So I am trying to make an online webpage board game hybrid of chess and Go where the player clicks on the board to place his/her pieces. My problem is, there are a lot of algorithms that come into play in order to make such a board game possible, that are difficult to write out myself. There are good npm packages that already have a lot of that sorted out (I'm thinking primarily of godash), but in order to use the algorithms the packages provide I believe I have to make some sort of data representation of the game, pass the data to the back-end, use the algorithm on it, and return the result to front-end.
The issue is that, just like chess, the game would require the player to make moves on the board in a relatively short amount of time. The question is, if I decided to make this API call with every move the player makes, would that make my game too slow/unresponsive? Do I have to knuckle down and code those algorithms in front-end so that the game isn't too slow?
You should look into the Websocket API. You can establish a session between your client and server without having to call your server each time a user plays
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I have started a social networking app and there is one user who won't stop uploading images of woman, who, well, are up to some sexual activities. He additionally adds offensive captions to them.
My question: how can I detect adult content in images and text and block them from my app? I think this is a problem that most people face who are making any kind of open networking app. It would be great if the solution was as fast and low-priced as possible.
Implement a system which essentially stores {256-sha image hash, human rating, computer rating} into a database.
Create an interface for the human rating and the computer rating which can judge and categorize images as well as an interface in your software which can use that information on how to handle such images.
Choose a tool, likely a convolutional neural network based algorithm, with an easy to use api. Here's a random result from searching: https://imagga.com/solutions/adult-content-moderation.html
Put everything together and you should have a system which can automatically guess how to handle images, but also allows you to iterate through them which both corrects the database as well as trains the rating algorithm which trains based on the existing human produced data.
Note: The status of an image is not permanent by the software unless a human rates it. Whenever one is accessed, the latest state of the image detection decides on it. If this happens far too frequently to support, then associate a time buffer with the image so that it doesn't re-rate it often.
Update: The advantages of this custom solution is that you can control things to work the way you want. You can define the rating system and how to handle the situation as well as governance over whatever set of trained algorithms you are using. You always have the final say and you can see what is going on at all times. The catch is that you would need to implement this software as an extension to your project.
Not easily, it would require machine learning techniques and a ton of training. Not to mention, all modern techniques can easily be tricked.
There are a few moderation solutions, but they aren't ideal.
First, you could ban them. Not the best, as they could make another account, but it means that they have to make another email for it.
Second, you could isolate him. I forget exactly how it works, but the idea is that they still think that they are posting on your app, but none of their content gets propagated to other people.
I don't know the legality of either of these, its all up to your terms and conditions. But AI is not really a good option, especially if your app were to need to scale.
I am designing a bot to play Texas Hold'Em Poker on tables of up to ten players, and the design includes a few feed forward neural networks (FFNN). These neural nets each have 8 to 12 inputs, 2 to 6 outputs, and 1 or 2 hidden layers, so there are a few hundred weights that I have to optimize. My main issue with training through back propagation is getting enough training data. I play poker in my spare time, but not enough to gather data on my own. I have looked into purchasing a few million hands off of a poker site, but I don't think my wallet will be very happy with me if I do... So, I have decided on approaching this by designing a genetic algorithm. I have seen examples of FFNNs being trained to play games like Super Mario and Tetris using genetic algorithms, but never for a game like poker, so I want to know if this is a viable approach to training my bot.
First, let me give a little background information (this may be confusing if you are unfamiliar with poker). I have a system in place that allows the bot to put its opponents on a specific range of hands so that it can make intelligent decisions accordingly, but it relies entirely on accurate output from three different neural networks:
NN_1) This determines how likely it is that an opponent is a) playing the actual value of his hand, b) bluffing, or c) playing a hand with the potential to become stronger later on.
NN_2) This assumes the opponent is playing the actual value of his hand and outputs the likely strength. It represents option (a) from the first neural net.
NN_3) This does the same thing as NN_2 but instead assumes the opponent is bluffing, representing option (b).
Then I have an algorithm for option (c) that does not use a FFNN. The outputs for (a), (b), and (c) are then combined based on the output from NN_1 to update my opponent's range.
Whenever the bot is faced with a decision (i.e. should it fold, call, or raise?), it calculates which is most profitable based on its opponents' hand ranges and how they are likely to respond to different bet sizes. This is where the fourth and final neural net comes in. It takes inputs based on properties unique to each player and the state of the table, and it outputs the likelihood of the opponent folding, calling, or raising.
