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21

Losing games to computers because of mistakes made under time pressure was probably a thing about 20 years ago, when Kasparov lost to DeepBlue after such a mistake(correction: it was Kramnik with the blunder, not Kasparov (see edit 2)). But after Kramnik's loss in early 2000s, no world champion ever tried to play against a computer (to my knowledge). ...


14

Good question. First and foremost is that in Go deepmind had no superhuman opponents to challenge. Go engines were not anywhere near the highest level of the top human players. In chess, however, the engines are 500 ELO points stronger than the top human players. This is a massive difference. The amount of work that has gone into contemporary chess engines ...


10

Alexander Kronrod once said, “Chess is the Drosophila of artificial intelligence”. John McCarthy disagrees with this statement. I think it's primarily because he has different vision.Techniques and Innovative methods developed to play these games have been found useful over the wide spectrum of Computer Science (and not just Artificial Intelligence). The ...


8

If one thinks of intelligence as a continuous measure of optimization power (that is, how much better are outcomes for any unit of cognitive effort expended), then exhaustive search has non-zero intelligence (in that it does actually give better outcomes as more effort is expended) but very, very low intelligence (as the outcomes are better mostly by luck, ...


7

When we use the term rationality in AI, it tends to conform to the game theory/decision theory definition of rational agent. In a solved or tractable game, an agent can have perfect rationality. If the game is intractable, rationality is necessarily bounded. (Here, "game" can be taken to mean any problem.) There is also the issue of imperfect information ...


7

This relates to the concept of "solved games". In general, two player turn-based games with perfect information - of which chess is an example - can result in all three possible outcomes: a forced win for white, a forced win for black, or a forced draw. The short, although unsatisfactory answer is that chess is not solved, and it is not clear whether it can ...


7

The allegation was based on the fact that Deep Blue made a choice that did not yield the immediate (or short term) benefit that was synonymous with systems back then (1997). Computational capability was significantly less powerful then, and Kasparov claimed that only a grand master would have made the decision that the system did - so the deep blue team ...


7

If a computer is just brute-forcing the solution, it's not learning anything or using any kind of intelligence at all, and therefore it shouldn't be called "artificial intelligence." It has to make decisions based on what's happened before in similar instances. For something to be intelligent, it needs a way to keep track of what it's learned. A chess ...


4

Why is Game Playing R&D a Focus of Resource Allocation? When examining the apparent obsession with game playing as researchers attempt to simulate portions of human problem solving abilities, the orthodoxy of the views of John McCarthy (1927 – 2011) may be misleading. Publication editorial bias and popular science fiction themes may obscure the ...


4

I've read that a simple reflex agent will not act rationally in a lot of environments. E.g. a simple reflex agent can't act rationally when driving a car as it needs previous perceptions to make correct decisions. I wouldn't say that the need for previous perceptions is the reason why a simple reflex agent doesn't act rationally. I'd say the more serious ...


4

AI programs that exist in today's world fall into the category of Narrow Intelligence. Narrow Intelligence are easy to distinguish when compared to General Intelligence (ones that resemble more like humans). Highly advanced AI can often resemble to act like humans thought. I will like to talk about Deep Blue here. Garry Kasparov, in a series of matches ...


4

There are three cases in which it is easily possible to distinguish strong AI play from strong human play: The AI is playing at super human skill level This seems obvious, but I want to mention it for the sake of completeness. The current skill ceiling of top level chess is well known and an opponent playing way above this skill ceiling must either be an ...


4

MCTS for chess had been tried in the literature with little success. It was assumed AlphaGo's approach would never work on chess, maybe in Go but not in chess. Suddenly, Google announced the approach was working and it was beating the World's strongest chess program by a very signficiant margin. Before Google, all chess programmers were taught crafting ...


3

The branching factor is important, as it limits the effectiveness of search. However, the branching factor in chess is already too high to effectively search without techniques that reduce the size of the search space. Even with millions of tests per second, a computer can only check a small fraction of the possible future games in order to find results in ...


