I am planning on making a note and crosses game in java for my final year college project and I was wondering what different types are AI are there that I can benefit from using in terms of shortening my code etc. Currently, I am planning on using series of if, else if, else statements but my guesses are that it will require large amount of code because I will need to check for all the possibilities for example, when checking for wins, I would do something like.
if(button1.getText().equals(button2.getText()) && button2.getText().equals(button3.getText()) && button1.getText().equals("X"))
{
wining statement.....
}
.....
.....
.....
repeat for all the possibilities of winning,
Let's see what you've got. Probably this is a duplicate of a bunch of questions though.
It seems you do not have the data structures right yet. I would go with a two dimensional array (or a one dimensional with accessor methods to look like a 2D array), so the individual (named) buttons is a bad idea imho.
After that you should check the tic-tac-toe related questions, I guess you will find some inspiration and the discussion part of your paper will have more voices, opinions. There are many ways to solve this, playing the game a few times you will realize the first user should put its sign to the middle and after that there are not many choices. If you prefer to find these rules automatically, you can use backtracking, alpa-beta cutting, neural network, almost anything that is AI related.
Good luck to this task!
Related
I have a challenge that my teacher gave to beat an army of his soldiers on a 18x24 grid, with random obstacles placed on the board. The game is turn based and I have an army of 50 soldiers, each of which needs to either move or attack on their turn.
My problem is I only have access to creating a class of soldiers to fight in this environment. Currently I have a method that evaluates the board position by looking at how many soldiers there are left from each team and does yourTeam - enemyTeam to get the current score, and I have a method that will produce the legal moves for the soldier.
I want to know how I would create a reinforcement learning agent in Java with what I have access to. If you know any ways to do this or any resources that may help that would be great. Thank you for the help!
Java is not a good language for doing math heavy computation (which is what you will need to do for RL). You could attempt to implement the Q-Learning, value-iteration or policy-iteration algorithms but I would avoid doing anything with neural networks/modern deep RL approaches here as your work load will increase dramatically.
With regard to your problem, if you are to implement one of the old-school algorithms. Think about your state and action space. I have serious concerns about the size of your action space, even with a small number of moves for each solider (say 3 - attack, move up, move down) with 50 soldiers the action space will be very large - 50^3, even this many will be difficult to deal with, any more (even 4 or 5) will send you deep into some complex topics in RL.
Other problems are - defining a good reward signal, efficiently running (potentially millions) of simulated games.
The short answer is, this is not something to be taken lightly, it would be challenging and time consuming even for someone who has experience in the field and using Java is a no-no (Python is better). Given you probably don't have long to find a good solution, I would recommend trying a different approach - planning based maybe, or hard coding a reasonable strategy.
If you still want to go ahead and read up on the topic here are some good resources:
Reinforcement Learning an Introduction (Sutton & Barto) - any edition is fine
Selected chapters in Artificial Intelligence: A Modern Approach (Russel & Norvig)
Hope this helps and sorry it may not have been the answer you we hoping for!
I've been using a formula for some time to try to find value in spreads for sports betting and done this by basically creating my own spread and comparing to what bookies offer and would like to automate the process.
I've written some code in java which will do the maths on the data I give it and I'm looking for a way to populate the input data either from a database or from an xml file I create but i'm quite new to programming.
Say for example if I pick two teams to compare. For each team I need a list of the teams they played, how many points each team scored in total, how many points each team conceded in total and how many games each team played so I can run the maths on those figures and I have no idea where to start. Could anyone help me or point me in the right direction?
It sounds like you've defined your problem (how to start), and also listed the information you need to get started (compare two teams, points, previous games, conceded points, etc). Are you sure you don't know how to start?
For a point in the right direction - I recommend creating a test case where you select two teams, give them some sample data for their previous games, points scored and conceded, and start working on the structure of your program.
This question lends itself too much to personal opinion and personal experience, and that makes it difficult to give you definitive answers without looking at any code or a program layout.
Give it your best effort and reply back with what you come up with, that will be much easier to critique and offer suggestions to.
we are working on a little Java game, based on the game Blokus.
Blokus-Manual
I'm a Java-beginner and plan to implement an advanced artificial intelligence. We already have a random AI (picks a random valid move) and an AI with a simple move-rating mechanism. We also want an AI which should be as good as possible (or at least very good ;) ).
The question is: Which AI-concept would be suitable for our purpose?
The minimax-algorithm seems to be a valid choice, but how do you adapt it to a 4-player-game? Are there better concepts for a game like blokus?
Thanks already :)
Min-max is hard to implement in a 4 player game because:
Decision tree grows exponentially, so you're going to be bounded by memory and/or computation time to a log(medMoves)=N steps. For a 4 player game, this is down to N/4. If N is 8 for example, you're only going to be able to see 2 moves ahead for each player.
