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• RTS AI: where to start?

  - by awegawef

  I'd like to begin tinkering around with an RTS AI, but I'm having trouble finding a good environment to work with, ie a game that has been already created. I have looked at Spring RTS and Bos Wars, but they don't seem to be conducive to creating simple examples. I am not totally opposed to writing my own game environment, it would just take a long time. Does anyone have a suggestion as to how I can get my feet wet without programming my own game?

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• Evolutionary Algorithms: Optimal Repopulation Breakdowns

  - by Brian MacKay

  It's really all in the title, but here's a breakdown for anyone who is interested in Evolutionary Algorithms: In an EA, the basic premise is that you randomly generate a certain number of organisms (which are really just sets of parameters), run them against a problem, and then let the top performers survive. You then repopulate with a combination of crossbreeds of the survivors, mutations of the survivors, and also a certain number of new random organisms. Do that several thousand times, and efficient organisms arise. Some people also do things like introduce multiple "islands" of organisms, which are seperate populations that are allowed to crossbreed once in awhile. So, my question is: what are the optimal repopulation percentages? I have been keeping the top 10% performers, and repopulating with 30% crossbreeds and 30% mutations. The remaining 30% is for new organisms. I have also tried out the multiple island theory, and I'm interested in your results on that as well. It is not lost on me that this is exactly the type of problem an EA could solve. Are you aware of anyone trying that? Thanks in advance!

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• ai: Determining what tests to run to get most useful data

  - by Sai Emrys

  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 total feature space is approximately 50 million items (URLs) for any given test, I can only query approx. 0.2% of that space I can only make the decision of what to query, based on results so far, ~10-30 times, and must do so in <~100ms (though I can take much longer to do post-processing, relevant aggregation, etc) getting the AI's probability ranking of categories based on results so far is mildly expensive; ideally the decision will depend mostly on a few cheap sql queries I have training data that can say authoritatively that any two feature vectors are the same category but not that they are different (people sometimes forget their codes and use new ones, thereby making a new user id) I need an algorithm to determine what features (sites) are most likely to have a high ROI to query (i.e. to better discriminate between plausible-so-far categories [users], and to increase certainty that it's any given one). This needs to take into balance exploitation (test based on prior test data) and exploration (test stuff that's not been tested enough to find out how it performs). There's another question that deals with a priori ranking; this one is specifically about a posteriori ranking based on results gathered so far. Right now, I have little enough data that I can just always test everything that anyone else has ever gotten a hit for, but eventually that won't be the case, at which point this problem will need to be solved. I imagine that this is a fairly standard problem in AI - having a cheap heuristic for what expensive queries to make - but it wasn't covered in my AI class, so I don't actually know whether there's a standard answer. So, relevant reading that's not too math-heavy would be helpful, as well as suggestions for particular algorithms. What's a good way to approach this problem?

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• Tic-Tac-Toe AI: How to Make the Tree?

  - by cam

  I'm having a huge block trying to understand "trees" while making a Tic-Tac-Toe bot. I understand the concept, but I can't figure out to implement them. Can someone show me an example of how a tree should be generated for such a case? Or a good tutorial on generating trees? I guess the hard part is generating partial trees. I know how to implement generating a whole tree, but not parts of it.

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• Static Evaluation Function for Checkers

  - by Kamikaze

  Hi all! I'm trying to write an evaluation function for a game of checkers that I'm developing but I can't find the right documentation. I've read several documents on the web witch describe different techniques for either writing one or letting the computer find it(using genetic algorithms or Bayesian learning) but they're too complicated for a novice like me. All documents pointed a reference to "Some studies in machine learning using the game of checkers" by A.L.Samuel but I couldn't get my hands on it yet :(. I've only read the follow up "Some studies in machine learning using the game of checkers -II" and found some good info there, but it doesn't explain what the eval parameters mean (I think I don't have the whole article). Thanks for your help!

