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How do I find the most “Naturally" direct route using A-star (A*)

- by Greg B

I have implemented the A* algorithm in AS3 and it works great except for one thing. Often the resulting path does not take the most “natural” or smooth route to the target. In my environment the object can move diagonally as inexpensively as it can move horizontally or vertically. Here is a very simple example; the start point is marked by the S, and the end (or finish) point by the F. | | | | | | | | | | |S| | | | | | | | | x| | | | | | | | | | x| | | | | | | | | | x| | | | | | | | | | x| | | | | | | | | | x| | | | | | | | | | |F| | | | | | | | | | | | | | | | | | | | | | | | | | | | | As you can see, during the 1st round of finding, nodes [0,2], [1,2], [2,2] will all be added to the list of possible node as they all have a score of N. The issue I’m having comes at the next point when I’m trying to decide which node to proceed with. In the example above I am using possibleNodes[0] to choose the next node. If I change this to possibleNodes[possibleNodes.length-1] I get the following path. | | | | | | | | | | |S| | | | | | | | | | |x| | | | | | | | | | |x| | | | | | | | | | |x| | | | | | | | |x| | | | | | | | |x| | | | | | | | |F| | | | | | | | | | | | | | | | | | | | | | | | | | | | | And then with possibleNextNodes[Math.round(possibleNextNodes.length / 2)-1] | | | | | | | | | | |S| | | | | | | | | |x| | | | | | | | | x| | | | | | | | | | x| | | | | | | | | | x| | | | | | | | | | x| | | | | | | | | | |F| | | | | | | | | | | | | | | | | | | | | | | | | | | | | All these paths have the same cost as they all contain the same number of steps but, in this situation, the most sensible path would be as follows... | | | | | | | | | | |S| | | | | | | | | |x| | | | | | | | | |x| | | | | | | | | |x| | | | | | | | | |x| | | | | | | | | |x| | | | | | | | | |F| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Is there a formally accepted method of making the path appear more sensible rather than just mathematically correct?

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What algorithms are suitable for this simple machine learning problem?

- by user213060

I have a what I think is a simple machine learning question. Here is the basic problem: I am repeatedly given a new object and a list of descriptions about the object. For example: new_object: 'bob' new_object_descriptions: ['tall','old','funny']. I then have to use some kind of machine learning to find previously handled objects that had similar descriptions, for example, past_similar_objects: ['frank','steve','joe']. Next, I have an algorithm that can directly measure whether these objects are indeed similar to bob, for example, correct_objects: ['steve','joe']. The classifier is then given this feedback training of successful matches. Then this loop repeats with a new object. a Here's the pseudo-code: Classifier=new_classifier() while True: new_object,new_object_descriptions = get_new_object_and_descriptions() past_similar_objects = Classifier.classify(new_object,new_object_descriptions) correct_objects = calc_successful_matches(new_object,past_similar_objects) Classifier.train_successful_matches(object,correct_objects) But, there are some stipulations that may limit what classifier can be used: There will be millions of objects put into this classifier so classification and training needs to scale well to millions of object types and still be fast. I believe this disqualifies something like a spam classifier that is optimal for just two types: spam or not spam. (Update: I could probably narrow this to thousands of objects instead of millions, if that is a problem.) Again, I prefer speed when millions of objects are being classified, over accuracy. What are decent, fast machine learning algorithms for this purpose?

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Programming Technique: How to create a simple card game

- by Shyam

Hi, As I am learning the Ruby language, I am getting closer to actual programming. So I was thinking of creating a simple card game. My question isn't Ruby orientated, but I do know want to learn how to solve this problem with a genuine OOP approach. In my card game I want to have four players. Using a standard deck with 52 cards, no jokers/wildcards. In the game I won't use the Ace as a dual card, it is always the highest card. So, the programming problems I wonder about are the following: How can I sort/randomize the deck of cards? There are four types, each having 13 values. Eventually there can be only unique values, so picking random values could generate duplicates. How can I implement a simple AI? As there are tons of card games, someone would have figured this part out already, so references would be great. I am a truly Ruby nuby, and my goal here is to learn to solve problems, so pseudo code would be great, just to understand how to solve the problem programmatically. I apologize for my grammar and writing style if it's unclear, for it is not my native language. Also pointers to sites where such challenges are explained, would be a great resource! Thank you for your comments, answers and feedback!

