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So I've been thinking of creating a program similar to this in Python. The problem is that I know next to nothing about AI. Right now I'm reading up on genetic algorithms and neural networks and such, but there's a lot of things to absorb.
My largest problem now is I don't know how the entire thing is put together. So if anyone can tell me the general framework for a program like this, I'll be really grateful. For example, how the creatures can "see" things in their surroundings, the kind of "chromosomes" they have and how the environment is created.
Also, can anyone suggest suitable libraries for AIs, any engines, etc? I've been thinking of using neurolab, will that be a good idea?
Thanks!
Interestingly, I've created what you're describing, in Python.
Here's a pic:
The green dots are food, and the red ones are organisms that need to evolve and learn how to eat the food blocks.
The best source of inspiration and guidance for something like this is definitely Polyworld: youtube.com/watch?v=_m97_kL4ox0.
Another great example which helped me learn about backpropagation, which I suggest you should learn too, is here: arctrix.com/nas/python/bpnn.py.
Although this question is simply too vague to answer correctly, the general tip is that you first get a solid ground on how ANN's function, and then construct a program with this general algorithm:
Check for user input (should the camera move? should the program quit?)
For each organism:
Check if it is alive, etc.
Take input from the world
Evaluate its neural network
Apply the results to the physical body
Apply positive or negative feedback (as you'll see, it all comes down to this)
Update the world (you can use a physics engine)
Optionally, draw the world
For example, your input neurons could correspond with physical or visual stimulation. Is there something in front of me? Then set neuron X to 1, otherwise to 0.
And your output neurons could map into forces. Is neuron Y active? If so, apply an impulse to this organism's in this world cycle.
I also recommend that you don't use other people's libraries for your neural network computation, because this is a great way to actually learn how to implement them yourself. You could you Pygame for rendering, and PyBox2D for physics, though.
Now, about "seeing" other organisms... These problems are best solved when isolated. You need a good software design, that will enable you to divide the whole problem into many subproblems, which are easier to solve.
In general, seeing can be done by raycasting. Create a directional, normalized vector (where the length is 1 unit, that is, sqrt(x*x + y*y) == 1), and then scale it (x *= factor; y *= factor;). That way, you get a line that's stretching outwards. Before you scale it, you can then loop through all of the organisms in the world (you'll have to optimize this), and check if the vector is inside of them. If it is, calculate its length, and your organism has some information about its surroundings.
There are many simpler ways. For example, divide space around each organism into four quadrants, which four represent eyes (X - up, right, down, left of the organism). Then calculate the length from the organism to every other organism (optimize!). Find the closest organism. Find out in which quadrant it is. Voila, an active eye!
I hope I was helpful, but you'll have to be more precise and come back when you have an actual problem. Read the FAQ as well.
http://scikit-learn.org/ is a great resource (and framework) for you to learn about machine learning in python.
If you want to build a neural network, http://pybrain.org/ is a good framework.
Related
I'm producing an ugv prototype. The goal is to perform the desired actions to the targets set within the maze. When I surf the Internet, the mere right to navigate in the labyrinth is usually made with a distance sensor. I want to consult more ideas than the question.
I want to navigate the labyrinth by analyzing the image from the 3d stereo camera. Is there a resource or successful method you can suggest for this? As a secondary problem, the car must start in front of the entrance of the labyrinth, see the entrance and go in, and then leave the labyrinth after it completes operations in the labyrinth.
I would be glad if you suggest a source for this problem. :)
The problem description is a bit vague, but i'll try to highlight some general ideas.
An useful assumption is that labyrinth is a 2D environment which you want to explore. You need to know, at every moment, which part of the map has been explored, which part of the map still needs exploring, and which part of the map is accessible in any way (in other words, where are the walls).
An easy initial data structure to help with this is a simple matrix, where each cell represents a square in the real world. Each cell can be then labelled according to its state, starting in an unexplored state. Then you start moving, and exploring. Based on the distances reported by the camera, you can estimate the state of each cell. The exploration can be guided by something such as A* or Q-learning.
