Chessand Computation
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ChessandComputation

Alejandro Fernández Camello

A free book on chess algorithms, search, and machine learning.

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The knight’s tour1.2

Contents

  1. IntroductionReading complete
  2. 01Chess Problems
    1. 1.1
      The n-queens problemReading completeTo stimulate your curiosity and engagement in this fascinating chapter, allow me to reveal an intriguing incentive.
    2. 1.2
      The knight's tourReading completeIn this section, we will delve into the exploration of one of the most intriguing problems originating from the fascinating world of chess.
    3. 1.3
      The king's marchesReading completeIn this chapter, the question of how many routes a chess king can take, starting from one specific square to another, using the minimum number of moves will be addressed.
  3. 02Artificial Intelligence
    1. 2.1
      What is artificial intelligence?Reading completeTo delve deeper into the field of artificial intelligence (AI), it is essential to clearly distinguish several related terms.
    2. 2.2
      Formal definition of chessReading completeThe analysis of how artificial intelligence (AI) and its subfields approach the study of chess requires first the establishment of a defined objective.
    3. 2.3
      Game treeReading completeGame trees represent a fundamental structure for the conceptualization of strategies in games such as chess. This strategic tool originated for the first time in the 19th century.
    4. 2.4
      HeuristicsReading completeA heuristic is a strategic method or general rule that we employ to simplify decision-making or the resolution of complex problems.
    5. 2.5
      The minimax algorithmReading completeThe operation of the minimax algorithm is intrinsically anchored to its denomination. This algorithm is used to identify the theoretically optimal move in any perfect information game.
    6. 2.6
      Alpha-beta pruningReading completeAlpha-beta pruning is an efficient technique that drastically reduces the number of states visited in the game tree of the minimax algorithm.
    7. 2.7
      Board representationReading completeAfter exploring the search mechanisms in artificial intelligence games, it is crucial to examine another essential component: the representation of the game board.
    8. 2.8
      Advanced techniques in the game treeReading completeIn this section, we will focus on the different techniques employed to optimize the search in alpha-beta pruning, an essential strategy for efficiency in AI games.
  4. 03Deep Reinforcement LearningReading complete
    1. 3.1
      RewardReading completeReward is a key element in reinforcement learning, as it provides artificial intelligence (AI) with feedback regarding its performance, signaling whether its performance is correct or, on the contrary.
    2. 3.2
      PolicyReading completePolicy, in terms of machine learning, represents the decisions made by an agent based on a given state. These policies can be classified in two main ways: deterministic and non-deterministic.
    3. 3.3
      ModelReading completeThe main objective of our model is to deeply understand the underlying dynamics of a game. This implies knowing the transition probabilities to different states after the execution of a specific action.
    4. 3.4
      Reinforcement learning algorithmsReading completeThe field of reinforcement learning has experienced remarkable effervescence in recent years, leading to the creation of considerable diversity in algorithms.
    5. 3.5
      Neural networksReading completeIn recent years, neural networks and reinforcement learning have been the main drivers of the incredible advancement of artificial intelligence.
    6. 3.6
      AlphaZeroReading completeIn this section we will delve into the analysis of a revolutionary module that has transformed the chess landscape as we know it.
  5. 04State of the ArtReading complete
    1. 4.1
      Stockfish strikes backReading completeThe Stockfish development team, far from sitting idly by after its defeat against AlphaZero, decided to adopt a strategy that recalls the old saying: "If you can't beat your enemy, join him".
    2. 4.2
      Imitating human decision-makingReading completeThroughout this text we have delved into a multitude of strategies aimed at achieving a singular objective: developing superlative skills in the game of chess.
  6. 05StandardsReading complete
  7. AFlowchartsReading complete
  8. BChessReading complete
  9. CChess NotationReading complete
  10. DRecommendationsReading complete
  11. —ReferencesReading complete