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  • Why does A* path finding sometimes go in straight lines and sometimes diagonals? (Java)

    - by Relequestual
    I'm in the process of developing a simple 2d grid based sim game, and have fully functional path finding. I used the answer found in my previous question as my basis for implementing A* path finding. (http://stackoverflow.com/questions/735523/pathfinding-2d-java-game). To show you really what I'm asking, I need to show you this video screen capture that I made. I was just testing to see how the person would move to a location and back again, and this was the result... http://www.screenjelly.com/watch/Bd7d7pObyFo Different choice of path depending on the direction, an unexpected result. Any ideas?

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  • Are evolutionary algorithms and neural networks used in the same problem domains?

    - by Joe Holloway
    I am trying to get a feel for the difference between the various classes of machine-learning algorithms. I understand that the implementations of evolutionary algorithms are quite different from the implementations of neural networks. However, they both seem to be geared at determining a correlation between inputs and outputs from a potentially noisy set of training/historical data. From a qualitative perspective, are there problem domains that are better targets for neural networks as opposed to evolutionary algorithms? I've skimmed some articles that suggest using them in a complementary fashion. Is there a decent example of a use case for that? Thanks

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  • How do I create a good evaluation function for a new board game?

    - by A. Rex
    I write programs to play board game variants sometimes. The basic strategy is standard alpha-beta pruning or similar searches, sometimes augmented by the usual approaches to endgames or openings. I've mostly played around with chess variants, so when it comes time to pick my evaluation function, I use a basic chess evaluation function. However, now I am writing a program to play a completely new board game. How do I choose a good or even decent evaluation function? The main challenges are that the same pieces are always on the board, so a usual material function won't change based on position, and the game has been played less than a thousand times or so, so humans don't necessarily play it enough well yet to give insight. (PS. I considered a MoGo approach, but random games aren't likely to terminate.) Any ideas? Game details: The game is played on a 10-by-10 board with a fixed six pieces per side. The pieces have certain movement rules, and interact in certain ways, but no piece is ever captured. The goal of the game is to have enough of your pieces in certain special squares on the board. The goal of the computer program is to provide a player which is competitive with or better than current human players.

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  • Creating a smart text generator

    - by royrules22
    I'm doing this for fun (or as 4chan says "for teh lolz") and if I learn something on the way all the better. I took an AI course almost 2 years ago now and I really enjoyed it but I managed to forget everything so this is a way to refresh that. Anyway I want to be able to generate text given a set of inputs. Basically this will read forum inputs (or maybe Twitter tweets) and then generate a comment based on the learning. Now the simplest way would be to use a Markov Chain Text Generator but I want something a little bit more complex than that as the MKC basically only learns by word order (which word is more likely to appear after word x given the input text). I'm trying to see if there's something I can do to make it a little bit more smarter. For example I want it to do something like this: Learn from a large selection of posts in a message board but don't weight it too much For each post: Learn from the other comments in that post and weigh these inputs higher Generate comment and post See what other users' reaction to your post was. If good weigh it positively so you make more posts that are similar to the one made, and vice versa if negative. It's the weighing and learning from mistakes part that I'm not sure how to implement. I thought about Artificial Neural Networks (mainly because I remember enjoying that chapter) but as far as I can tell that's mainly used to classify things (i.e. given a finite set of choices [x1...xn] which x is this given input) not really generate anything. I'm not even sure if this is possible or if it is what should I go about learning/figuring out. What algorithm is best suited for this? To those worried that I will use this as a bot to spam or provide bad answers to SO, I promise that I will not use this to provide (bad) advice or to spam for profit. I definitely will not post it's nonsensical thoughts on SO. I plan to use it for my own amusement. Thanks!

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  • AI opponent car logic in car race game.

    - by ashok patidar
    hello i want to develop AI car(opponent) in car race game what should be my direction to develop them with less complexity because i don't have any idea. because the player car is moving on the scrolling track plz suggest me should i have to use relative motion or way point concept but that should also be change on the scrolling track (i.e. player car movement)

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  • Incomplete information card game

    - by binil
    I would like to develop a trick taking card game. The game is between four players, one of which is a human and the other three hands are played by the computer. Where can I read up about developing the AI for such games?

