I'm developing a poker game for iPhone/iPad which uses Apple's matchmaking service. I'll be using a client-server topology where the dealer is the server. With each hand there will be a new server/dealer. However, before the initial dealer/server selection, the game uses peer-to-peer topology, which leaves me with my dilemma.
How do I get all the players/peers to agree on one random peer to be the initial dealer/server, quickly and efficiently?
I'm currently troubleshooting my own method by which I have each peer broadcast a random number. After all numbers have been received, they are sorted and the peer with the lowest number is the initial dealer. However, the issues I'm having with (duplicate numbers, etc) have prompted me to find a better solution.
Any help would be greatly appreciated.
You need to learn about the Paxos algorithm (i.e., election of a leader).
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I have been digging google for 2 days looking for an explanation for algorithm that solve the pick up and delivery variant of the vehicle route problem, but, I could not find, so please either someone give me an example or point me to some resources preferably with worked examples in them.
I cam across something called saving algorithm, but could not find any resource on how to use it to solve pick up and delivery variant.
There are many variants of the vehicle routing problem, so I wouldn't be surprised if there's no tutorial material for this particular one.
If you're not comfortable with constraint programming, I'd suggest the Biased Random Key Genetic Algorithm (BRKGA) framework, which has tutorial material and several implementations. A BRKGA genome is a vector of numbers between zero and one, and the framework defines all operations on genomes except for the one that decodes a genome into a feasible solution, which can be composed with the objective function to compute fitness.
In general there is an art to choosing a good decoder. For this problem I would try this first. Define the vector length to be the number of stops (pickup or delivery). To determine the order of stops, initialize a priority queue with all of the pickups, where the genome determines the priorities. Until the queue is empty, pop the max priority stop and schedule it next. If it's a pickup, add the corresponding delivery to the queue.
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.
I want to create a functionality for suggesting connections in a social network.
In the network you can have connections and connect to other users.
I want to implement a connection suggestion functionality on the network.
I think the most basic approach to implement this is to check all my connections most occurring common connection that my user is not connected to and sugest this user to my user to connect to.
My questions is:
Is this a good basic approach for an easy connection finder?
Is there any good implementation algorithm that i can use for finding my connections most occurring user that they are connected to?
I'd try a machine learning approach for this problem.
I'll suggest two common machine learning concepts in order to solve this problem. In order for both of them to work - you need to extract features from the data (for example look at a subgraph, and friendship with each member in the subgraph is a binary feature).
The two approaches are:
Classification. In here, you are trying to find a classifier C:UserxUser->Boolean (A classifier that given two users, gives a boolean answer - should they be friends). The classification approach will require you to first manually label, or extract some classified information (A big enough set of pairs, each with a classification). The algorithm will learn this pattern, and use it to predict future inputs.
Clustering (AKA Unsupervised learning). You can try and find clusters in your graph, and suggest users to be friends with all members in their cluster.
I have to admit I never used any of these methods for friendship suggestion - so I have no idea how accurate it will be. You can use cross-validation in order to estimate the accuracy of the algorithm.
If you are interested in learning more about it - two weeks ago an on line free course has started in stanford about machine learning: https://class.coursera.org/ml-2012-002
I am developing a simple fast-paced 2 dimensional real-time multiplayer game in Flash. Players can only shoot and walk in point to move fashion. I use TCP socket connection (AS3 doesnt offer UDP).
All I want is to synchronize players' actions so Player1 could see the same on its screen as Player2,Player3... or just see close representation (position,taking damage,etc).
I know movement vector coordinates and I can easily interpolate on them using latency.
However, I can not figure out an effective way to determine how much time (T1) did it take the state update to travel client1-server-client2 and then make corrections to client2's screen based on T1. (You know, ping times may fluctuate quite a bit).I need a way to do the above-mentioned, i need way which is as fast and as accurate as posssible but not extremely sophisticated. (what algorithm should i use? what is the solution - timestamps, maybe or what? - I dont know.)
First of all, I think the server should constantly have updated information about the entire "world". All the clients send their playing actions (that is, what the player does, like movements, shootings, etc) to the server, and using compressed data.
I would divide the "world" into regions. Each player has of course a limited view, so he can't see all the world at once (luckily), thus he needs to get updates to only the regions he can see.
So the thing is:
The server generates the world, and divides it into regions.
When a player enters the world, it gets general information about the entire world and detailed information about the regions in his sight
Each action of a player that has consequences, must be sent to the server (compressed) that acquires the information. If the status change has the effect of changing one or more regions, each user interested in those regions must receive the change notification
It looks like a publish/subscribe design pattern (Observer), but
each client is the publisher and the server is the subscriber for what concerns the player status change.
the server is publisher and the clients are subscribers for what concerns the world change, but only for the regions each player is interested in. this is a particular Observer in that the subscription changes over time (regions) due to movement
I am a graph/network enthusiast and this just for my curiosity :)
I am trying to model the StackOverflow community as a graph/network. Assume that the people in the SO community are nodes and that the answers given to any of the question establishes a relationship between these nodes. The relationship can be assumed to be directed(link from answer -> question) or undirected. The graph could be weighted and that the weights of the nodes could represented number of vote-ups/downs (normalized on the scale of 0 to 1).
What kind of graph/network does one end up with at any given snapshot of time? Is it scale-free? Is it a small-world? The graph is continuously evolving over a period of time and i would like to understand its structure and dynamics.
Is there a way where can i retrieve this relationship data from - may be SO APIs or some one from SO can help me out with (sample) data?
Clarification edit:
Scale-free network: A network whose degree distribution asymptotically follows a power law Small-world: A network that has sub-networks characterized by presence of connections between almost any two nodes within them and most pairs of nodes are connected by at least one short path.
To the second part of your question:
Is there a way where can i retrieve
this relationship data from - may be
SO APIs or some one from SO can help
me out with (sample) data?
Try these questions instead. There are a lot of plans to implement an API to access SO data. Some things are in change, but there are possibilities to screen-scrape the data or access them via JSON (afaik).
Is there a guide to accessing StackOverflow data programmatically?
What would you want to see in a StackOverflow API?
Are there plans for a StackOverflow API?
Try it out. Good luck!
What kind of graph/network does one end up with at any given snapshot of time? Is it scale-free? Is it a small-world? The graph is continuously evolving over a period of time and i would like to understand its structure and dynamics.
It takes only a few links between remote clusters to turn a random network into a small world one, so it's quite likely to be small world.
As to whether it's scale free, that would require there to be a few posters with lots of answers and many with only one or two. I seem to recall Jeff saying that there were lots with only one question in one of the pod-casts; you might be better off asking the question there rather than here, as he will have the data.