artificial-intelligence

问题 I try to learn and implement a simple genetic algorithm library for my project. At this time, evolution, selection of population is ready, and I'm trying to implement a simple good mutation operator like the Gaussian mutation operator (GMO) for my genetic evolution engine in Java and Scala. I find some information on Gaussian mutation operator (GMO) into the paper A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms (P.M. Mateo, I. Alberto), page 6 and 7.

问题 I have 6 int parameters ranging from 0 to 100 The total combination of the numbers are 100^6 and each combination gives a result ranging approx. from -10000 to 100000 or even more. Input data example: MySimulation (57, 78, 20, 10, 90, 50) = 300 <- Best Result MySimulation (50, 80, 10, 90, 35, 8) = 200 MySimulation (4, 55, 40, 99, 40, 50) = -50 <- Worst Result The higher the result the better the combination of numbers are, I already have the calculation which gives a result, I only need AI to

问题 I have 6 int parameters ranging from 0 to 100 The total combination of the numbers are 100^6 and each combination gives a result ranging approx. from -10000 to 100000 or even more. Input data example: MySimulation (57, 78, 20, 10, 90, 50) = 300 <- Best Result MySimulation (50, 80, 10, 90, 35, 8) = 200 MySimulation (4, 55, 40, 99, 40, 50) = -50 <- Worst Result The higher the result the better the combination of numbers are, I already have the calculation which gives a result, I only need AI to

问题 It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, visit the help center. Closed 7 years ago . According to Wikipedia (which is a bad source, I know) A neural network is comprised of An input layer of A neurons Multiple (B) Hidden layers each comprised of C neurons. An output layer of "D" neurons. I

问题 I have an object, that is facing a particular direction with (for instance) a 45 degree field of view, and a limit view range. I have done all the initial checks (Quadtree node, and distance), but now I need to check if a particular object is within that view cone, (In this case to decide only to follow that object if we can see it). Apart from casting a ray for each degree from Direction - (FieldOfView / 2) to Direction + (FieldOfView / 2) (I am doing that at the moment and it is horrible),

问题 I have created a Tic-Tac-Toe game on a microcontroller, including a perfect AI (perfect meaning that it doesn't lose). I did not use a minimax algorithm for that, just a little state machine with all possible and optimal moves. My problem now is that I wanted to implement different difficulties (Easy, Medium and Hard). The AI so far would be the hard one. So I've thought about how to do this the best way and ended up wanting to use the minimax algorithm but in a way that it calculates all the

问题 The 15 Puzzle is a classical problem for modelling algorithms involving heuristics. Commonly used heuristics for this problem include counting the number of misplaced tiles and finding the sum of the Manhattan distances between each block and its position in the goal configuration. Note that both are admissible, i.e., they never overestimate the number of moves left, which ensures optimality for certain search algorithms such as A*. What Heuristic do you think is proper, A* seems to work nice

问题 A little background: as a way to learn multinode trees in C++, I decided to generate all possible TicTacToe boards and store them in a tree such that the branch beginning at a node are all boards that can follow from that node, and the children of a node are boards that follow in one move. After that, I thought it would be fun to write an AI to play TicTacToe using that tree as a decision tree. TTT is a solvable problem where a perfect player will never lose, so it seemed an easy AI to code

问题 The classical 8-puzzle belongs to the family of sliding blocks. My book (Artificial intelligence A modern approach by Stuart Russell and peter Norwig) says that the 8-puzzle has 9!/2 possible states. But WHY the /2 ? How do you get this? 回答1: 9! is the total number of possible configurations of the puzzle, whereas 9!/2 is the total number of solvable configurations. For example, this configuration doesn't have a solution: 1 2 3 4 5 6 8 7 Read more about the solvability of certain

问题 The classical 8-puzzle belongs to the family of sliding blocks. My book (Artificial intelligence A modern approach by Stuart Russell and peter Norwig) says that the 8-puzzle has 9!/2 possible states. But WHY the /2 ? How do you get this? 回答1: 9! is the total number of possible configurations of the puzzle, whereas 9!/2 is the total number of solvable configurations. For example, this configuration doesn't have a solution: 1 2 3 4 5 6 8 7 Read more about the solvability of certain