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  • Information Gain and Entropy

    - by dhorn
    I recently read this question regarding information gain and entropy. I think I have a semi-decent grasp on the main idea, but I'm curious as what to do with situations such as follows: If we have a bag of 7 coins, 1 of which is heavier than the others, and 1 of which is lighter than the others, and we know the heavier coin + the lighter coin is the same as 2 normal coins, what is the information gain associated with picking two random coins and weighing them against each other? Our goal here is to identify the two odd coins. I've been thinking this problem over for a while, and can't frame it correctly in a decision tree, or any other way for that matter. Any help? EDIT: I understand the formula for entropy and the formula for information gain. What I don't understand is how to frame this problem in a decision tree format. EDIT 2: Here is where I'm at so far: Assuming we pick two coins and they both end up weighing the same, we can assume our new chances of picking H+L come out to 1/5 * 1/4 = 1/20 , easy enough. Assuming we pick two coins and the left side is heavier. There are three different cases where this can occur: HM: Which gives us 1/2 chance of picking H and a 1/4 chance of picking L: 1/8 HL: 1/2 chance of picking high, 1/1 chance of picking low: 1/1 ML: 1/2 chance of picking low, 1/4 chance of picking high: 1/8 However, the odds of us picking HM are 1/7 * 5/6 which is 5/42 The odds of us picking HL are 1/7 * 1/6 which is 1/42 And the odds of us picking ML are 1/7 * 5/6 which is 5/42 If we weight the overall probabilities with these odds, we are given: (1/8) * (5/42) + (1/1) * (1/42) + (1/8) * (5/42) = 3/56. The same holds true for option B. option A = 3/56 option B = 3/56 option C = 1/20 However, option C should be weighted heavier because there is a 5/7 * 4/6 chance to pick two mediums. So I'm assuming from here I weight THOSE odds. I am pretty sure I've messed up somewhere along the way, but I think I'm on the right path! EDIT 3: More stuff. Assuming the scale is unbalanced, the odds are (10/11) that only one of the coins is the H or L coin, and (1/11) that both coins are H/L Therefore we can conclude: (10 / 11) * (1/2 * 1/5) and (1 / 11) * (1/2) EDIT 4: Going to go ahead and say that it is a total 4/42 increase.

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  • AI navigation around a 2d map - Avoiding obstacles.

    - by Curt Walker
    Hey there, I know my question seems pretty vague but I can't think of a better way to put it so I'll start off by explaining what I'm trying to do. I'm currently working on a project whereby I've been given a map and I'm coding a 'Critter' that should be able to navigate it's way around the map, the critter has various other functions but are not relevant to the current question. The whole program and solution is being written in C#. I can control the speed of the critter, and retrieve it's current location on the map by returning it's current X and Y position, I can also set it's direction when it collides with the terrain that blocks it. The only problem I have is that I can't think of a way to intelligently navigate my way around the map, so far I've been basing it around what direction the critter is facing when it collides with the terrain, and this is in no way a good way of moving around the map! I'm not a games programmer, and this is for a software assignment, so I have no clue on AI techniques. All I am after is a push in the right direction on how I could go about getting this critter to find it's way around any map given to me. Here's an image of the map and critters to give you an idea of what i'm talking about. Here's a link to an image of what the maps and critters look like. Map and Critter image I'm in no way looking for anyone to give me a full solution, just a push in the general direction on map navigation. Thanks in advance!

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  • Placement of defensive structures in a game

    - by Martin
    I am working on an AI bot for the game Defcon. The game has cities, with varying populations, and defensive structures with limited range. I'm trying to work out a good algorithm for placing defence towers. Cities with higher populations are more important to defend Losing a defence tower is a blow, so towers should be placed reasonably close together Towers and cities can only be placed on land So, with these three rules, we see that the best kind of placement is towers being placed in a ring around the largest population areas (although I don't want an algorithm just to blindly place a ring around the highest area of population, sometime there might be 2 sets of cities far apart, in which case the algorithm should make 2 circles, each one half my total towers). I'm wondering what kind of algorithms might be used for determining placement of towers?