3

The number is 4672 from Google. https://arxiv.org/pdf/1712.01815.pdf A move in chess may be described in two parts: selecting the piece to move, and then selecting among the legal moves for that piece. We represent the policy π(a|s) by a 8 × 8 × 73 stack of planes encoding a probability distribution over 4,672 possible moves. Each of the 8×8 positions ...


3

Stockfish is free software (source while AlphaGo can only be used by employees at Deepmind. This is important, because it means all answers to this question can only rely on the AlphaGo paper. Some decisions during the matches (e.g. giving limited time per move and not per game; giving different computational power) lead to unclarity how good alpha go really ...


3

On page 13, right under Table S1 in the linked paper, this is explained (emphasis in bold at the end mine): Each set of planes represents the board position at a time-step $t - T + 1, \dots, t$, and is set to zero for time-steps less than $1$. I suspect the solution they write there would indeed work better than just repeating the starting position up to ...


3

It means that there is no explicit coding of action choices to promote to queen, it is the default assumption if the underpromotion actions are not taken. The Alpha Zero chess implementation can represent promotion to queen by not selecting an underpromotion action, whilst moving a pawn so that it qualifies for promotion.


3

OpenAI's Gym is a standardised API, useful for reinforcement learning, applied to a range of interesting environments many of which you can then access for free with little effort. It is very simple to use, and IMO worth learning if you want to practice RL using Python to any depth at all. You could use it to ensure you have good understanding of basic ...


2

Really any 'intelligence' exhibited by a computer is deemed AI, regardless of brute force or use of smart heuristics. For example, a chat bot can be coded to respond to most responses using many, many if statements. This is an AI no matter how poorly coded/designed it is. The chess playing computer beating a human professional can be seen as a meaningful ...


2

Yes of course they are possible, I have built some! You are correct that there is an aspect of randomness to the process of machine learning but it is more accurate to describe this as trial and error. Each successive try in a machine learning system is evaluated against a goal and if it is an improvement or is closer to the goal, then this try is stored and ...


2

Brute force approach is certainly the first step of many in AI programming. But using these experiences the program must learn to find the best solution or at least a closer solution to the problem. Since the first goal in AI is to find any solution, nothing can beat the brute force approach. But then using the previous results of brute force approaches, the ...


2

I find the statement troubling as the first confirmed algorithmic intelligence may have been a NIM automata, so from my perspective, the development of Algorithmic Intelligence is inseparable from combinatorial games. it would also seem that McCarthy does not hold the opinion that games are useful, which leads me to suspect he has never seriously studied the ...


2

is there a value given for each piece (e.g. 1 for pawn, 3 for knight, 9 for queen, etc.) to train the algorithm, or does the algorithm learn this by himself? No, there are no such explicit values assigned to pieces, no manually-constructed evaluation functions. The paper states that "no domain knowledge" is given to the algorithm other than the game's rules ...


2

It's possible for an AI to learn chess without even knowing how to move the pieces. Google's AlphaZero didn't do that as their programmers coded the chess rules, but it's possible. One can learn the rules from human played chess games. Once the rules are known, we could use reinforcement learning to improve playing strength (and other board games).


2

The The Oxford Companion to Chess has entries on only 700 named openings, and lists another 1327 opening variations in the index, and I wouldn't be surprised if someone out there had them all memorized. For an algorithm, however, storing that number of openings is trivial, and Chess algorithms traditionally made use of high-quality "game books" which are ...


2

If you can remember everything and there's no randomisation in your outcome like chess, there is absolutely no reason not to do that. Anybody who can remember all the possible board configurations in chess, by definition plays perfect chess. A perfect player would never lose. Unfortunately, most practical problems can't be solved by brute-force, and that ...


2

I see, based on the articles you provide, many levels of surprise in the victory: Chess is hard game to master and the counter part had the world's best practices, AlphaZero had tabula rasa. Learning took four hours and AlphaZero lost no match of 100. Playing style was an alien mix of human and computer like moves, aggressive and some times seeming ...


2

A simple google search gives plenty of results. If you have a look at the entry in wikipedia for Minimax it has mathematical representations as well as some basic pseudocode and tree representations to help grasp the concept. Proving it would be a matter of going through the regular methods of a mathematical proof and would probably be a bit complicated. ...


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