artificial-intelligence

I am putting together a project for a class that requires me to put AI in a top down Tactical Strategy game in Flash AS3. I decided that I would use a node based path finding approach because the game is based on a circular movement scheme. When a player moves a unit he essentially draws a series of line segments that connect that a player unit will follow along. I am trying to put together a similar operation for the AI units in our game by creating a list of nodes to traverse to a target node. Hence my use of Astar (the resulting path can be used to create this line). Here is my Algorithm

I've been working on a 15-puzzle solver in C. And I had some issues with the huge amounts of memory that my code uses. I won't be posting my code because it's too long... I've implemented most of the libraries I'm using and it will probably just bring confusion to you. Let's start on the basics. The things that I'm using right now are: (All of them implemented in C) - Fibonacci Heap: /* Struct for the Fibonacci Heap */ typedef struct _fiboheap { int size; // Number of nodes in the heap node min; // Pointer to the minimun element in the Heap funCompare compare; // Function to compare within

I'm new to Neural Networks, and programming generally. I've written a neural network in java, and i'm looking at football data. I have two inputs: 1) Home team win % over n games 2) Away team win % over n games Using 'standard statistical models' one can predict the number of goals that will occur in a match using these two numbers alone, with a reasonable degree of accuracy. However, when i attempt to train my NN to predict the number of goals, it simply doesn't converge :( I'm using a genetic algorithm to train the network, here is the fittest individual from the first few generations with a

When performing reference resolution on predicates describing the semantics of dialogue expressions, I need to be able to allow for partial unification due to working in an open world. For example, consider the following scenario: There is a blue box in front of you. We refer to this blue box using the id 3 . A set of predicates box(x)^blue(x) can easily resolve to the blue box you know about. Making this query will return 3 A set of predicates ball(x)^yellow(x) will not resolve to anything. This is fine. But now consider ball(x)^yellow(x)^box(y)^blue(y)^behind(x,y) that is, the yellow ball

I'm making an Othello player, and implemented a minimax algorithm with alpha-beta pruning. Then I did a bunch of research on the best ones online and keep hearing about a "negamax" algorithm that they all use. It seems like most people think negamax is faster than minimax (i think because it doesn't switch between min and max player?), so I'd like to turn my minimax algorithm into negamax if that's not too difficult. I was wondering if people had any insight on how much faster using negamax is, and any tips or code on how to turn my minimax code into a negamax algorithm that'd be appreciated!

With reference to Section 3.5 of Russel and Norvig : On a grid, each state has four successors, so the search tree including repeated states has 4^d leaves; but there are only about 2d^2 distinct states within d steps of any given state. What is the meaning of distinct states here. Can someone explain me by considering various values of d, say 1,2,3,4. What is the meaning of distinct states here. The meaning of distinct state is a unique cell, you count each cell in the grid only once. Crude upper bound to number of distinct states: First, look at a subgrid of size 2d+1 X 2d+1 , and you start