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Higher-order unification

- by rwallace

I'm working on a higher-order theorem prover, of which unification seems to be the most difficult subproblem. If Huet's algorithm is still considered state-of-the-art, does anyone have any links to explanations of it that are written to be understood by a programmer rather than a mathematician? Or even any examples of where it works and the usual first-order algorithm doesn't?

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Find optimal/good-enough strategy and AI for the game 'Proximity'?

- by smci

'Proximity' is a strategy game of territorial domination similar to Othello, Go and Risk. Two players, uses a 10x12 hex grid. Game invented by Brian Cable in 2007. Seems to be a worthy game for discussing a) optimal algorithm then b) how to build an AI. Strategies are going to be probabilistic or heuristic-based, due to the randomness factor, and the insane branching factor (20^120). So it will be kind of hard to compare objectively. A compute time limit of 5s per turn seems reasonable. Game: Flash version here and many copies elsewhere on the web Rules: here Object: to have control of the most armies after all tiles have been placed. Each turn you received a randomly numbered tile (value between 1 and 20 armies) to place on any vacant board space. If this tile is adjacent to any ally tiles, it will strengthen each tile's defenses +1 (up to a max value of 20). If it is adjacent to any enemy tiles, it will take control over them if its number is higher than the number on the enemy tile. Thoughts on strategy: Here are some initial thoughts; setting the computer AI to Expert will probably teach a lot: minimizing your perimeter seems to be a good strategy, to prevent flips and minimize worst-case damage like in Go, leaving holes inside your formation is lethal, only more so with the hex grid because you can lose armies on up to 6 squares in one move low-numbered tiles are a liability, so place them away from your main territory, near the board edges and scattered. You can also use low-numbered tiles to plug holes in your formation, or make small gains along the perimeter which the opponent will not tend to bother attacking. a triangle formation of three pieces is strong since they mutually reinforce, and also reduce the perimeter Each tile can be flipped at most 6 times, i.e. when its neighbor tiles are occupied. Control of a formation can flow back and forth. Sometimes you lose part of a formation and plug any holes to render that part of the board 'dead' and lock in your territory/ prevent further losses. Low-numbered tiles are obvious-but-low-valued liabilities, but high-numbered tiles can be bigger liabilities if they get flipped (which is harder). One lucky play with a 20-army tile can cause a swing of 200 (from +100 to -100 armies). So tile placement will have both offensive and defensive considerations. Comment 1,2,4 seem to resemble a minimax strategy where we minimize the maximum expected possible loss (modified by some probabilistic consideration of the value ß the opponent can get from 1..20 i.e. a structure which can only be flipped by a ß=20 tile is 'nearly impregnable'.) I'm not clear what the implications of comments 3,5,6 are for optimal strategy. Interested in comments from Go, Chess or Othello players. (The sequel ProximityHD for XBox Live, allows 4-player -cooperative or -competitive local multiplayer increases the branching factor since you now have 5 tiles in your hand at any given time, of which you can only play one. Reinforcement of ally tiles is increased to +2 per ally.)

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Designing bayesian networks

- by devoured elysium

I have a basic question about Bayesian networks. Let's assume we have an engine, that with 1/3 probability can stop working. I'll call this variable ENGINE. If it stops working, then your car doesn't work. If the engine is working, then your car will work 99% of the time. I'll call this one CAR. Now, if your car is old(OLD), instead of not working 1/3 of the time, your engine will stop working 1/2 of the time. I'm being asked to first design the network and then assign all the conditional probabilities associated with the table. I'd say the diagram of this network would be something like OLD -> ENGINE -> CAR Now, for the conditional probabilities tables I did the following: OLD |ENGINE ------------ True | 0.50 False | 0.33 and ENGINE|CAR ------------ True | 0.99 False | 0.00 Now, I am having trouble about how to define the probabilities of OLD. In my point of view, old is not something that has a CAUSE relationship with ENGINE, I'd say it is more a characteristic of it. Maybe there is a different way to express this in the diagram? If the diagram is indeed correct, how would I go to make the tables? Thanks