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• Find optimal 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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• Applications for the Church Programming Language

  - by Chris S

  Has anyone worked with the programming language Church? Can anyone recommend practical applications? I just discovered it, and while it sounds like it addresses some long-standing problems in AI and machine-learning, I'm sceptical. I had never heard of it, and was surprised to find it's actually been around for a few years, having been announced in the paper Church: a language for generative models.

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• Implementing crossover in genetic programming

  - by Name

  Hi, I'm writing a genetic programming (GP) system (in C but that's a minor detail). I've read a lot of the literature (Koza, Poli, Langdon, Banzhaf, Brameier, et al) but there are some implementation details I've never seen explained. For example: I'm using a steady state population rather than a generational approach, primarily to use all of the computer's memory rather than reserve half for the interim population. Q1. In GP, as opposed to GA, when you perform crossover you select two parents but do you create one child or two, or is that a free choice you have? Q2. In steady state GP, as opposed to a generational system, what members of the population do the children created by crossover replace? This is what I haven't seen discussed. Is it the two parents, or is it two other, randomly-selected members? I can understand if it's the latter, and that you might use negative tournament selection to choose members to replace, but would that not create premature convergence? (After a crossover event the population contains the two original parents plus two children of those parents, and two other random members get removed. Elitism is inherent.) Q3. Is there a Web forum or mailing list focused on GP? Oddly I haven't found one. Yahoo's GP group is used almost exclusively for announcements, the Poli/Langdon Field Guide forum is almost silent, and GP discussions on general/game programming sites like gamedev.net are very basic. Thanks for any help you can provide!

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• Rush Hour - Solving the game

  - by Rubys

  Rush Hour if you're not familiar with it, the game consists of a collection of cars of varying sizes, set either horizontally or vertically, on a NxM grid that has a single exit. Each car can move forward/backward in the directions it's set in, as long as another car is not blocking it. You can never change the direction of a car. There is one special car, usually it's the red one. It's set in the same row that the exit is in, and the objective of the game is to find a series of moves (a move - moving a car N steps back or forward) that will allow the red car to drive out of the maze. I've been trying to think how to solve this problem computationally, and I can really not think of any good solution. I came up with a few: Backtracking. This is pretty simple - Recursion and some more recursion until you find the answer. However, each car can be moved a few different ways, and in each game state a few cars can be moved, and the resulting game tree will be HUGE. Some sort of constraint algorithm that will take into account what needs to be moved, and work recursively somehow. This is a very rough idea, but it is an idea. Graphs? Model the game states as a graph and apply some sort of variation on a coloring algorithm, to resolve dependencies? Again, this is a very rough idea. A friend suggested genetic algorithms. This is sort of possible but not easily. I can't think of a good way to make an evaluation function, and without that we've got nothing. So the question is - How to create a program that takes a grid and the vehicle layout, and outputs a series of steps needed to get the red car out? Sub-issues: Finding some solution. Finding an optimal solution (minimal number of moves) Evaluating how good a current state is Example: How can you move the cars in this setting, so that the red car can "exit" the maze through the exit on the right?

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• Good implementations of reinforced learning?

  - by Paperino

  For an ai-class project I need to implement a reinforcement learning algorithm which beats a simple game of tetris. The game is written in Java and we have the source code. I know the basics of reinforcement learning theory but was wondering if anyone in the SO community had hands on experience with this type of thing. What would your recommended readings be for an implementation of reinforced learning in a tetris game? Are there any good open source projects that accomplish similar things that would be worth checking out? Thanks in advanced Edit: The more specific the better, but general resources about the subject are welcomed. Follow up: Thought it would be nice if I posted a followup. Here's the solution (code and writeup) I ended up with for any future students :). Paper / Code

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• Genetic Programming Online Learning

  - by Lirik

  Has anybody seen a GP implemented with online learning rather than the standard offline learning? I've done some stuff with genetic programs and I simply can't figure out what would be a good way to make the learning process online. Please let me know if you have any ideas, seen any implementations, or have any references that I can look at.

