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How does client side prediction and server reconciliation work?
In the first article of this series, we explored a client-server model with an authoritative server and dumb clients that just send inputs to the server and then render the updated game state when the server sends it.
Can a client predict the state of the game world?
Under this assumption, the client can predict the state of the game world after the inputs are processed, and most of the time this will be correct.
How is Lag related to client side prediction?
If the game world is deterministic enough (that is, given a game state and a set of inputs, the result is completely predictable), Let’s suppose we have a 100 ms lag, and the animation of the character moving from one square to the next takes 100 ms. Using the naive implementation, the whole action would take 200 ms: Network delay + animation.
What’s the difference between client and server predictions?
The predicted state at the client is x = 12, but the server says the new game state is x = 11. Because the server is authoritative, the client must move the character back to x = 11.
How to simulate and predict identified model output?
There are two ways of generating an identified model response: Simulation computes the model response using input data and initial conditions. Prediction computes the model response at some specified amount of time in the future using the current and past values of measured input and output values, as well as initial conditions.
How is client side prediction used in gaming?
To achieve this, you’ll need to implement client-side prediction and smoothing. There are many variants of client-side prediction but the basic idea is always the same: the client responds to player input by moving the player before the server processes the input and tells the client where they player should be.
How does simulation and prediction work in math?
Simulation computes the model response using input data and initial conditions. Prediction computes the model response at some specified amount of time in the future using the current and past values of measured input and output values, as well as initial conditions.