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Neural Network settings for fast training

- by danpalmer

I am creating a tool for predicting the time and cost of software projects based on past data. The tool uses a neural network to do this and so far, the results are promising, but I think I can do a lot more optimisation just by changing the properties of the network. There don't seem to be any rules or even many best-practices when it comes to these settings so if anyone with experience could help me I would greatly appreciate it. The input data is made up of a series of integers that could go up as high as the user wants to go, but most will be under 100,000 I would have thought. Some will be as low as 1. They are details like number of people on a project and the cost of a project, as well as details about database entities and use cases. There are 10 inputs in total and 2 outputs (the time and cost). I am using Resilient Propagation to train the network. Currently it has: 10 input nodes, 1 hidden layer with 5 nodes and 2 output nodes. I am training to get under a 5% error rate. The algorithm must run on a webserver so I have put in a measure to stop training when it looks like it isn't going anywhere. This is set to 10,000 training iterations. Currently, when I try to train it with some data that is a bit varied, but well within the limits of what we expect users to put into it, it takes a long time to train, hitting the 10,000 iteration limit over and over again. This is the first time I have used a neural network and I don't really know what to expect. If you could give me some hints on what sort of settings I should be using for the network and for the iteration limit I would greatly appreciate it. Thank you!

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chess AI for GAE

- by Richard

I am looking for a Chess AI that can be run on Google App Engine. Most chess AI's seem to be written in C and so can not be run on the GAE. It needs to be strong enough to beat a casual player, but efficient enough that it can calculate a move within a single request (less than 10 secs). Ideally it would be written in Python for easier integration with existing code. I came across a few promising projects but they don't look mature: http://code.google.com/p/chess-free http://mariobalibrera.com/mics/ai.html

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How to find minimum cut-sets for several subgraphs of a graph of degrees 2 to 4

- by Tore

I have a problem, Im trying to make A* searches through a grid based game like pacman or sokoban, but i need to find "enclosures". What do i mean by enclosures? subgraphs with as few cut edges as possible given a maximum size and minimum size for number of vertices that act as soft constraints. Alternatively you could say i am looking to find bridges between subgraphs, but its generally the same problem. Given a game that looks like this, what i want to do is find enclosures so that i can properly find entrances to them and thus get a good heuristic for reaching vertices inside these enclosures. So what i want is to find these colored regions on any given map. The reason for me bothering to do this and not just staying content with the performance of a simple manhattan distance heuristic is that an enclosure heuristic can give more optimal results and i would not have to actually do the A* to get some proper distance calculations and also for later adding competitive blocking of opponents within these enclosures when playing sokoban type games. Also the enclosure heuristic can be used for a minimax approach to finding goal vertices more properly. Do you know of a good algorithm for solving this problem or have any suggestions in things i should explore?

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mixture of expert

- by Hamid

Hi everybody, I'm looking for mixture of MLP(Multilayer Perceptron) expert matlab's code.could you please help me how i can find it? Thanks

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Quantum PSO and Charged PSO (PSO = Particle Swarm Optimizer)

- by The Elite Gentleman

Hi Guys I need to implement PSO's (namely charged and quantum PSO's). My questions are these: What Velocity Update strategy do each PSO's use (Synchronous or Asynchronous particle update) What social networking topology does each of the PSO's use (Von Neumann, Ring, Star, Wheel, Pyramid, Four Clusters) For now, these are my issues. All your help will be appreciated. Thanks.

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how to work with strings and integers as bit strings in python?

- by Manuel

Hello! I'm developing a Genetic Algorithm in python were chromosomes are composed of strings and integers. To apply the genetic operations, I want to convert these groups of integers and strings into bit strings. For example, if one chromosome is: ["Hello", 4, "anotherString"] I'd like it to become something like: 0100100100101001010011110011 (this is not actual translation). So... How can I do this? Chromosomes will contain the same amount of strings and integers, but this numbers can vary from one algorithm run to another. To be clear, what I want to obtain is the bit representation of each element in the chromosome concatenated. If you think this would not be the best way to apply genetic operators (such as mutation and simple crossover) just tell me! I'm open to new ideas. Thanks a lot! Manuel

