artificial-intelligence

I was thinking of creating a chatbot using something like markov chains, but I'm not entirely sure how to get it to work. From what I understand, you create a table from data with a given word and then words which follow. Is it possible to attach any sort of probability or counter while training the bot? Is that even a good idea? The second part of the problem is with keywords. Assuming I can already identify keywords from user input, how do I generate a sentence which uses that keyword? I don't always want to start the sentence with the keyword, so how do I seed the markov chain? Nocker I

Back when I was at school, I remember tinkering with a Mac game where you programmed little robots in a sort of pseudo-assembler language which could then battle each other. They could move themselves around the arena, look for opponents in different directions, and fire some sort of weapon. Pretty basic stuff, but I remember it quite fondly, even if I can't remember the name. Are there any good modern day equivalents? The game in question was definitely Robowar for the Mac. My son had a lot of fun with it and went on to program real robots. As mentioned earlier by Proud, there is a wiki page

I have this following rule in my CLIPS file: (defrule check-final (declare (salience 12)) ?scnt <- (set-count (value ?v) (class ?c)) (test (= ?v ?*total*)) => (printout T ?*total* " == " ?v crlf) ) And I get the following strange output: CLIPS>(run) 14 == 9 5 == 2 How is this possible ???? Pattern matching for this rule occurs whenever the fact set-count is asserted or modified. The rule is fired some time afterwards, during the call to run . These two processes can be widely separated in time. The value of ?*v* can of course change during that long period of time. The key is to realize that

Here is an idea: We have web applications with exposed restful APIs which accepts json. Now how about using google speech APIs to take user voice input convert it to text then somehow translate that text to JSONs required by APIs and then call those application APIe with JSON? Is there any. Library to translate text to a specified JSon format? Has anybody used this approach? This is called "intent analysis". There are such libraries, for example RASA For example you input is "show me chinese restaurants". The output would be { "text": "show me chinese restaurants", "intent": "restaurant_search

I have written the following binary classification program in tensorflow that is buggy. The cost is returning to be zero all the time no matter what the input is. I am trying to debug a larger program which is not learning anything from the data. I have narrowed down at least one bug to the cost function always returning zero. The given program is using some random inputs and is having the same problem. self.X_train and self.y_train is originally supposed to read from files and the function self.predict() has more layers forming a feedforward neural network. import numpy as np import

I have a quick question regarding backpropagation. I am looking at the following: http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf In this paper, it says to calculate the error of the neuron as Error = Output(i) * (1 - Output(i)) * (Target(i) - Output(i)) I have put the part of the equation that I don't understand in bold. In the paper, it says that the Output(i) * (1 - Output(i)) term is needed because of the sigmoid function - but I still don't understand why this would be nessecary. What would be wrong with using Error = abs(Output(i) - Target(i)) ? Is the error function regardless of the