random
You are currently browsing articles tagged random.
(Original Image by everyone’s idle.)
This post was a originally published on Luma Labs, now dead.
As old as stimulus-response techniques are, they still form an important part of many AI systems, even if it is a thin layer underneath a sophisticated decision, planning, or learning system. In this tutorial I give some advice to their design and implementation, mostly out of experience gained from implementing the AI for some racing games.
A stimulus response agent (or a reactive agent) is an agent that takes inputs from its world through sensors, and then takes action based on those inputs through actuators. Between the stimulus and response, there is a processing unit that can be arbitrarily complex. An example of such an agent is one that controls a vehicle in a racing game: the agent “looks” at the road and nearby vehicles, and then decides how much to turn and break.
Read the rest of this entry »
Tags: AI, artificial intelligence, convolution, filtering, Kalman filter, Mathematics, PID controller, racing game ai, random, random steering, response curve, Special Numbers Library, stimilus response agent, system design
(Original image by Hljod.Huskona / CC BY-SA 2.0).
I used to hate neural nets. Mostly, I realise now, because I struggled to implement them correctly. Texts explaining the working of neural nets focus heavily on the mathematical mechanics, and this is good for theoretical understanding and correct usage. However, this approach is terrible for the poor implementer, neglecting many of the details that concern him or her.
This tutorial is an implementation guide. It is not an explanation of how or why neural nets work, or when they should or should not be used. This tutorial will tell you step by step how to implement a very basic neural network. It comes with a simple example problem, and I include several results that you can compare with those that you find.
Read the rest of this entry »
Tags: AI, artificial intelligence, Matlab, neural network, Octave, optimisation, pattern recognition, random, sampling
I finally laid my hands on Donald Knuth’s The Art of Computer Programming (what a wonderful set of books!), and found a neat algorithm for generating random integers 0, 1, 2, … , n – 1, with probabilities p_0, p_1, … , p_(n-1).
I have written about generating random numbers (floats) with arbitrary distributions for one dimension and higher dimensions, and indeed that method can be adapted for generating integers with specific probabilities. However, the method described below is much more concise, and efficient (I would guess) for this special case. Moreover, it is also easy to adapt it to generate floats for continuous distributions.
Read the rest of this entry »
Tags: 2D, optimisation, probability, Python, random, random distribution, random integer, random number generation
It is sometimes necessary to find the distribution given a sample set from that distribution. If we do not know anything about the distribution, we cannot recover it exactly, so here we look at ways of finding a (discrete) approximation.
Read the rest of this entry »
Tags: 2D, convolution, distribution estimation, Python, random, random distribution
I have already covered how to generate random numbers from arbitrary distributions in the one-dimensional case. Here we look at a generalisation of that method that works for higher dimensions.
The basic trick, while easy to understand, is hard to put in words (without reverting to mathematical equations). For two dimensions, we divide the plane into slices. Each slice is a 1D distribution. We also calculate a distribution from summing the frequencies in each slice. The latter distribution gives us one coordinate, and the appropriate slice to use. The distribution of that slice then gives the second coordinate. All distributions are put into inverse accumulative response curves as was done to generate one-dimensional random numbers. (You should review that before implementing the 2D case).
In more dimensions, we also slice the space up into 1D distributions. Sums of these give us more distributions, which we can sum again, and again, until we reach a single distribution. This is used for the first coordinate, and to determine which distribution to use for the next coordinate. This goes on, until a 1D slice gives us the final coordinate. Again, all distributions are converted to inverse accumulative response curves.
If the above is unclear, I hope the detailed description below clears things up.
Read the rest of this entry »
Tags: 2D, distribution function, grids, n-dimensional distributions, Python, random, random distribution, random number generation, response curve, Special Numbers Library
A cellular automata system is one of the best demonstrations of emergence. If you do not know what cellular automata (CA) is, then you should go download Conway’s Game of Life immediately:
Conway’s Game of Life
Essentially, CA is a collection of state machines, updated in discrete time intervals. The next state of one of these depends on the current state as well as the states of neighbours. Usually, the state machines correspond to cells in a grid, and the neighbours of a cell are the cells connected to that cell. For a more detailed explanation, see the Wikipedia article.