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Neural Network with softmax activation

- by Cambium

This is more or less a research project for a course, and my understanding of NN is very/fairly limited, so please be patient :) ============== I am currently in the process of building a neural network that attempts to examine an input dataset and output the probability/likelihood of each classification (there are 5 different classifications). Naturally, the sum of all output nodes should add up to 1. Currently, I have two layers, and I set the hidden layer to contain 10 nodes. I came up with two different types of implementations 1) Logistic sigmoid for hidden layer activation, softmax for output activation 2) Softmax for both hidden layer and output activation I am using gradient descent to find local maximums in order to adjust the hidden nodes' weights and the output nodes' weights. I am certain in that I have this correct for sigmoid. I am less certain with softmax (or whether I can use gradient descent at all), after a bit of researching, I couldn't find the answer and decided to compute the derivative myself and obtained softmax'(x) = softmax(x) - softmax(x)^2 (this returns an column vector of size n). I have also looked into the MATLAB NN toolkit, the derivative of softmax provided by the toolkit returned a square matrix of size nxn, where the diagonal coincides with the softmax'(x) that I calculated by hand; and I am not sure how to interpret the output matrix. I ran each implementation with a learning rate of 0.001 and 1000 iterations of back propagation. However, my NN returns 0.2 (an even distribution) for all five output nodes, for any subset of the input dataset. My conclusions: o I am fairly certain that my gradient of descent is incorrectly done, but I have no idea how to fix this. o Perhaps I am not using enough hidden nodes o Perhaps I should increase the number of layers Any help would be greatly appreciated! The dataset I am working with can be found here (processed Cleveland): http://archive.ics.uci.edu/ml/datasets/Heart+Disease

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Neural Network: Handling unavailable inputs (missing or incomplete data)

- by Mike

Hopefully the last NN question you'll get from me this weekend, but here goes :) Is there a way to handle an input that you "don't always know"... so it doesn't affect the weightings somehow? Soo... if I ask someone if they are male or female and they would not like to answer, is there a way to disregard this input? Perhaps by placing it squarely in the centre? (assuming 1,0 inputs at 0.5?) Thanks

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A* (A-star) implementation in AS3

- by Bryan Hare

Hey, 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 function findShortestPath (startN:node, goalN:node) { var openSet:Array = new Array(); var closedSet:Array = new Array(); var pathFound:Boolean = false; startN.g_score = 0; startN.h_score = distFunction(startN,goalN); startN.f_score = startN.h_score; startN.fromNode = null; openSet.push (startN); var i:int = 0 for(i= 0; i< nodeArray.length; i++) { for(var j:int =0; j<nodeArray[0].length; j++) { if(!nodeArray[i][j].isPathable) { closedSet.push(nodeArray[i][j]); } } } while (openSet.length != 0) { var cNode:node = openSet.shift(); if (cNode == goalN) { resolvePath (cNode); return true; } closedSet.push (cNode); for (i= 0; i < cNode.dirArray.length; i++) { var neighborNode:node = cNode.nodeArray[cNode.dirArray[i]]; if (!(closedSet.indexOf(neighborNode) == -1)) { continue; } neighborNode.fromNode = cNode; var tenativeg_score:Number = cNode.gscore + distFunction(neighborNode.fromNode,neighborNode); if (openSet.indexOf(neighborNode) == -1) { neighborNode.g_score = neighborNode.fromNode.g_score + distFunction(neighborNode,cNode); if (cNode.dirArray[i] >= 4) { neighborNode.g_score -= 4; } neighborNode.h_score=distFunction(neighborNode,goalN); neighborNode.f_score=neighborNode.g_score+neighborNode.h_score; insertIntoPQ (neighborNode, openSet); //trace(" F Score of neighbor: " + neighborNode.f_score + " H score of Neighbor: " + neighborNode.h_score + " G_score or neighbor: " +neighborNode.g_score); } else if (tenativeg_score <= neighborNode.g_score) { neighborNode.fromNode=cNode; neighborNode.g_score=cNode.g_score+distFunction(neighborNode,cNode); if (cNode.dirArray[i]>=4) { neighborNode.g_score-=4; } neighborNode.f_score=neighborNode.g_score+neighborNode.h_score; openSet.splice (openSet.indexOf(neighborNode),1); //trace(" F Score of neighbor: " + neighborNode.f_score + " H score of Neighbor: " + neighborNode.h_score + " G_score or neighbor: " +neighborNode.g_score); insertIntoPQ (neighborNode, openSet); } } } trace ("fail"); return false; } Right now this function creates paths that are often not optimal or wholly inaccurate given the target and this generally happens when I have nodes that are not path able, and I am not quite sure what I am doing wrong right now. If someone could help me correct this I would appreciate it greatly. Some Notes My OpenSet is essentially a Priority Queue, so thats how I sort my nodes by cost. Here is that function function insertIntoPQ (iNode:node, pq:Array) { var inserted:Boolean=true; var iterater:int=0; while (inserted) { if (iterater==pq.length) { pq.push (iNode); inserted=false; } else if (pq[iterater].f_score >= iNode.f_score) { pq.splice (iterater,0,iNode); inserted=false; } ++iterater; } } Thanks!

