Chess > Board Game Of Your Choice
Waiting for the contender's third argument.
The round will be automatically forfeited in:
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Chess is my favorite board game. Let's see yours's.
CHESS = a board game of strategic skill for two players, played on a chequered board on which each playing piece is moved according to precise rules. The object is to put the opponent's king under a direct attack from which escape is impossible ( checkmate ).
1. No new arguments are to be made in the final round.
2. Definitions are agreed upon and are not to be contested.
3. Rules are agreed upon and are not to be contested.
4. Sources can be hyperlinked or provided in the comment section.
5. A breach of rules 1-5 should result in a 1 point penalty.
6. No Kritiks.
7. Bones cannot participate (Due to me being sick of losing elo XD)
8. A breach in rules 6-8 should result an instant loss.
PRO: Will argue that Chess is the best board game out there.
CON: Will argue that another board game is better.
R1: Opening Statements.
R2: Rebuttal and Defense
R3: Closing Statements.
Another way to think of it is to compare Go to chess, which in the '90s was hard enough to imagine AI mastering before IBM came along. After the first two moves of a Chess game, there are 400 possible next moves. In Go, there are close to 130,000."The search space in Go is vast... a number greater than there are atoms in the universe," Google wrote in a January blog post about the game.
While there appears to be no correlation between the number of games played and the number of draws that occur at high-level chess, draws are still the most frequently seen result in the game of chess. Whether or not tournament directors will be able to change this through shorter time controls or other methods is up for debate. However, it can be assumed that only when there is a lack of information, there is a significantly high number of draws at the highest levels. For the years 1971 until the early 1990s, not a lot of games were collected. While there is a high number of draws, it cannot be assumed that the number of games collected is the cause of the number of draws, because of the lack of data. However, from the 1990s onward, the relative percentage of draws each year stabilizes to around 50%. We can, therefore, conclude that with a higher number of collected games, there will be a more consistent number of draws.
We created AlphaGo, a computer program that combines advanced search tree with deep neural networks. These neural networks take a description of the Go board as an input and process it through a number of different network layers containing millions of neuron-like connections.One neural network, the “policy network”, selects the next move to play. The other neural network, the “value network”, predicts the winner of the game. We introduced AlphaGo to numerous amateur games to help it develop an understanding of reasonable human play. Then we had it play against different versions of itself thousands of times, each time learning from its mistakes.Over time, AlphaGo improved and became increasingly stronger and better at learning and decision-making. This process is known as reinforcement learning. AlphaGo went on to defeat Go world champions in different global arenas and arguably became the greatest Go player of all time.
AlphaGo uses deep learning and neural networks to essentially teach itself to play. Just as Google Photos lets you search for all your pictures with a cat in them because it holds the memory of countless cat images that have been processed down to the pixel level, AlphaGo’s intelligence is based on it having been shown millions of Go positions and moves from human-played games.The twist is that DeepMind continually reinforces and improves the system’s ability by making it play millions of games against tweaked versions of itself. This trains a "policy" network to help AlphaGo predict the next moves, which in turn trains a "value" network to ascertain and evaluate those positions. AlphaGo looks ahead at possible moves and permutations, going through various eventualities before selecting the one it deems most likely to succeed. The combined neural nets save AlphaGo from doing excess work: the policy network helps reduce the breadth of moves to search, while the value network saves it from having to internally play out the entirety of each match to come to a conclusion.This reinforced learning system makes AlphaGo a lot more human-like and, well, artificially intelligent than something like IBM’s Deep Blue, which beat chess grandmaster Garry Kasparov by using brute force computing power to search for the best moves — something that just isn’t practical with Go. It’s also why DeepMind can’t tweak AlphaGo in between matches this week, and since the system only improves by teaching itself, the single match each day isn’t going to make a dent in its learning. DeepMind founder Demis Hassabis says that although AlphaGo has improved since beating Fan Hui in October, it’s using roughly the same computing power for the Lee Se-dol matches, having already hit a point of diminishing returns in that regard.
There are board games with complete information available to both players and ones with incomplete information. Chess and Go are both games that involve complete information, nothing about the game’s unfolding has a situation where there’s anything mysterious or hidden to any players, of course this is only really true if both players know enough about the game and are equally intelligent and attentive as well as lusting for victory.Chess is overrated and well known, it’s the ‘logic game’ and most who push for it being supreme tend to think that all other board games are inferior to Chess at exploring logic but the game Go is irrefutably superior to Chess in every level of logic, you may argue it’s a different type of logic though and I’ll go into that in my next point.
Chess is a logic game. It is a war game. It is a classic game.
If you accentuate the popularity of Chess, I'll probably take Monopoly or could even troll and go for snakes/chutes and ladders. - RM
Go revolves around many twists and turns, you are never in a situation where you 100% know for sure what you’re doing is optimal (apart from getting corner spots, that’s usually always optimal). The game of Go flows with many back and forths if both players are good at it, both on the edge of their seats (as are the spectators) since quite literally no human brain quite knows who really is going to win (this is of course only true if both are extremely good or equally poor, if one if very supreme at Go, then you do pretty much know they’ll win but it’s still far more interesting to see it unfold than Chess from a strategic standpoint).
- Go is too complicated to be enjoyable among commercial standards.
- Games are too long to be exciting.
- AlphaGo is inferior to AlphaZero.
- My opponent has not made a point to any of these, so I extend all.