Even simple update rules can lead to interesting behaviour: patterns that cannot be predicted from the rules except by running them. With suitable rules, CA can simulate many systems:
- Natural phenomena: weather, fire, plant growth, migration patterns, spread of disease.
- Socio-economic phenomena: urbanisation, segregation, construction and property development, traffic, spread of news.
Read the rest of this entry »
Tags: 2D, AI, algorithm, blending, cellular automata, Conway's game of life, Dev.Mag, diffusion, discrete dynamics, disease simulation, fire simulation, force, Game Maker, grids, optimisation, probability, random, sampling, Simulation, social dynamics, sum, tiles
Faster Code
A while back I wrote about a simple texture algorithm that I have been exploring. The Python implementation was very slow – so much, that I decided to implement it in C++ to see what performance gain I would get. Surprisingly, the C++ version is about 100 faster, if not more. I expected a decent increase, but what once took several hours can now be done in a minute!
Read the rest of this entry »
Tags: 2D, blend animation, blending, C++, Image Processing, optimisation, Perlin noise, procedural texture, prototyping, Python, random
Random steering is often a useful for simulating interesting steering motion. In this post we look at components that make up a random steering toolkit. These can be combined in various ways to get agents to move in interesting ways.
You might want to have a look at Craig Reynolds’ Steering Behaviour for Autonomous Characters — the wander behaviour is what is essentially covered in this tutorial. The main difference is that we control the angle of movement directly, while Reynolds produce a steering force. This post only look at steering — we assume the forward speed is constant. All references to velocity or acceleration refers to angular velocity and angular acceleration.
Whenever I say “a random number”, I mean a uniformly distributed random floating point value between 0 and 1.
Read the rest of this entry »
Tags: 2D, AI, artificial intelligence, blending, C++, Game Development, Game Maker, Perlin noise, probability, random, random distribution, random motion, random steering, response curve, Reynolds, sampling, Simulation, steering bahaviors, steering behaviours, vector field, wander, white noise
The code below implements some quadtree extensions, as discussed in another Dev.Mag tutorial about quadtrees (see Issue 27). The tutorial covers the following topics:
- what to consider in choosing whether to use a quadtree or not;
- tests to help you choose an appropriate threshold;
- how to handle discrete data; and
- some modifications to the basic algorithm.
Handling Discrete Data
When it comes to discrete data, the “average” of a number of pixels doesn’t make sense. However, we can give it meaning and still use it to get good approximations of the original data, as illustrated in the images below.
 |
The original grid of discrete data. The grid contains integers from 0 to 4. Every integer is mapped to a colour in this image. (If this grid represented a tile map, every integer would be mapped to a different tile). |
 |
Here we allowed floating point numbers in the quadtree. Results of queries into the quadtree are rounded before mapping them to colours. |
 |
Here we used the floating point part of queries to bias a randomly selected integer. For example, a result of 1.25 will result in a 75% chance of yielding 2. |
 |
Here we use a quadtree with interpolation. The result is rounded before it is mapped. |
 |
Here we use a quadtree with interpolation, and use the floating point part of the number to bias a randomly selected tile, as above. |
Download
Python Implementation
Download from: http://code-spot.co.za/python-image-code/ (See quadtree.py, quadtree_image.py, and quadtree_demo.py).
Tags: 2D, AI, compression, computer graphics, Dev.Mag, image partitioning, Image Processing, Python, quadtree, quadtrees, random, spatial partitioning
In a previous post I explained a simple algorithm for generating textures. Below are some more examples of the kinds of textures that the algorithm can generate, as well as how the different parameters influence the result. You can also download the code.
Read the rest of this entry »
Tags: procedural texture, Python, random
Recent Comments