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Update Rule in Temporal difference

- by Betamoo

The update rule TD(0) Q-Learning: Q(t-1) = (1-alpha) * Q(t-1) + (alpha) * (Reward(t-1) + gamma* Max( Q(t) ) ) Then take either the current best action (to optimize) or a random action (to explorer) Where MaxNextQ is the maximum Q that can be got in the next state... But in TD(1) I think update rule will be: Q(t-2) = (1-alpha) * Q(t-2) + (alpha) * (Reward(t-2) + gamma * Reward(t-1) + gamma * gamma * Max( Q(t) ) ) My question: The term gamma * Reward(t-1) means that I will always take my best action at t-1 .. which I think will prevent exploring.. Can someone give me a hint? Thanks

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Pong: How does the paddle know where the ball will hit?

- by Roflcoptr

After implementing Pacman and Snake I'm implementing the next very very classic game: Pong. The implementation is really simple, but I just have one little problem remaining. When one of the paddle (I'm not sure if it is called paddle) is controlled by the computer, I have trouble to position it at the correct position. The ball has a current position, a speed (which for now is constant) and a direction angle. So I could calculate the position where it will hit the side of the computer controlled paddle. And so Icould position the paddle right there. But however in the real game, there is a probability that the computer's paddle will miss the ball. How can I implement this probability? If I only use a probability of lets say 0.5 that the computer's paddle will hit the ball, the problem is solved, but I think it isn't that simple. From the original game I think the probability depends on the distance between the current paddle position and the position the ball will hit the border. Does anybody have any hints how exactly this is calculated?

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How can I make this Java code run faster?

- by Martin Wiboe

Hello all, I am trying to make a Java port of a simple feed-forward neural network. This obviously involves lots of numeric calculations, so I am trying to optimize my central loop as much as possible. The results should be correct within the limits of the float data type. My current code looks as follows (error handling & initialization removed): /** * Simple implementation of a feedforward neural network. The network supports * including a bias neuron with a constant output of 1.0 and weighted synapses * to hidden and output layers. * * @author Martin Wiboe */ public class FeedForwardNetwork { private final int outputNeurons; // No of neurons in output layer private final int inputNeurons; // No of neurons in input layer private int largestLayerNeurons; // No of neurons in largest layer private final int numberLayers; // No of layers private final int[] neuronCounts; // Neuron count in each layer, 0 is input // layer. private final float[][][] fWeights; // Weights between neurons. // fWeight[fromLayer][fromNeuron][toNeuron] // is the weight from fromNeuron in // fromLayer to toNeuron in layer // fromLayer+1. private float[][] neuronOutput; // Temporary storage of output from previous layer public float[] compute(float[] input) { // Copy input values to input layer output for (int i = 0; i < inputNeurons; i++) { neuronOutput[0][i] = input[i]; } // Loop through layers for (int layer = 1; layer < numberLayers; layer++) { // Loop over neurons in the layer and determine weighted input sum for (int neuron = 0; neuron < neuronCounts[layer]; neuron++) { // Bias neuron is the last neuron in the previous layer int biasNeuron = neuronCounts[layer - 1]; // Get weighted input from bias neuron - output is always 1.0 float activation = 1.0F * fWeights[layer - 1][biasNeuron][neuron]; // Get weighted inputs from rest of neurons in previous layer for (int inputNeuron = 0; inputNeuron < biasNeuron; inputNeuron++) { activation += neuronOutput[layer-1][inputNeuron] * fWeights[layer - 1][inputNeuron][neuron]; } // Store neuron output for next round of computation neuronOutput[layer][neuron] = sigmoid(activation); } } // Return output from network = output from last layer float[] result = new float[outputNeurons]; for (int i = 0; i < outputNeurons; i++) result[i] = neuronOutput[numberLayers - 1][i]; return result; } private final static float sigmoid(final float input) { return (float) (1.0F / (1.0F + Math.exp(-1.0F * input))); } } I am running the JVM with the -server option, and as of now my code is between 25% and 50% slower than similar C code. What can I do to improve this situation? Thank you, Martin Wiboe