Now, a rather subtle issue is that you will have to deal with uncertainty and noise. Sometimes you can ignore it, sometimes you don't. The finer the resolution you need, the bigger is the issue. A probabilistic framework is most likely the best solution.
There is an entire field of research of the so-called SLAM algorithms. SLAM stands for simultaneous localization and mapping. They build a map using some sort of input from various types of cameras or sensors, and they build a map. While building the map, they also solve the localization problem within the map. The algorithms are usually designed for 3d environments, and are more demanding than the simpler solution indicated above, but you can find ready to use implementations. For exploration, something like Q-learning still have to be used.
I am trying to assess the potential of Python to calculate the service area of two points.
The idea is to create a map showing which terminal is more efficient in serving a given cell based in distance or cost or time (different map for each).
The image shows point A and point B as terminals, I am trying to calculate the service area (or influence area) for each of the terminals.
In the example on the right the domain is homogeneous, and in the example on the left we have rail (green) and waterway (yellow). The different transportation modes will change the cost and time to market of any shipment to/from A and B. Intermodal operations are possible when any of the modes intercept i.e. green to white, white to yellow, yellow to green, etc.
By service area I mean a given cell is closer/cheaper/faster to a A or to B. Once I have this information than I´d be able to create a service area map of A and B.
My question is if python is the right tool for this. As you might notice I am not familiar with programming and would appreciate any tips (tutorials, etc).
Please feel free to ask any questions back if the problem description is not clear.
Domain of the problem:
You can solve this problem in almost any programming language.
Python is a high-level programming language, meaning it takes care of things like memory management. This makes it somewhat slower but easier to learn as you have to write fewer lines of code to do what you want.
It is also versatile, well supported and established, making it a good candidate for a first language.
However, ultimately, the question is what you are going to do with it? For example, if you want to develop something for the web, then going with JavaScript is probably better.
Here a rough guide where different programming languages are used
Otherwise google "which programming language should I learn" to find any of millions of articles on this topic.
I'd like to improve my little robot with machine learning.
Up to now it uses simple while and if then decisions in its main function to act as a lawn mowing robot.
My idea is to use SKLearn for that purpose.
Please help me to find the right first steps.
i have a few sensors that tell about the world otside:
World ={yaw, pan, tilt, distance_to_front_obstacle, ground_color}
I have a state vector
State = {left_motor, right_motor, cutter_motor}
that controls the 3 actors of the robot.
I'd like to build a dataset of input and output values to teach sklearn the wished behaviour, after that the input values should give the correct output values for the actors.
One example: if the motors are on and the robot should move forward but the distance meter tells constant values, the robot seems to be blocked. Now it should decide to draw back and turn and move to another direction.
First of all, do you think that this is possible with sklearn and second how should i start?
My (simple) robot control code is here: http://github.com/bgewehr/RPiMower
Please help me with the first steps!
I would suggest to use Reinforcement Learning. Here you have a tutorial of Q-Learning that fits well into your problem.
If you want code in python, right now I think there is no implementation of Q-learning in scikit-learn. However, I can give you some examples of code in python that you could use: 1, 2 and 3.
Also please have in mind that reinforcement learning is set to maximize the sum of all future rewards. You have to focus on the general view.
Good luck :-)
The sklearn package contains a lot of useful tools for machine learning so I dont think thats a problem. If it is, then there are definitely other useful python packages. I think collecting data for the supervised learning phase will be the challenging part, and wonder if it would be smart to make a track with tape within a grid system. That would make it be easier to translate the track to labels (x,y positions in the grid). Each cell in the grid should be small if you want to make complex tracks later on I think. It may be very smart to check how they did in the self-driving google car.
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I made a heightmap generator which uses gradient/value noise to generate a terrain. The problem is, that the height map is too chaotic to look realistic.
Here's what I am talking about:
Here's the map without the colors:
I used a 257x257 grid of blocks with 17x17 gradients.
As it is visible, there are too many islands as well as there are some random small beach islands in the middle of the ocean.