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• Neural Network problems

  - by Betamoo

  I am using an external library for Artificial Neural Networks in my project.. While testing the ANN, It gave me output of all NaN (not a number in C#) The ANN has 8 input , 5 hidden , 5 hidden , 2 output, and all activation layers are of Linear type , and it uses back-propagation, with learning rate 0.65 I used one testcase for training { -2.2, 1.3, 0.4, 0.5, 0.1, 5, 3, -5 } ,{ -0.3, 0.2 } for 1000 epoch And I tested it on { 0.2, -0.2, 5.3, 0.4, 0.5, 0, 35, 0.0 } which gave { NaN , NaN} Note: this is one example of many that produces same case... I am trying to discover whether it is a bug in the library, or an illogical configuration.. The reasons I could think of for illogical configuration: All layers should not be linear Can not have descending size layers, i.e 8-5-5-2 is bad.. Only one testcase ? Values must be in range [0,1] or [-1,1] Is any of the above reasons could be the cause of error, or there are some constraints/rules that I do not know in ANN designing..? Note: I am newbie in ANN

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• Is the board game "Go" NP complete?

  - by sharkin

  There are plenty of Chess AI's around, and evidently some are good enough to beat some of the world's greatest players. I've heard that many attempts have been made to write successful AI's for the board game Go, but so far nothing has been conceived beyond average amateur level. Could it be that the task of mathematically calculating the optimal move at any given time in Go is an NP-complete problem?

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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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• 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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• 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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• Sharepoint BDC Error: The title property of entity tblStaff is set to an invalid value

  - by Christopher Rathermel

  I am just starting to create our Business Data Catalog(s) for our practice management system and I am running into an issue w/ our staff table. Background: I am using Business Data Catalog Definition Editor to create my ADF. I am using the RevertToSelf Authentication Mode. I have tried a few other tables and they seem to work just fine thus far.. only issue is w/ the staff table. If I removed all the columns for the staff entity except the ID and a few columns for the name it actually works. So it has a problem w/ one of my columns in tblStaff. I receive this error even when I set up an ADF w/ just this one entity. So w/ no associations.. When attempting to view the record: http://servername/ssp/admin/Content/tblstaff.aspx?StaffID={0} w/ {0} replaced w/ an actual staff ID I get the following error: The title property of entity tblStaff is set to an invalid value. Things I have tried: I noticed that I do have a column in my staff table called "Title" and removed it from ADF w/ no luck... Same error.. I tried to use bdc meta man to create my ADF and I got the same error... Any ideas? Chris

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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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• When to use Genetic Algorithms and when to use Neural Networks?

  - by Neil N

  Is there a rule of thumb or set of examples to determine when Genetic Algorithms versus when to use Neural Networks to solve a problem? I know there are cases in which you can have both methods mixed, but I am looking for a high level reasoning between the two methods. EDIT: XKCD has now touched on genetic algo's:

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• Application Publisher

  - by mob1lejunkie

  Hi, I am about to finish my Android application. I would like to provide lite version and more complete paid pro version. Unfortunately, Google does not allow Australian developers to sell applications and I don't have any friends in US or UK. Does anyone know of a person/company who would publish my pro application on my behalf? Google search did not bring up any results. Thanks.

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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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• Help with Neuroph neural network

  - by user359708

  For my graduate research I am creating a neural network that trains to recognize images. I am going much more complex than just taking a grid of RGB values, downsampling, and and sending them to the input of the network, like many examples do. I actually use over 100 independently trained neural networks that detect features, such as lines, shading patterns, etc. Much more like the human eye, and it works really well so far! The problem is I have quite a bit of training data. I show it over 100 examples of what a car looks like. Then 100 examples of what a person looks like. Then over 100 of what a dog looks like, etc. This is quite a bit of training data! Currently I am running at about one week to train the network. This is kind of killing my progress, as I need to adjust and retrain. I am using Neuroph, as the low-level neural network API. I am running a dual-quadcore machine(16 cores with hyperthreading), so this should be fast. My processor percent is at only 5%. Are there any tricks on Neuroph performance? Or Java peroformance in general? Suggestions? I am a cognitive psych doctoral student, and I am decent as a programmer, but do not know a great deal about performance programming.

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