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Annoying Captcha >> How to programm a form that can SMELL difference between human and robot?

- by Sam

Hi folks. On the comment of my old form needing a CAPTHA, I felt I share my problem, perhaps you recognize it and find its time we had better solutions: FACTUAL PROBLEM I know most of my clients (typical age= 40~60) hate CAPTCHA things. Now, I myself always feel like a robot, when I have to sueeze my eyes and fill in the strange letters from the Capcha... Sometimes I fail! Go back etc. Turnoff. I mean comon its 2011, shouldnt the forms have better A.I. by now? MY NEW IDEA (please dont laugh) Ive thought about it and this is my idea's to tell difference between human and robot: My idea is to give credibility points. 100 points = human 0% = robot. require real human mouse movements require mousemovements that dont follow any mathematical pattern require non-instantaneous reading delays, between load and first input in form when typing in form, delays are measured between letters and words approve as human when typical human behaviour measured (deleting, rephrasing etc) dont allow instant pasting or all fields give points for real keyboard pressures retract points for credibility when hyperlinks in form Test wether fake email field (invisible by human) is populated (suggested by Tomalak) when more than 75% human cretibility, allow to be sent without captcha when less than 25% human crecibility, force captcha puzzle to be sure Could we write a A.I. PHP that replaces the human-annoying capthas, meanwhile stopping most spamservers filing in the data? Not only for the fun of it, but also actually to provide a 99% better alternative than CAPTHCA's. Imagine the userfriendlyness of your forms! Your site distinguishing itself from others, showing your audience your sites KNOWS the difference between a robot and a human. Imagine the advangage. I am trying to capture the essense of that distinguishing edge. PROGRAMMING QUESTION: 1) Are such things possible to programm? 2) If so how would you start such programm? 3) Are there already very good working solutions available elsewhere? 4) If it isn't so hard, your are welcome to share your answer/solutions below. 5) upon completion of hints and new ideas, could this page be the start of a new AI captcha, OR should I forget about it and just go with the flow, forget about the whole AI dream, and use captcha like everyone else.

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Prolog: Not executing code as expected.

- by Louis

Basically I am attempting to have an AI agent navigate a world based on given percepts. My issue is handling how the agent moves. Basically, I have created find_action/4 such that we pass in the percepts, action, current cell, and the direction the agent is facing. As it stands the entire code looks like: http://wesnoth.pastebin.com/kdNvzZ6Y My issue is mainly with lines 102 to 106. Basically, in it's current form the code does not work and the find_action is skipped even when the agent is in fact facing right (I have verified this). This broken code is as follows: % If we are headed right, take a left turn find_action([_, _, _, _, _], Action, _, right) :- retractall(facing(_)), assert(facing(up)), Action = turnleft . However, after some experimentation I have concluded that the following works: % If we are headed right, take a left turn find_action([_, _, _, _, _], Action, _, _) :- facing(right), retractall(facing(_)), assert(facing(up)), Action = turnleft . I am not entire sure why this is. I've attempted to create several identical find_action's as well, each checking a different direction using the facing(_) format, however swipl does not like this and throws an error. Any help would be greatly appreciated.