Also, There are a lot of sharp edges, especially for the mountain terrain (dark gray).
What I would like is a smoother and less chaotic terrain, such as a large island, etcetera. How do I do that?
In games, the most common noise generator for textures and heightmaps is the Perlin Noise.
I don't know from your answer is you actually want to create the noise generator or use it in your application.
If you are looking to create your own Perlin Noise Generator, this would be a good starting point.
I would however recommend using the noise (https://pypi.python.org/pypi/noise/) library available through pip using:
pip install noise
You can then use the noise.snoise2(x,y,a,b,c) function and fiddle with with the different parameters.
I would recommend reading this article: http://simblob.blogspot.ch/2010/01/simple-map-generation.html if you want to learn more about terrain generation.
Look at this article where Amit walks through some map generation techniques. He even has sample code online.
In the article, he takes perlin noise as a randomization parameter to his terrain generator, but doesn't use it as the whole generator. The result looks really good. (I'd post a picture of the result, but I don't know of copyright issues just yet.)
While you're at it, Amit has written and curated on things game programming for years and years. Here and here are a few more articles of his on the subject. I hope this doesn't become a time sink for you, I've certainly spent many hours on his blog. :)
(PS. I prefer simplex noise over perlin noise. Same inventor, simpler implementation, and looks better to me.)
From what I see, your sample may lack octaves and interpolation.
Depending on the implementation you are using, you may play with octave number, frequency, persistence / lacunarity, various interpolation techniques, etc...
Try playing / mixing with turbulence too (easy way to add fancy features to your height maps).
Many simplex noise (Ken Perlin's too, but scales better / faster on more dimensions) implementations deal with pretty complete set of parameters for you to play with, when generating your height maps.
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I am a machine learning beginner. I'd like to learn the basics by teaching computers to play checkers. Actually, the games I want to learn are Domineering and Hex. My language of choice is Python
These games are pretty easy to store and the rules are much simpler than chess, but there aren't too many people who play. If I can get this idea off the ground it would be great for experimenting Combinatorial Game Theory to see if a computer and find the optimal move.
I found this old paper on checkers from the 1960's by a guy at IBM. Originally I had asked about neural networks, but they are saying it's the wrong tool.
EDIT: It could be that machine learning is not the right strategy. In that case, what goes wrong? and what is a better way?
You might want to take a look at the following: Chinook, Upper Confidence Trees, Reinforcement Learning, and Alpha-Beta pruning. I personally like to combine Alpha-Beta Pruning and Upper Confidence Trees (UCT) for perfect information games where each player has less than 10 reasonable moves. You can use Temporal Difference Learning to create a position evaluation function. Game AI is probably the most fun way to learn machine learning.
For links to all of these topics, click on
http://artent.net/blog/2012/09/26/checkers-and-machine-learning/
(I was not able to include more links because the stack overflow software considers me a newbie!)
Get the book called "Machine learning" by McGraw Hill and read the first chapter. It's extremely well written and the first chapter will teach you enough to make a program that plays checkers. Personally I made a program that plays 5 in a row on miniclip.com, also in python.
http://www.amazon.com/Learning-McGraw-Hill-International-Editions-Computer/dp/0071154671
When playing checkers, you seek to gain an advantage over your opponent by taking his or her pieces and crowning your own. Losing your pieces and allowing your opponent to crown his or her pieces is not desirable, so you avoid doing it.
Board game engines usually revolve around a position evaluation function. For checkers, my first guess would be something like this:
score = number of allies - number of opponents
+ 3 * number of crowned allies - 3 * number of crowned opponents
Given a board, this function will return the score of the board. The higher the score, the better your position. The lower the score, the worse your position.
To make a naive checkers "engine", all you need to do is find the best move given a board position, which is just searching through all immediate legal moves and finding the one that maximizes your score.
Your engine won't think ahead more than one move, but it will be able to play against you somewhat.
The next step would to give your engine the ability to plan ahead, which essentially is predicting your opponent's responses. To do that, just find your opponent's best move (here comes recursion) and subtract it from your score.