The bot will also have a value for aggression (how likely it is to raise instead of call) and its opening range (which hands to play pre-flop). These four neural networks and two values will define each generation of bots in my genetic algorithm.
Here is my plan for training:
I will be simulating multiple large tournaments with 10n initial bots each with random values for everything. For the first few dozen tournaments, they will all be placed on tables of 10. They will play until either one bot is left or they play, say, 1,000 hands. If they reach that hand limit, the remaining bots will instantly go all-in every hand until one is left. After each table has completed, the most accurate FFNNs will be placed in the winning bot that will move on to the next round (even if the bot containing the best FFNN was not the winner). The winning bot will retain its aggression and opening range values. The tournament ends when only 100 bots remain, and random variations on those bots will generate the players for the next tournament. I'm assuming the first few tournaments will be complete chaos, so I don't want to narrow down my options too much early on.
If by some miracle, the bots actually develop a profitable, or at least somewhat coherent, strategy (I will check for this periodically), I will begin decreasing the amount of variation between bots. Anyone who plays poker could tell you that there are different types of players each with different strategies. I want to make sure that I am allowing enough room for different strategies to develop throughout this process. Then I may develop some sort of "super bot" that can switch between those different strategies if one is failing.
So, are there any glaring issue with this approach? If so, how would you recommend fixing them? Do you have any advice for speeding up this process or increasing my chances of success? I just want to make sure I'm not about to waste hundreds of hours on something doomed to fail. Also, if this site is not the correct place to be asking this question, please refer me to another website before flagging this. I would really appreciate it. Thanks all!
It will be difficult to use ANN for poker bot. It is better to think for expert system. You can use odds calculator to have numerical evaluation of the hand strength and after that expert system for money management (risk management). ANNs are good in other problems.
EDIT
I'm completely rewriting this since I realized it wasn't very clear what I needed.
So, I'm going to implement an online game. The idea is quite simple. All players have to answer the same set of questions that is downloaded from the server. To answer each question the player has a given amount of time that depends on question difficulty. The questions are presented one at a time. After the time to answer the current question elapses the next one is presented to the player. After the last question the client should display the leaderboard with the scores of all currently online players. The leaderboard is (of course) computed on server and clients should download it when the game finishes.
OK that's the idea. What I require is some suggestions on how to implement the whole client-server communication. I don't need details but just some ideas. Most important I'm not sure how client-server time-sync could work. It's important that all players have the same amount of time to answer each question. I also have a solution that is very simple but I'm not sure about possible pitfalls. What I have in mind is that when the player first connects (or when a new game starts) the client downloads the whole list of questions for current game. Also some time-sync messages are exchanged to get current game time. Then after questions and time-sync are known at the client, a local timer is started and the game runs completely offline. When the game finishes each client sends its own score/result to the server. When the leaderboard is ready the server sends it back to all clients. Once again a local timer could be used to know when a new game is starting and to download the new list of questions.
Please post your suggestions and comment my solution. Thanks
I want to make a game card battle based game. In this cards have specific attributes which can increase player's hp/attack/defense or attack an enemy to reduce his hp/attack/defense
I am trying to make an AI for this game. AI has to predict which card it will select on the basis of current situations like AI's hp/attack/defense and Enemy's hp/attack/defense. Since AI cannot see enemy's card hence it cannot predict future moves.
I searched few techniques of AI like minmax but I think minmax will not be suitable since AI cannot predict any future moves.
I am searching for a technique which is very flexible so that i can add a large variety of cards later.
Can you please suggest a technique for such game.
Thanks
This isn't an ActionScript 3 topic per se but I do think it's rather interesting.
First I'd suggest picking up Yu-Gi-Oh's Stardust Accelerator World Championship 2009 for the Nintendo DS or a comparable game.
The game has a fairly advanced computer AI system that not only deals with expected advantage or disadvantage in terms of hit points but also card advantage and combos. If you're taking on a challenge like this, I definately recommend you do the required research (plus, when playing video games is research, who can complain?)
My suggestion for building an AI is as follows:
As the computer decides its move, create an array of Move objects. Then have it create a new Move object for each possible Move that it can see.
For each move object, calculate how much less HP the opponent will have, how many cards they will still have, how many creatures,etc.
Have the computer decide what's most important (more damage, more card advantage) and have it play that move.
More sophisticated AI's will also think several turns in advance and perhaps "see" moves that others do not.
I suggest you look at this game of Reversi I built a few weeks back for fun in Flash. This has a very basic AI implemented, but the basics could be applied to your situation.