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Artifical neural networks height-weight problem

- by hammid1981

i plan to use neurodotnet for my phd thesis, but before that i just want to build some small solutions to get used to the dll structure. the first problem that i want to model using backward propagation is height-weight ratio. I have some height and weight data, i want to train my NN so that if i put in some weight then i should get correct height as a output. i have 1 input 1 hidden and 1 output layer. Now here is first of many things i cant get around :) 1. my height data is in form of 1.422, 1.5422 ... etc and the corresponding weight data is 90 95, but the NN takes the input as 0/1 or -1/1 and given the output in the same range. how to address this problem

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TicTacToe AI Making Incorrect Decisions

- by Chris Douglass

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 for my first time trying an AI. Now when I first implemented the AI, I went back and added two fields to each node upon generation: the # of times X will win & the # of times O will win in all children below that node. I figured the best solution was to simply have my AI on each move choose and go down the subtree where it wins the most times. Then I discovered that while it plays perfect most of the time, I found ways where I could beat it. It wasn't a problem with my code, simply a problem with the way I had the AI choose it's path. Then I decided to have it choose the tree with either the maximum wins for the computer or the maximum losses for the human, whichever was more. This made it perform BETTER, but still not perfect. I could still beat it. So I have two ideas and I'm hoping for input on which is better: 1) Instead of maximizing the wins or losses, instead I could assign values of 1 for a win, 0 for a draw, and -1 for a loss. Then choosing the tree with the highest value will be the best move because that next node can't be a move that results in a loss. It's an easy change in the board generation, but it retains the same search space and memory usage. Or... 2) During board generation, if there is a board such that either X or O will win in their next move, only the child that prevents that win will be generated. No other child nodes will be considered, and then generation will proceed as normal after that. It shrinks the size of the tree, but then I have to implement an algorithm to determine if there is a one move win and I think that can only be done in linear time (making board generation a lot slower I think?) Which is better, or is there an even better solution?

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Astar implementation in AS3

- by Bryan Hare

Hey, 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 function findShortestPath (startN:node, goalN:node) { var openSet:Array = new Array(); var closedSet:Array = new Array(); var pathFound:Boolean = false; startN.g_score = 0; startN.h_score = distFunction(startN,goalN); startN.f_score = startN.h_score; startN.fromNode = null; openSet.push (startN); var i:int = 0 for(i= 0; i< nodeArray.length; i++) { for(var j:int =0; j<nodeArray[0].length; j++) { if(!nodeArray[i][j].isPathable) { closedSet.push(nodeArray[i][j]); } } } while (openSet.length != 0) { var cNode:node = openSet.shift(); if (cNode == goalN) { resolvePath (cNode); return true; } closedSet.push (cNode); for (i= 0; i < cNode.dirArray.length; i++) { var neighborNode:node = cNode.nodeArray[cNode.dirArray[i]]; if (!(closedSet.indexOf(neighborNode) == -1)) { continue; } neighborNode.fromNode = cNode; var tenativeg_score:Number = cNode.gscore + distFunction(neighborNode.fromNode,neighborNode); if (openSet.indexOf(neighborNode) == -1) { neighborNode.g_score = neighborNode.fromNode.g_score + distFunction(neighborNode,cNode); if (cNode.dirArray[i] >= 4) { neighborNode.g_score -= 4; } neighborNode.h_score=distFunction(neighborNode,goalN); neighborNode.f_score=neighborNode.g_score+neighborNode.h_score; insertIntoPQ (neighborNode, openSet); //trace(" F Score of neighbor: " + neighborNode.f_score + " H score of Neighbor: " + neighborNode.h_score + " G_score or neighbor: " +neighborNode.g_score); } else if (tenativeg_score <= neighborNode.g_score) { neighborNode.fromNode=cNode; neighborNode.g_score=cNode.g_score+distFunction(neighborNode,cNode); if (cNode.dirArray[i]>=4) { neighborNode.g_score-=4; } neighborNode.f_score=neighborNode.g_score+neighborNode.h_score; openSet.splice (openSet.indexOf(neighborNode),1); //trace(" F Score of neighbor: " + neighborNode.f_score + " H score of Neighbor: " + neighborNode.h_score + " G_score or neighbor: " +neighborNode.g_score); insertIntoPQ (neighborNode, openSet); } } } trace ("fail"); return false; } Right now this function creates paths that are often not optimal or wholly inaccurate given the target and this generally happens when I have nodes that are not path able, and I am not quite sure what I am doing wrong right now. If someone could help me correct this I would appreciate it greatly. Some Notes My OpenSet is essentially a Priority Queue, so thats how I sort my nodes by cost. Here is that function function insertIntoPQ (iNode:node, pq:Array) { var inserted:Boolean=true; var iterater:int=0; while (inserted) { if (iterater==pq.length) { pq.push (iNode); inserted=false; } else if (pq[iterater].f_score >= iNode.f_score) { pq.splice (iterater,0,iNode); inserted=false; } ++iterater; } } Thanks!

