artificial-intelligence

I prototyped a tiny search engine with pagerank that worked on my computer. I am interested in building a knowledge graph on top of it, and it should return only queried webpages that are within the right context, similarly to how Google found relevant answers to search questions. I saw a lot of publicity around knowledge graph but not a lot of literature and almost no pseudocode like guideline of building one. Does anyone know good references on how such knowledge graph works internally, so there will be no need to create models about a knowledge graph? Kikohs Knowledge graph is a buzzword.

The kernel trick maps a non-linear problem into a linear problem. My questions are: 1. What is the main difference between a linear and a non-linear problem? What is the intuition behind the difference of these two classes of problem? And How does kernel trick helps use the linear classifiers on a non-linear problem? 2. Why is the dot product so important in the two cases? Thanks. Many classifiers, among them the linear Support Vector Machine (SVM) , can only solve problems that are linearly separable, i.e. where the points belonging to class 1 can be separated from the points belonging to

I'm designing a realtime strategy wargame where the AI will be responsible for controlling a large number of units (possibly 1000+) on a large hexagonal map. A unit has a number of action points which can be expended on movement, attacking enemy units or various special actions (e.g. building new units). For example, a tank with 5 action points could spend 3 on movement then 2 in firing on an enemy within range. Different units have different costs for different actions etc. Some additional notes: The output of the AI is a "command" to any given unit Action points are allocated at the

For learning purposes I'd like to study an open source expert system, in particular one that can reason and explain it's reasoning. Which ones do you know? Some open source expert systems / expert systems tools (tools you can use to write Expert Systems) include C Language Integrated Production System (CLIPS) , CLIPS is an enviroment used to make rule or o bject based expert systems Python Knowledge Engine Pyke , Pyke allows you to use Logic Programming to make expert systems in Python OpenExpert PHP Expert System Tool mainly focused on application for Legal Expert Systems. d3web is Java

I am still a beginner but I want to write a character-recognition-program. This program isn't ready yet. And I edited a lot, therefor the comments may not match exactly. I will use the 8-connectivity for the connected component labeling. from PIL import Image import numpy as np im = Image.open("D:\\Python26\\PYTHON-PROGRAMME\\bild_schrift.jpg") w,h = im.size w = int(w) h = int(h) #2D-Array for area area = [] for x in range(w): area.append([]) for y in range(h): area[x].append(2) #number 0 is white, number 1 is black #2D-Array for letter letter = [] for x in range(50): letter.append([]) for y

some time ago I have started my adventure with machine learning (during last 2 years of my studies). I have read a lot of books and written a lot of code with machine learning algorithms EXCEPT neural networks, which were out of my scope. I'm very interested in this topic, but I have a huge problem: All the books I have read have two main issues: Contain tones of maths equations. After lecture I'm quite familiar with them and by hand, on the paper I can do the calculations. Contain big examples embedded in some complicated context (for example investigating internet shop sales rates) and to

I want to build a bot that asks someone a few simple questions and branches based on the answer. I realize parsing meaning from the human responses will be challenging, but how do you setup the program to deal with the "state" of the conversation? It will be a one-to-one conversation between a human and the bot. argv0 You probably want to look into Markov Chains as the basics for the bot AI. I wrote something a long time ago (the code to which I'm not proud of at all, and needs some mods to run on Python > 1.5) that may be a useful starting place for you: http://sourceforge.net/projects/benzo/

I recently attended a class on coursera about "Natural Language Processing" and I learnt a lot about parsing, IR and other interesting aspects like Q&A etc. though I grasped the concepts well but I did not actually get any practical knowledge of it. Can anyone suggest me good online tutorials or books for Natural Language Processing? Thanks You could read Jurafsky and Martin's Speech and Language Processing (2008 edition) , which is the standard textbook in the field. It's long, and has a variety of topics, so I'd suggest reading just the chapters that really apply to your interests. Further,

Does anyone know the specific differences and features among the three, Or if one has more features/more flexible to use as a developer? Chandra Sekhar wit.ai vs api.ai vs luis.ai ╔══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╗ ║ wit.ai vs api.ai vs luis.ai ║ ╠══════╦════════════════════════════════════╦═════════════════════════════════════════════╦════════════════════════════════════╣ ║ S.No ║ Wit.ai ║ Api.ai ║ Luis.ai ║ ╠══════╬════════════════════════════════════╬═════════════════════════════════════════════╬══

I found a lot of references to the AI of the ghosts in Pacman, but none of them mentioned how the eyes find their way back to the central ghost hole after a ghost is eaten by Pacman. In my implementation I implemented a simple but awful solution. I just hard coded on every corner which direction should be taken. Are there any better/or the best solution? Maybe a generic one that works with different level designs? Kylotan Actually, I'd say your approach is a pretty awesome solution, with almost zero-run time cost compared to any sort of pathfinding. If you need it to generalise to arbitrary