Difference between revisions of "Losing play"

From HexWiki
Jump to: navigation, search
(New article)
 
(typo and added category)
Line 1: Line 1:
 
'''Losing play''' is when a player knows that they are [[optimal play|theoretically losing]], but continues the game in the hopes that the opponent will make a mistake. This typically happens when the winning player is less skilled than the losing player and may not be able to see the winning strategy.
 
'''Losing play''' is when a player knows that they are [[optimal play|theoretically losing]], but continues the game in the hopes that the opponent will make a mistake. This typically happens when the winning player is less skilled than the losing player and may not be able to see the winning strategy.
  
One example of losing play is a [[handicap|handicap game]], where the stronger player is losing at the start of the game, but hopes that the opponents will make enough mistakes to tilt the game in the player's favor.
+
One example of losing play is a [[handicap|handicap game]], where the stronger player is losing at the start of the game, but hopes that the opponent will make enough mistakes to tilt the game in the player's favor.
  
 
== Strategies for losing play ==
 
== Strategies for losing play ==
Line 23: Line 23:
  
 
[[category: Advanced Strategy]]
 
[[category: Advanced Strategy]]
 +
[[category: Computer Hex]]

Revision as of 04:32, 1 October 2023

Losing play is when a player knows that they are theoretically losing, but continues the game in the hopes that the opponent will make a mistake. This typically happens when the winning player is less skilled than the losing player and may not be able to see the winning strategy.

One example of losing play is a handicap game, where the stronger player is losing at the start of the game, but hopes that the opponent will make enough mistakes to tilt the game in the player's favor.

Strategies for losing play

Losing play feels very different from regular play, because the player already knows that there is no winning move and therefore has no viable options to choose from. The player might be tempted to resign. The objective of losing play is to induce the opponent to make a mistake.

Give them enough rope

A common strategy for losing play is to make the situation as complicated as possible, so that the opponent will not be able to analyze it in the amount of time they have. This strategy has the gruesome name "give them enough rope". The name is derived from the proverb "Give someone enough rope, and they will hang themselves". In Hex, this can take several forms:

  • Do not simplify. Don't play any moves that would unnecessarily simplify the board position and clarify the situation for your opponent. For example, if your opponent has a complicated virtual connection that uses an intricate web of double threats, don't intrude in the double threats, as it would simplify your opponent's connection. Another example is fast forwarding a ladder, rather than playing it out.
  • Give your opponent many potential responses. If you play a forcing move, your opponent typically only has one possible response, and you are effectively giving them no choice but to play a winning move. Instead, play a move where the opponent must choose from many potential responses. Hopefully they will pick the wrong one.
  • Play an unexpected move. It often helps to play a move that your opponent did not anticipate. The opponent is forced to analyze your move from scratch, and may not have enough time to complete the analysis. Effectively you are giving them a puzzle that they may fail to solve.

Play a fishing move

A fishing move is a move that the opponent could foil, but they may not know how to do so. If the game is still undecided or you are winning, playing a fishing move is a bad idea, because when the opponent foils, it worsens your position. But in losing play you literally have nothing else to lose, so you may as well try a fishing move. If you do so, try to play it as early as possible, hopefully before your opponent can see what you are doing.

Losing play in computer Hex

Most computer Hex algorithms are not good at losing play. Both neural network algorithms and the more traditional alpha-beta-search algorithms are optimized to find the most promising move from a number of possibilities. However, if all available options are clearly losing, these algorithms do not have a notion of which moves are "more" losing than others. When presented with such a position, most algorithms just make random moves.