artificial-intelligence

问题 This is for http://cssfingerprint.com I have a system (see about page on site for details) where: I need to output a ranked list, with confidences, of categories that match a particular feature vector the binary feature vectors are a list of site IDs & whether this session detected a hit feature vectors are, for a given categorization, somewhat noisy (sites will decay out of history, and people will visit sites they don't normally visit) categories are a large, non-closed set (user IDs) my

问题 This is my minimax method which implements alpha beta pruning and memoization: public int[] newminimax499(int a, int b){ int bestPos=-1; int alpha= a; int beta= b; int currentScore; //boardShow(); String stateString = ""; for (int i=0; i<state.length; i++) stateString += state[i]; int[] oldAnswer = oldAnswers.get(stateString); if (oldAnswer != null) return oldAnswer; if(isGameOver2()!='N'){ int[] answer = {score(), bestPos}; oldAnswers.put (stateString, answer); return answer; } else{ for(int

问题 i'm working with api.ai and stuck on a point i want to expire lifespan of a context on a specific user input how to expire context lifespan on specific user input Api.ai any one please tell me solution since i'm unable to find it in documentation of api.ai 回答1: You can "reset" the context by setting the output context's lifetime to 0 as described on this page: https://docs.api.ai/docs/concept-contexts 来源： https://stackoverflow.com/questions/43146087/how-to-expire-context-lifespan-on-specific

问题 I would like to crop photo such that only "necessary" thing left - in this case I mean text of photographed page left. I hope, that the belowe example demonstrates it clear. I would like to do my task in python. I guess opencv library may be helpful, but what algorithms would you suggest to use? Write some own heuristics basing on image statistics, or are there some ready tools, and there is really no need for reinventing the wheel? 来源： https://stackoverflow.com/questions/18069843/image

问题 I am trying to detect features(eg.: screen, processing speed) of a product(eg.: mobile, respectively) in an english sentence. For this, my approach is that in a paragraph(that talks about the product) containing multiple sentences, the words( apart from words like pronouns or sentiment words like good, bad etc, which I store in a file) that appear most frequently are the features of that product and so I rank on the basis of their frequency and their distance with the sentiment words and take

问题 how can i be sure that the plan, computed by the fast forward planner, is the best of all the possible plans?! Does exist an automatic tool to solve this problem?! thanks a lot! 回答1: If I don't remember it wrong FF is not an optimal planner so you can't be sure if the generated plan is optimal. On the other side FF is fast in generating "good enough" solutions in contrast to optimal planners ( cpt4 , bjolp , ecc...) which provide optimal plans but much more slowly than satisficing planners.

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 3 years ago . I would like to do it (Probably it not necessary to do it) a console interface to make an implementation of a chat bot. I would like my chat bot has 3 levels of “intelligence”. Level one : My chat bot is like a dictionary. You write something like “If user say ‘Hello’, chatbot say ‘Hello’”. Or “if user say ‘How

问题 import tensorflow as tf import numpy as np layer1_weight = tf.Variable(tf.zeros([2 , 3])) layer1_bias = tf.Variable(tf.zeros([3 , 1])) layer2_weight = tf.Variable(tf.zeros([3, 1])) layer2_bias = tf.Variable(tf.constant([[0.]])) input = tf.placeholder(tf.float32 , [2 , 1] ) result = tf.placeholder(tf.float32 ,[1 , 1] ) data_input = [np.float32([[0.],[0.]]) , np.float32([[0.],[1.]]) , np.float32([[1.],[0.]]) , np.float32([[1.],[1.]])] data_output = [np.float32([[0.]]) , np.float32([[1.]]) , np

问题 I am looking at FANN (Fast Artificial Neural Network) to create a neural network to drive a car around a track in a simulation. It's possible to train on a fixed data set, but this isn't suitable for training a car to drive. I would like to use a fitness function to train my NN. Is this possible? Is it possible to tell FANN to use a fitness function rather than a fixed data set to train a NN? 回答1: I would like to use a fitness function to train my NN. Is this possible? Fitness functions judge

问题 I currently work on WPF application according to my AI interest. I wrote a Uniform Cost Search algorithm (pathfinding) and want to present it in graphical way. Path should be found and show on a graph which could be adjusted by user. I'm quite new in WPF technology, I worked more with WinForms and now have a problem with creating and managing graphical elements. In other words - I want to give opportunity to click on data grid and create your own node (sth like place on a map) which is