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A computer challenge to test understanding of Bayesian updating.

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BayesianGame

Processing sketches to simulate creating maps from noisy information channels.

For more information about how to succeed. Click on the picture below to see a YouTube about how to make a Bayesian estimator.

Youtube Tutorial

In this Processing sketch, we create a Field that has a number randomly positioned boxes that may overlap. The Field is displayed on the left half of the display. There is also an MyMap object that is displayed on the right hand side of the display. The MyMap object a grid that displays the likelihood of there being an object in corresponding region of the Field. Initially the all areas of the MyMap object are set to a 20% chance of there being an object. There is a Sensor object that looks at random positions in the Field and reports that it findings to the MyMap object by calling its update method. The Sensor object is prone to error with the following issues:

Error type Error strength
Error on positive sighting 20% of the time falsely reports nothing
Error on negative sighting 10% of the time falsely reports something
Error on coordinates Coordinates are wrong in both the x and y directions by a random error with standard deviation 5

Using this information your challenge is to rewrite the update function within the MyMap class. **Do not change other files other than the MyMap file, and you will likely only need to work within the update function. Though you can add more class variables and helper functions the MyMap class if you like.

The function currently there is the one that takes the information raw and sets the corresponding part of the map to the information from the sensor. The code for that is given by:

 void update(int myx, int myy, float value) {
        
    /* THIS IS WHERE YOU PUT CODE.
    
    update takes in an x coordinate,  a y coordinate, and a value that  
     is either 0.0 or 1.0. Your job is to update the map array. The array 
     contains a triple array of boxes that represent square 12x12 regions of 
     the field. E.g. map[3][2][1] is the probability that there IS SOMETHING THERE 
     (because the third coordinate is 1) in the region defined by x coordinates
     between 36 and 48 (because the first coordinate is 3) and y coordinates 
     between 24 and 36 (because the second coordinate is 2).
    
    

     */

    int newX, newY;

    newX=myx/gridsize;
    newY=myy/gridsize;
    // If value=0 then 1-0 is 1, and 100 percent. 
    myMap[newX][newY][0]=(int)(1-value);
    myMap[newX][newY][1]=(int)(value);
  }

DESCRIPTION OF VARIABLES IN MyMap:

Variable Description
myx the x-coordinate where the sensor has evaluated (prone to some error)
myy the x-coordinate where the sensor has evaluated (prone to some error)
value either 0 or 1. 0 means the sensor has not sensed a white object. 1 means it has.
gridsize the size of the squares that make up the map
myWidth the number of columns in the map.
myHeight the number of rows in the map.
offx a variable that is used to determine where to draw the map. You should not need to use this variable.
offy a variable that is used to determine where to draw the map. You should not need to use this variable.

After three minutes of this code your map may look something like this.

The goal is to use Bayesian updating to create a more faithful representation. For instance, one code that implemented a bayesian approach was able to get these results after 3 minutes.

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