventurer. By creating a deterministic simulation, we can explore these scenarios in detail and make sure the thief's actions are both believable and fair. This ultimately leads to a better gaming experience for the players, as they can trust that the game is operating according to the rules and not just relying on random chance.

Design: Making It Deterministic

Okay, so how do we actually make this happen? The key is the RandomService. We need to make it injectable and seedable. Injectable means we can easily swap out the random number generator for testing purposes. Seedable means we can give it a specific starting point (a seed), so it produces the same sequence of random numbers every time. Imagine it like planting a seed in the ground – if you plant the same seed, you expect the same plant to grow. Similarly, with the same seed in our RandomService, we expect the same thief behavior. This allows us to recreate scenarios exactly as they happened, which is super useful for debugging. All the probability checks related to the thief will use this RandomService**. This centralizes the randomness and makes it easier to control. We avoid scattering random number generation throughout the code, which could make testing a nightmare. Instead, everything goes through the RandomService, giving us a single point of control. This design decision is crucial for maintaining determinism. If different parts of the code were generating random numbers independently, we'd lose the ability to predict the thief's actions. By channeling all random number requests through a single, controlled service, we ensure that the sequence of random numbers is consistent and repeatable. This approach also makes it easier to monitor and adjust the randomness if needed. For example, we might want to change the distribution of random numbers to simulate different types of environments or scenarios. With a centralized RandomService, we can make these adjustments in one place, without having to modify the code in multiple locations. This simplifies the development process and reduces the risk of introducing bugs. Furthermore, the RandomService` can be extended to provide more advanced features, such as the ability to log the sequence of random numbers generated, or to rewind and replay the sequence. These features can be invaluable for debugging complex scenarios and understanding exactly why the thief behaved in a particular way.

Now, let's talk about the test harness. This is where the magic happens. We need a way to create deterministic tick sequences and handle edge cases. Think of a