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Using Hidden Markov Model for designing AI mp3 player

- by Casper Slynge

Hey guys. Im working on an assignment, where I want to design an AI for a mp3 player. The AI must be trained and designed with the use of a HMM method. The mp3 player shall have the functionality of adapting to its user, by analyzing incoming biological sensor data, and from this data the mp3 player will choose a genre for the next song. Given in the assignment is 14 samples of data: One sample consist of Heart Rate, Respiration, Skin Conductivity, Activity and finally the output genre. Below is the 14 samples of data, just for you to get an impression of what im talking about. Sample HR RSP SC Activity Genre S1 Medium Low High Low Rock S2 High Low Medium High Rock S3 High High Medium Low Classic S4 High Medium Low Medium Classic S5 Medium Medium Low Low Classic S6 Medium Low High High Rock S7 Medium High Medium Low Classic S8 High Medium High Low Rock S9 High High Low Low Classic S10 Medium Medium Medium Low Classic S11 Medium Medium High High Rock S12 Low Medium Medium High Classic S13 Medium High Low Low Classic S14 High Low Medium High Rock My time of work regarding HMM is quite low, so my question to you is if I got the right angle on the assignment. I have three different states for each sensor: Low, Medium, High. Two observations/output symbols: Rock, Classic In my own opinion I see my start probabilities as the weightened factors for either a Low, Medium or High state in the Heart Rate. So the ideal solution for the AI is that it will learn these 14 sets of samples. And when a users sensor input is received, the AI will compare the combination of states for all four sensors, with the already memorized samples. If there exist a matching combination, the AI will choose the genre, and if not it will choose a genre according to the weightened transition probabilities, while simultaniously updating the transition probabilities with the new data. Is this a right approach to take, or am I missing something ? Is there another way to determine the output probability (read about Maximum likelihood estimation by EM, but dont understand the concept)? Best regards, Casper

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Is F# a good language for card game AI?

- by Anthony Brien

I'm writing a Mahjong Game in C# (the Chinese traditional game, not the solitaire kind). While writing the code for the bot player's AI, I'm wondering if a functional language like F# would be a more suitable language than what I currently use which is C# with a lot of Linq. I don't know much about F# which is why I ask here. To illustrate what I try to solve, here's a quick summary of Mahjong: Mahjong plays a bit like Gin Rummy. You have 13 tiles in your hand, and each turn, you draw a tile and discard another one, trying to improve your hand towards a winning Mahjong hand, which consists or 4 sets and a pair. Sets can be a 3 of a kind (pungs), 4 of a kind (kongs) or a sequence of 3 consecutive tiles (chows). You can also steal another player's discard, if it can complete one of your sets. The code I had to write to detect if the bot can declare 3 consecutive tiles set (chow) is pretty tedious. I have to find all the unique tiles in the hand, and then start checking if there's a sequence of 3 tiles that contain that one in the hand. Detecting if the bot can go Mahjong is even more complicated since it's a combination of detecting if there's 4 sets and a pair in his hand. And that's just a standard Mahjong hand. There's also numerous "special" hands that break those rules but are still a Mahjong hand. For example, "13 unique wonders" consists of 13 specific tiles, "Jade Empire" consists of only tiles colored green, etc. In a perfect world, I'd love to be able to just state the 'rules' of Mahjong, and have the language be able to match a set of 13 tiles against those rules to retrieve which rules it fulfills, for example, checking if it's a Mahjong hand or if it includes a 4 of a kind. Is this something F#'s pattern matching feature can help solve?

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