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  • Automated Legal Processing

    - by Chris S
    Will it ever be possible to make legal systems quantifiable enough to process with computer algorithms? What technologies would have to be in place before this is possible? Are there any existing technologies that are already trying to accomplish this? Out of curiosity, I downloaded the text for laws in my local municipality, and tried applying some simple NLP tricks to extract rules from sentences. I had mixed results. Some sentences were very explicit (e.g. "Cars may not be left in the park overnight"), but other sentences seemed hopelessly vague (e.g. "The council's purpose is to ensure the well-being of the community"). I apologize if this is too open-ended a topic, but I've often wondered what society would look like if legal systems were based on less ambiguous language. Lawyers, and the legal process in general, are so expensive because they have to manually process a complex set of rules codified in ambiguous legal texts. If this system could be represented in software, this huge expense could potentially be eliminated, making the legal system more accessible for everyone.

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  • Beginner's resources/introductions to classification algorithms.

    - by Dirk
    Hi, everybody. I am entirely new to the topic of classification algorithms, and need a few good pointers about where to start some "serious reading". I am right now in the process of finding out, whether machine learning and automated classification algorithms could be a worthwhile thing to add to some application of mine. I already scanned through "How to Solve It: Modern heuristics" by Z. Michalewicz and D. Fogel (in particular, the chapters about linear classifiers using neuronal networks), and on the practical side, I am currently looking through the WEKA toolkit source code. My next (planned) step would be to dive into the realm of Bayesian classification algorithms. Unfortunately, I am lacking a serious theoretical foundation in this area (let alone, having used it in any way as of yet), so any hints at where to look next would be appreciated; in particular, a good introduction of available classification algorithms would be helpful. Being more a craftsman and less a theoretician, the more practical, the better... Hints, anyone?

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  • Dynamic Multiple Choice (Like a Wizard) - How would you design it? (e.g. Schema, AI model, etc.)

    - by henry74
    This question can probably be broken up into multiple questions, but here goes... In essence, I'd like to allow users to type in what they would like to do and provide a wizard-like interface to ask for information which is missing to complete a requested query. For example, let's say a user types: "What is the weather like in Springfield?" We recognize the user is interested in weather, but it could be Springfield, Il or Springfield in another state. A follow-up question would be: What Springfield did you want weather for? 1 - Springfield, Il 2 - Springfield, Wi You can probably think of a million examples where a request is missing key data or its ambiguous. Make the assumption the gist of what the user wants can be understood, but there are missing pieces of data required to complete the request. Perhaps you can take it as far back as asking what the user wants to do and "leading" them to a query. This is not AI in the sense of taking any input and truly understanding it. I'm not referring to having some way to hold a conversation with a user. It's about inferring what a user wants, checking to see if there is an applicable service to be provided, identifying the inputs needed and overlaying that on top of what's missing from the request, then asking the user for the remaining information. That's it! :-) How would you want to store the information about services? How would you go about determining what was missing from the input data? My thoughts: Use regex expressions to identify clear pieces of information. These will be matched to the parameters of a service. Figure out which parameters do not have matching data and look up the associated question for those parameters. Ask those questions and capture answers. Re-run the service passing in the newly captured data. These would be more free-form questions. For multiple choice, identify the ambiguity and search for potential matches ranked in order of likelihood (add in user history/preferences to help decide). Provide the top 3 as choices. Thoughts appreciated. Cheers, Henry

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  • Determining what action an NPC will take, when it is partially random but influenced by preferences?

    - by lala
    I want to make characters in a game perform actions that are partially random but also influenced by preferences. For instance, if a character feels angry they have a higher chance of yelling than telling a joke. So I'm thinking about how to determine which action the character will take. Here are the ideas that have come to me. Solution #1: Iterate over every possible action. For each action do a random roll, then add the preference value to that random number. The action with the highest value is the one the character takes. Solution #2: Assign a range of numbers to an action, with more likely actions having a wider range. So, if the random roll returns anywhere from 1-5, the character will tell a joke. If it returns 6-75, they will yell. And so on. Solution #3: Group all the actions and make a branching tree. Will they take a friendly action or a hostile action? The random roll (with preference values added) says hostile. Will they make a physical attack or verbal? The random roll says verbal. Keep going down the line until you reach the action. Solution #1 is the simplest, but hardly efficient. I think Solution #3 is a little more complicated, but isn't it more efficient? Does anyone have any more insight into this particular problem? Is #3 the best solution? Is there a better solution?