Player collusion is hard to account for. In a realistic game, some players might help each other out (even if they're not on the same team). This will cause them to deviate from their personal 'maximum'.
If you want Minmax, you're going to have to do a lot of pruning to make it viable. What I would suggest is learning a few patterns so the AI would know how to react. This can be done via neural net, or reinforcement learning with a few tweaks.
These patterns could be be static (you can create the input scenario manually or programmatically), or dynamic (create all valid scenarios and randomly makes moves select the ones with the best score).
Theoretically speaking, an "as good as possible AI" is a perfect AI, which is an AI that has, at any moment in the game, full knowledge of the game state (if the full game state is not known by human players). In case of games that everyone has full game state knowledge (like Blokus), a good as possible AI is an AI that can try to predict the very best move to make (minimax here, as you said). You can also google for genetic algorithms and simulated annealing, as they are valid, depending on what you want. Also, you can use minimax for more than 2 players.
I would recommend minimax algorithm. One thing you can add to make it more efficient (meaning you should be able go more moves deep into the future) is alpha-beta pruning.
The problem with minimax search is that the number of games states it has to examine is exponential in the depth of the tree. Unfortunately, we can't eliminate the exponent, but it turns out we can effectively cut it in half.
The quote is from Chapter 5.3 of Artificial Intelligence: A Modern Approach third edition by Stuart Russel and Peter Norvig. It was holding up my monitor, and I used it in a few of my classes in college. I know people don't often reference books on SO, but it's extremely relevant. I have used it extensively, and I do really recommend it for both being understandable, and covering a wide range of AI content.
It is available on amazon for $104, or * cough cough * I'm sure you can find it online if you don't have that kind of money for a textbook floating around. Looking up the minimax algorithm and alpha beta pruning online should also get you good results.
I think the only circumstance that would make Minimax a poor option for you is if the game state is only partially observable to any given player (they don't know everything about what's going on), or if the game is non-deterministic (it has random elements). Because neither of these are the case for Blokus, I think you made an excellent choice with Minimax.
The area of AI is called Adversarial Search in the textbook (Chapter 5: Adversarial Search), so looking up more info online with that term may get you more helpful information, or help you find an example Java implementation. I do not consider this a beginner's task, but it sounds like you are up to it, if you made the game and can pick random valid moves. Keep up the good work!
In 2011, with many updates since then, a program called Pentobi
was released, and it is a very strong Blokus playing program.
The only one known to date, in fact, which is any good at all, and it
surpasses all the others by a great deal. It will beat many good human players and gives even the best a run for their money.
Its main algorithm is Monte Carlo Search Tree, but it also uses a "book" of openings and some heuristics.
There is documentation and download information at
http://pentobi.sourceforge.net/
I found that using a very simple heuristic provides a fairly intelligent player even using only 1-step look ahead. I implemented what I called "space heuristic" which takes the board state and floods it by coloring all squares adjacent to each placed piece the color of that piece. Then, the total number of colored squares is counted once the flooding terminates. The space heuristic gives a rough estimate of how much a play claims or occupies board space, and way outperforms random play. Could be combined with minimax or MCTS to get way stronger as well.
This is my first question here, if I did something wrong, tell me...
I'm currently making a draughts game in Java. In fact everything works except the AI.
The AI is at the moment single threaded, using minimax and alpha-beta pruning. This code works, I think, it's just very slow, I can only go 5 deep into my game tree.
I have a function that recieves my mainboard, a depth (starts at 0) and a maxdepth. At this maxdepth it stops, returns the player's value (-1,1 or 0) with the most pieces on the board and ends the recursive call.
If maxdepth isn't reached yet, I calculate all the possible moves, I execute them one by one, storing my changes to the mainboard in someway.
I also use alpha-beta pruning, e.g. when I found a move that can make the player win I don't bother about the next possible moves.
I calculate the next set of moves from that mainboard state recursively. I undo those changes (from point 2) when coming out of the recursive call. I store the values returned by those recursive calls and use minimax on those.
That's the situation, now I have some questions.
I'd like to go deeper into my game tree, thus I have to diminish the time it takes to calculate moves.
Is it normal that the values of the possible moves of the AI (e.g. the moves that the AI can choose between) are always 0? Or will this change if I can go deeper into the recursion? Since at this moment I can only go 5 deep (maxdepth) into my recursion because otherwise it takes way too long.
I don't know if it's usefull, but how I can convert this recursion into a multithreaded recursion. I think this can divide the working time by some value...
Can someone help me with this please?
1. Is it normal that the values of the possible moves of the AI (e.g. the moves that the AI can choose between) are always 0?