Basically, the way that game works is after each move (player or CPU, so I can determine if the player made the right move in comparison to what the CPU would have made), I create a Vector of each possible legal move. I then decide which move provides the highest score change, and set that as best move. However, I also check to see if the move would result in the other player having access to a corner (if you've never played, the player who grabs the corners generally wins). If it does, I tell the CPU to avoid that move and check the second best move and so on. The end result is a CPU who can actually put up a fight.
Keep in mind that this is just a single afternoon of work (for the entire game, from the crappy GUI to the functionality to the AI) so it is very basic and I could do things like run future possible moves through the check sequence as well. Fun fact, though, my moves (which I based the AI on obviously) are the ones the CPU would make nearly 80% of the time. The only time that does not hold true is when I play the game like you would Chess, where your move is made solely to position a move four turns down the line
For your game, you have a ton of variables to consider and not a single point scale as I had. I would suggest listing out each thing and applying a point value to each thing so you can apply importance values to each one. I did something similar for a caching system that automatically determines what is the most important file to keep based on age, usage, size, etc. You then look at each card in the CPU's hand, calculate what each card's value is and play that card (assuming it is legal to do so, of course).
Once you figure that out, you can look into things like what each move could do in the next turn (i.e. "damage" values for each move). And once that is done, you could add functionality to it that would let the CPU make strategic moves that would allow them to use a more powerful card or perform a "finishing" move or however it works in the end.
Again, though, keep it to a simple point based system and keep going from there. You need something you can physically compare, so sticking to a point based system makes that simple.
I apologize for the length of this answer, but I hope it helps in some way.
I want to make a 2 player pong game that uses websockets and node.js server. socket.io is used on both client and server. So far, my only experience is creating a chat application.
This is my first attempt at a multiplayer game so I'm not so familiar with network gaming. Should the server keep track of:
Every position the ball is at and how often or when?
player movement, player move left or right, what if I press and hold for awhile, how do I handle this? Should I send like a pressHoldStartPosition and pressHoldStopPosition? I guess this is easy if I only allow pressing but not holding down.
My thoughts:
When the ball hits a player, the client calculates velocity, start and end position and the other client should perform the correct animation from that.
No idea.
The clients calculates - nothing - unless you want it to predict next game step* (server anwser). The whole game runs on server which is one and only trustworthy source of data and calculations. In pong like games where there is less than 10 objects you can send data quite often (50ms interval will do). For a ball I'd send [x, y, velocity, or angle], for a paddles [x, y].
Then interpolate (animate in time - 50ms) between old (x,y on client) and new (x,y which you just got from server).
Don't send milion copies of - player pressed up - rather send one packet each period of time (100ms?) containing informations like "player is pressing up" for 100ms, or "player set position" to (32, 100) then check server-side was this move possible and accept it or reject.
Maybe this thread will help with your adventure:
Multiplayer JavaScript game built with Node.JS - Separating players
[*1] preditction is a lag compensation mechanism which you can implement later. In simplified way it means that server and client shares some of game logic code and when client has no current data from server it tries to simulate what is happening in the real game.
You are going to find books, upon books, about gaming networking.
In a brief description, here is what I find an interesting implementation:
The server and the client calculate the physics of the game. The server will be a master, and have a correct state of all the entities, while the client will attempt to "predict" the state of the entities and keep a smooth animation.
The client will get updated the correct state of entities from server periodically. In some games you might see entities suddenly teleport, and that is because client predicted position does not match the server actual position.
An entity state, such as the ball, or the players, usually contains information such as the position, velocity, button state (key down/key up).
Besides that, it is letting your imagination go wild and testing for the best solution and seeing what works. There are a lot to take into consideration, such as player latency and bandwidth.
There is a stackexchange site dedicated to game development: https://gamedev.stackexchange.com/
Best practice is to only send updates that the clients need to know about. Also, generally you send the result of user actions and not the actions themselves. Things that can be calculated (physics) don't need to be sent, except for periodic sync points to account for floating point errors that accumulate error over time and to sync with remote user actions. This also makes the game seem more interactive and covers some of the stutter that results from network latency and jitter.
The main downside to this model is that if you have high network latency or packet drops, you will get the diverging timeline effect when the physics calculation continues and suddenly you get an update from the remote user indicating that they did something to effect the physics that we hadn't caught with yet. But this is a standard problem in most network games and the alternatives are worse for most types of real-time games.