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  • Google analytics-style custom report builder UI

    - by gregmac
    I'm looking for a reporting engine/UI that can be integrated into a product, which has a UI along the lines of Google Analytics' Custom Reports builder. Is anyone aware of such a thing? The data is in our case is not page views/visitors/etc, but is similar in nature, in that there are limited entities or types of data, but each entity has many attributes/columns and many different ways of aggregating data (or in GA-style speak, metrics and dimensions). The analytics-style UI is very intuitive and allows many reports to be created in powerful ways, without having to know SQL. I have preference for a web-based tool (seeing that it is 2010 and this is a web app -- I mention only because it seems the vast majority of reporting tools still have only a non-web-based creation tool).

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  • Is there any self-improving compiler around?

    - by JohnIdol
    I am not aware of any self-improving compiler, but then again I am not much of a compiler-guy. Is there ANY self-improving compiler out there? Please note that I am talking about a compiler that improves itself - not a compiler that improves the code it compiles. Any pointers appreciated! Side-note: in case you're wondering why I am asking have a look at this post. Even if I agree with most of the arguments I am not too sure about the following: We have programs that can improve their code without human input now — they’re called compilers. ... hence my question.

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  • AI opponenet car logic in car race game.

    - by ashok patidar
    hello i want to develop AI car(opponent) in car race game what should be my direction to develop them with less complexity because i don't have any idea. because the player car is moving on the scrolling track plz suggest me should i have to use relative motion or way point concept but that should also be change on the scrolling track (i.e. player car movement)

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  • How to convert the output of an artificial neural network into probabilities?

    - by Mathieu Pagé
    I've read about neural network a little while ago and I understand how an ANN (especially a multilayer perceptron that learns via backpropagation) can learn to classify an event as true or false. I think there are two ways : 1) You get one output neuron. It it's value is 0.5 the events is likely true, if it's value is <=0.5 the event is likely to be false. 2) You get two output neurons, if the value of the first is than the value of the second the event is likely true and vice versa. In these case, the ANN tells you if an event is likely true or likely false. It does not tell how likely it is. Is there a way to convert this value to some odds or to directly get odds out of the ANN. I'd like to get an output like "The event has a 84% probability to be true"

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  • Natural Language Processing in Ruby

    - by Joey Robert
    I'm looking to do some sentence analysis (mostly for twitter apps) and infer some general characteristics. Are there any good natural language processing libraries for this sort of thing in Ruby? Similar to http://stackoverflow.com/questions/870460/java-is-there-a-good-natural-language-processing-library but for Ruby. I'd prefer something very general, but any leads are appreciated!

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  • Another StackOverflow website?

    - by Betamoo
    It seems that StackOverflow is more concerned about programming techniques and coding skills (which is a good thing!).. But I am asking if anyone knows another "StackcOverflow"-like site, but which is mainly concerned about Machine Learning and AI? BTW: I have asked this question after nearly a week without an answer for Question

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  • TicTacToe strategic reduction

    - by NickLarsen
    I decided to write a small program that solves TicTacToe in order to try out the effect of some pruning techniques on a trivial game. The full game tree using minimax to solve it only ends up with 549,946 possible games. With alpha-beta pruning, the number of states required to evaluate was reduced to 18,297. Then I applied a transposition table that brings the number down to 2,592. Now I want to see how low that number can go. The next enhancement I want to apply is a strategic reduction. The basic idea is to combine states that have equivalent strategic value. For instance, on the first move, if X plays first, there is nothing strategically different (assuming your opponent plays optimally) about choosing one corner instead of another. In the same situation, the same is true of the center of the walls of the board, and the center is also significant. By reducing to significant states only, you end up with only 3 states for evaluation on the first move instead of 9. This technique should be very useful since it prunes states near the top of the game tree. This idea came from the GameShrink method created by a group at CMU, only I am trying to avoid writing the general form, and just doing what is needed to apply the technique to TicTacToe. In order to achieve this, I modified my hash function (for the transposition table) to enumerate all strategically equivalent positions (using rotation and flipping functions), and to only return the lowest of the values for each board. Unfortunately now my program thinks X can force a win in 5 moves from an empty board when going first. After a long debugging session, it became apparent to me the program was always returning the move for the lowest strategically significant move (I store the last move in the transposition table as part of my state). Is there a better way I can go about adding this feature, or a simple method for determining the correct move applicable to the current situation with what I have already done?

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