I decided to check if GPT4 could interpret a Blinks game. I used The Cloud Game because I love its approach to detecting if all Blinks are on. Here is the full conversation:
Can you sumarize this code?
[The Cloud Game code, removed from here for brevity]
This code implements “The Cloud Game” on the Move38 Blinks platform. The game consists of hexagonal tile pieces called Blinks that can be turned on or off by pressing them. When a Blink is pressed, its state and its neighbors’ states are toggled. The goal is to turn all Blinks on. Triple-clicking any Blink resets the game.
Key elements in the code:
Global variables and constants are defined, such as maximum distance, communication states (FLIP, RESET, ACK), timers, and win animation variables.
setup() function initializes the random number generator.
loop() function handles the main game logic:
a. Handles input from button clicks (single click to flip, multi-click to reset).
b. Communicates and processes neighbor interactions.
c. Sends communication state to all faces.
d. Displays the current state of each Blink.
e. Displays the win animation if the win condition is met.
Helper functions areAllNeighbors() and areAnyNeighbors() check if all or any neighbors have a specific value.
displayWin() function handles the win animation stages: fade down, fade up border, fade up center, and loop center sunspots.
In summary, this code defines a simple game played with Blinks, where the objective is to turn all Blinks on by pressing them and toggling their states along with their neighbors. The code handles input, communication, display, and win conditions.
Can you give more detail on the algorithm used to determine if all Blinks are on?
The code doesn’t have a specific algorithm to determine if all Blinks are on. Instead, it uses a “propagation” approach by sending communication states between neighboring Blinks. The communication states contain the inverse of the distance from an OFF Blink.
Here’s a step-by-step explanation of how the code checks if all Blinks are on:
If a Blink is OFF, it initializes inverseDistanceFromOff as MAX_DISTANCE. If the Blink is ON, inverseDistanceFromOff is initialized as 0.
The code loops through all neighboring Blinks, checking the last value received on each face.
If a neighbor sends a FLIP value, it indicates that the neighbor was clicked. The Blink then updates its state accordingly (acknowledging the FLIP or toggling its state).
If a neighbor sends a RESET value, the Blink resets its state to OFF.
For each neighboring Blink with a value between 0 and MAX_DISTANCE (inclusive), the code updates inverseDistanceFromOff to be the maximum of the current inverseDistanceFromOff and the received value minus 1.
The Blink then sets its communication state (myCommState) to the calculated inverseDistanceFromOff.
The communication state is then sent to all neighboring Blinks. This process is repeated continuously in the loop() function.
If a Blink has an inverseDistanceFromOff value of 0, it indicates that it is the farthest from an OFF Blink, meaning all other Blinks are ON. In this case, the Blink displays the win animation.
It’s important to note that this approach doesn’t guarantee that all Blinks are on simultaneously; it only ensures that the Blink with inverseDistanceFromOff equal to 0 is the farthest from any OFF Blink. However, in the context of this game, this condition is enough to determine that all Blinks are ON, and the win condition is met.
What scenario would break this approach?
This approach can break in a few scenarios:
Large cluster of Blinks: If the cluster size is larger than the MAX_DISTANCE, the inverse distance values might not propagate far enough to cover the entire cluster. In this case, some Blinks may not receive the correct information about the farthest OFF Blink, which could lead to incorrect behavior or failure to detect the win condition.
Loop or ring-shaped cluster: In the case of a loop or ring-shaped cluster, the distance propagation might not accurately convey the farthest OFF Blink since the Blinks in the ring can continuously receive and propagate inverse distance values. The win condition might not be detected correctly, or the win animation might be displayed incorrectly.
Simultaneous clicks on multiple Blinks: If multiple adjacent Blinks are clicked simultaneously, the code has a mechanism to handle this situation by prioritizing one of the clicks. However, if multiple non-adjacent Blinks are clicked simultaneously, the inverse distance values might become inconsistent, leading to unpredictable behavior.
Rapid clicks or button glitches: If a user clicks very rapidly or the button has glitches, it might lead to issues with detecting the correct state and updating the Blinks accordingly.
In general, this approach works well for small to moderately-sized clusters and simple configurations, but it might not be suitable for more complex or larger scenarios. For such cases, alternative algorithms might be needed to ensure accurate win detection and game behavior.
