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72 votes
Accepted

How does an AI like ChatGPT answer a question in a subject which it may not know?

ChatGPT is a large language model. That means it's very good at stringing together words in ways that humans tend to use them. It's able to construct sentences that are grammatically correct and sound ...
Mithical's user avatar
  • 2,915
20 votes

How do neural networks play chess?

Minimax and related algorithms are used to play chess. That is how chess programs have worked for many years (with some additions such as standard opening playbooks). They do not need to process the ...
Neil Slater's user avatar
  • 32.5k
20 votes

How does an AI like ChatGPT answer a question in a subject which it may not know?

ChatGPT does not actually know anything. But more importantly even, it does not know this fact! Hence, it does not know that it does not know. It is only good at combining text.
DrCommando's user avatar
15 votes

How does an AI like ChatGPT answer a question in a subject which it may not know?

ChatGPT and other GPT-based machine learning models don't actually know anything in the sense you're thinking of. ChatGPT is a distant descendant of Markov chain text generators such as Dissociated ...
Mark's user avatar
  • 420
12 votes

How do neural networks play chess?

This is a good question. your understanding in general is correct. Indeed, data can be used to construct a proper evaluation of a move/board position and recommended moves based on its history (at ...
sma's user avatar
  • 823
8 votes

Is there any board game where a human can still beat an AI?

Not all games (or even board games) are computationally algorithmic. Even the least skilled player is likely to trounce the hottest pattern-matching algorithm in a game of Pictionary (for example). ...
Robert Cartaino's user avatar
6 votes
Accepted

Is there any board game where a human can still beat an AI?

For many years, the focus has been on games with perfect information. That is, in Chess and Go both of us are looking at the same board. In something like Poker, you have information that I don't have ...
Matthew Gray's user avatar
  • 4,262
5 votes
Accepted

How should I represent the input to a neural network for the games of tic-tac-toe, checkers or chess?

When you are working with neural networks, as long as the data is there, the neural network is usually able to learn how to process it into a useful result. However, you usually also want to keep the ...
Aiden Grossman's user avatar
3 votes

Which value to propagate in Monte Carlo Tree Search in a non-zero-sum game?

In theory: yes, you can backpropagate any sort of scores you want to maximise. They don't have to be restricted to just a small, discrete set of values such as $\{-1, 0, 1\}$, and also do not have to ...
Dennis Soemers's user avatar
  • 10.3k
3 votes

How should I represent the input to a neural network for the games of tic-tac-toe, checkers or chess?

The basis of reinforcement learning methods is to give each (game) state (or action) a value that somehow represents how good that state (or action) is. To store these values we could use something as ...
Hai Nguyen's user avatar
3 votes

How to deal with a huge action space, where, at every step, there is a variable number of legal actions?

One way I can think of is to redefine "actions" in a game to make them more fragmented, in such a way that a player has multiple actions per turn. In chess, for example, we can define an action as ...
Bridgeburners's user avatar
2 votes

Is there any board game where a human can still beat an AI?

Artificially intelligent computer programs should be able to be at the same level or beat humans at every game that we play. This is because games follow rules that are scriptable, and artificial ...
Veena Ghorakavi's user avatar
2 votes
Accepted

Neural Network output for the game of Checkers

Correction regarding policy encoding The policy encoding you propose for Chess is not the one used by the original AlphaZero and would not work. The network needs to output a policy distribution over ...
KarelPeeters's user avatar
2 votes

How should I represent the input to a neural network for the games of tic-tac-toe, checkers or chess?

Representation of states is very important to prepare the data for the neural networks. You can try a different way and pick which fit best in your case. You can use 18 neurons as input where each ...
Ankish Bansal's user avatar
2 votes
Accepted

How to account for a numeric variable in a state in RL?

You don't have "9 possible states" in your problem, you have those 9 board squares multiplied by all possible values that the numbers within them can take, in all combinations. If those are ...
Neil Slater's user avatar
  • 32.5k
2 votes

How does an AI like ChatGPT answer a question in a subject which it may not know?

The original question asked about "an AI" generally, yet most of the responses here focus on OpenAI's ChatGPT specifically. Seems like the answer would depend on the specific type of AI ...
Trutane's user avatar
  • 121
1 vote
Accepted

AlphaZero Chess: High portion of "draws" during first rounds of self-play hampers learning

First intuition I wrote a quick script to test the outcome distribution of random chess games: I get about 7% wins for black and white each, so 14% non-drawn games. The MCTS should still be able to ...
KarelPeeters's user avatar
1 vote

MCTS: Units away from the action

Monte Carlo Tree Search is searching for the action with the best expected reward. If the reward is determined by whether it wins or losses, then all winning moves are equal and one will be selected ...
CleverLikeAnOx's user avatar
1 vote

MCTS: Units away from the action

It is very nice and I recommend others to play with your code. Really nice. Did I get it right that the Carthaginian are the ones with extra power and you refer to them as the AI? It seems that the ...
Oren's user avatar
  • 141
1 vote

MCTS: Units away from the action

One option is that the RL is not there yet, you may need to continue with the training, or maybe add to the win/lose reward some tiny "hints" what is a good move (0.001 when you eat an enemy,...
Oren's user avatar
  • 141
1 vote

Monte Carlo Tree Search for Robo Rally AI

Have looked at the game description. MCTS can be a very good choice, as far as I can tell. The action that you take at each phase is selecting five cards from the nine you have and placing them face ...
Oren's user avatar
  • 141
1 vote
Accepted

MCTS with multi actions

Yes, for MCTS this is no problem at all. In fact it is slightly more annoying for minimax/alpha-beta based engines, because there we usually like (if possible) to use efficient negamax implementations ...
Dennis Soemers's user avatar
  • 10.3k
1 vote

Scrabble rack observation with MuZero

Use one hot encoding for each position, shape $(7, 27)$. Stack these two dimensions, shape $(189)$. Tile (replicate) this vector into images of the same resolution, now shape $(15, 15, 189)$ Stack ...
Uduse's user avatar
  • 136
1 vote

Strategy for playing a board game with Minimax algorithm

I'm not familiar with your game so I can't tell you what a good heuristic woul be in your specific case, but I can give you some advice on how to look for a good heuristic function. As a rule of thumb,...
Zekko's user avatar
  • 26
1 vote

How do I write a good evaluation function for a board game?

Based on your description, I'd maximize the following terms: i -max(f - 10 - (MAX_FIELD_INDEX - i), 0) - assuming consumption ...
maaartinus's user avatar
1 vote

How to design a good evaluation function for a go-like game?

If you have the best combination of distance between the stones, you should choose the best move to win. In this case, you have to be close to where your opponent plays. It is best to do this by ...
Azad İrven's user avatar

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