Sounds strange to me. If the number of possible moves is 0, then that player can't play his turn. This shouldn't be very common, or have I misunderstood something?
If the value you're referring to represents the "score" of that move, then obviously "always 0" would indicate that all move are equally good, which obviously doesn't make a very good AI algorithm.
2. I don't know if it's usefull, but how I can convert this recursion into a multithreaded recursion. I think this can divide the working time by some value...
I'm sure it would be very useful, especially considering that most machines have several cores these days.
What makes it complicated is your "try a move, record it, undo it, try next move" approach. This indicates that you're working with a mutable data structure, which makes it extremely complicated to paralellize the algorithm.
If I were you, I would let the bord / game state be represented by an immutable data structure. You could then let each recursive call be treated as a separate task, and use a pool of threads to process them. You would get close to maximum utilization of the CPU(s) and at the same time simplify the code considerably (by removing the whole restore-to-previous-state code).
Assuming you do indeed have several cores on your machine, this could potentially allow you to go deeper in the tree.
I would strongly recommend reading this book:
One Jump Ahead: Computer Perfection At Checkers
It will give you a deep history about computer AI in the game of Checkers and will probably given you some help with your evaluation function.
Instead of having an evaluation function that just gives 1/0/-1 for differing pieces, give a score of 100 for every regular piece and 200 for a king. Then give bonuses for piece structures. For instance, if my pieces form a safe structure that can't be captured, then I get a bonus. If my piece is all alone in the middle of the board, then I get a negative bonus. It is this richness of features for piece configurations that will allow your program to play well. The final score is the difference in the evaluation for both players.
Also, you shouldn't stop your search at a uniform depth. A quiescence search extends search until the board is "quiet". In the case of Checkers, this means that there are no forced captures on the board. If you don't do this, your program will play extremely poorly.
As others have suggested, transposition tables will do a great job of reducing the size of your search tree, although the program will run slightly slower. I would also recommend the history heuristic, which is easy to program and will greatly improve the ordering of moves in the tree. (Google history heuristic for more information on this.)
Finally, the representation of your board can make a big difference. Fast implementations of search do not make copies of the board each time a move is applied, instead they try to quickly modify the board to apply and undo moves.
(I assume by draughts you mean what we would call checkers here in the States.)
I'm not sure if I understand your scoring system inside the game tree. Are you scoring by saying, "Position scores 1 point if player has more pieces than the opponent, -1 point is player has fewer pieces, 0 points if they have the same number of pieces?"
If so, then your algorithm might just be capture averse for the first five moves, or things are working out so that all captures are balanced. I'm not deeply familiar with checkers, but it doesn't seem impossible that this is so for only five moves into the game. And if it's only 5 plies (where a ply is one player's move, rather than a complete set of opposing moves) maybe its not unusual at all.
You might want to test this by feeding in a board position where you know absolutely the right answer, perhaps something with only two checkers on the board with one in a position to capture.
As a matter of general principle, though, the board evaluation function doesn't make a lot of sense-- it ignores the difference between a piece and a crowned piece, and it treats a three piece advantage the same as a one piece advantage.
I have been struggling to think of some decent uses for things like vectors and stacks. Since I find myself best able to remember things once i've done something useful with it. I was after some short but useful applications you've found for several of the java data structures.
I'm not after any code samples, but more things that stick in your mind as 'that was a really great use of a hashmap/linked list etc' - things that i could then go on to try myself.
"Usefulness" is a subjective term, but in any case, an intuitive way to learn data structures is to use them to simulate real-life activities.
Stack
Simulate a secretary that is shredding a bunch of documents. She has -- guess what? -- a stack of documents on her desk, and she shreds them one by one by picking the top document and feeding it into the shredder, repeating this until all documents are shredded.
Her boss would intermittently come over to her desk and put a new document to shred on top of her stack.
Circular doubly linked list
Simulate kids playing in the playground. The kids stand in a circle, then each kid would -- guess what? -- link up by holding hands with the kid to the left (with the left hand) and to the right (with the right hand).
Do "Eeny, meeny, miny, moe" around the circle, say starting from the youngest kid. The "it" kid would then have to leave the circle, and the gap is closed in the most natural way, i.e. by having the two kids around the gap link up.
Restart the "Eeny, meeny, miny, moe" from the gap. Go the opposite direction on a whim. Do this until one kid remains.
Map
Dog says woof. Cow says moo. Yeah, simulate that.
A good use for a Stack would be bracket matching. Write a small program that will parse some input and report back if the bracket syntax was correct (i.e. every open bracket has a corresponding closing bracket).
How about an RPN Calculator for Stacks? Vectors can be applied to almost any problem.