6. Search: Games, Minimax, and Alpha-Beta

MIT OpenCourseWare
10 Jan 201448:16

TLDRThis lecture delves into the history and strategies of computer chess, highlighting the philosophical debates and technological advancements that led to Deep Blue's victory over the world chess champion. It explores various approaches to game-playing algorithms, focusing on the minimax algorithm with alpha-beta pruning for efficient game tree search. The speaker also discusses the concept of progressive deepening and the difference between human and 'bulldozer' intelligence in the context of chess, questioning whether computer chess models human cognition.

Takeaways

  • 😀 The discussion revisits a 1963 paper by Hubert Dreyfus, which claimed computers couldn't play chess, and the subsequent rebuttal by Seymour Papert after Dreyfus lost to a chess machine.
  • 🏆 In 1968, a bet was made between David Levy and John McCarthy on whether a computer would beat the world chess champion within a decade, highlighting the early skepticism about AI in chess.
  • 🤖 The 1997 victory of IBM's Deep Blue over world chess champion Garry Kasparov marked a significant milestone in AI, making chess less interesting as a challenge for AI.
  • 🧠 The lecture explores different approaches for designing computer programs to play chess, emphasizing the importance of understanding different types of intelligence, including those that model human thought processes.
  • 👥 The minimax algorithm with alpha-beta pruning is introduced as a key technique for efficient game tree search, which is crucial for AI in games like chess.
  • 🌳 The concept of a game tree is explained, with nodes representing board positions and branches representing possible moves, illustrating the complexity of searching through game possibilities.
  • 🔢 The static evaluation function is discussed, which assigns a numerical value to a board position, helping the AI decide which moves are most advantageous.
  • 🚀 The importance of 'progressive deepening' in AI game algorithms is highlighted, which allows the AI to search deeper into the game tree as time allows.
  • 💡 The lecture suggests that while AI like Deep Blue demonstrates a form of intelligence, it is different from human intelligence, often relying on raw computational power rather than pattern recognition and experience.
  • ⏰ Time management in AI gameplay is crucial, with strategies like progressive deepening ensuring that the AI can provide a move in a timely manner, even if it hasn't searched the entire game tree.

Q & A

  • Who wrote a paper in 1963 titled 'Computers Can't Play Chess'?

    -Hubert Dreyfus, a noted philosopher at MIT, wrote a paper in 1963 with the heading 'Computers Can't Play Chess'.

  • What was the rebuttal paper's title to Hubert Dreyfus' claim about computers and chess?

    -Seymour Papert wrote a rebuttal to Dreyfus' paper titled 'Dreyfus Can't Play Chess Either'.

  • Who made a bet in 1968 that no computer would beat the world chess champion within 10 years?

    -David Levy, a chess master, bet John McCarthy that no computer would beat the world chess champion within 10 years.

  • What was the outcome of the bet between David Levy and John McCarthy?

    -John McCarthy gave up on the bet after five years because it became clear that no computer would win in the way he wanted, by playing chess like a human.

  • In what year did Deep Blue beat the world chess champion, and what did this event lead to?

    -Deep Blue beat the world chess champion in 1997, which led to chess becoming uninteresting in the context of AI challenges.

  • What are the three approaches discussed in the script for a computer to play chess?

    -The three approaches discussed are: 1) The machine might make a description of the board the same way a human would, 2) Using if-then rules, and 3) Looking ahead and evaluating.

  • What is the term used to describe the number of choices considered at each level in a game tree?

    -The term used to describe the number of choices considered at each level in a game tree is 'branching factor'.

  • What is the minimax algorithm and how does it work?

    -The minimax algorithm is a decision-making procedure used in artificial intelligence and game theory. It involves evaluating possible moves in a game by considering the optimal response to each move, alternating between a maximizing player and a minimizing player.

  • How does the alpha-beta algorithm improve upon the minimax algorithm?

    -The alpha-beta algorithm improves upon the minimax algorithm by using two parameters, alpha and beta, to prune the search tree and eliminate branches that do not need to be explored, thus reducing the number of nodes that need to be evaluated.

  • What is the significance of the number of leaf nodes in a game tree?

    -The number of leaf nodes in a game tree represents the total possible game states that need to be evaluated. The more leaf nodes, the more computationally intensive the evaluation process becomes.

  • What is progressive deepening and how does it relate to game tree search?

    -Progressive deepening is a technique used in game tree search where the search is iteratively deepened until a set time limit is reached. This ensures that the program always has a move ready and can provide better moves as more time is available.

Outlines

00:00

😀 Early Doubts and Developments in Computer Chess

The paragraph discusses the early skepticism about computers playing chess, exemplified by Hubert Dreyfus's 1963 paper. Despite initial doubts, Dreyfus lost to a chess machine, and later, David Levy's bet against AI founder John McCarthy highlighted the evolving capabilities of computers in chess. The narrative then shifts to the significance of understanding different types of intelligence, including that demonstrated by game-playing programs, which led to the development of various approaches to designing computer chess programs.

05:02

🤔 Analyzing Chess Strategies and Evaluation Methods

This section delves into the methods used by computers to play chess, starting with the human-like approach of analyzing the board's strategic elements. It acknowledges the challenge of replicating human thought processes and moves on to discuss alternative approaches such as rule-based systems and lookahead evaluation. The paragraph introduces the concept of static evaluation, which assesses board positions without considering future moves, and explains how linear scoring polynomials are used to assign values to board states.

10:05

🌳 The Complexity of Chess Tree Search

The speaker introduces the concept of searching through a game tree to find the best move in chess, using the British Museum algorithm as an example of a brute force approach. The discussion highlights the branching factor and depth of the search tree, and the calculation of leaf nodes. It also touches on the impracticality of evaluating every possible move due to the vast number of combinations in chess, even with modern computing power.

15:07

🚀 The Impracticality of Exhaustive Search and Shift to Heuristic Methods

The paragraph explores the impracticality of using the British Museum algorithm for chess due to the astronomical number of leaf nodes. It humorously calculates the number of nanoseconds in the universe's history and concludes that even if all atoms in the universe were evaluating positions since the Big Bang, it would not be sufficient. This leads to the necessity of using heuristic methods like lookahead search, which is a more efficient way of evaluating potential moves.

20:08

🎯 The Minimax Algorithm and Its Application in Chess

This section explains the minimax algorithm, a recursive method for minimizing the maximum possible loss in zero-sum games like chess. It describes the algorithm's process of evaluating game trees and making decisions based on the maximizing and minimizing players' perspectives. The paragraph uses a simplified game tree to illustrate how the algorithm works, emphasizing its simplicity and effectiveness in decision-making.

25:12

🔍 Enhancing Minimax with Alpha-Beta Pruning

The paragraph introduces the alpha-beta algorithm as an enhancement to the minimax algorithm, designed to reduce the number of nodes evaluated in the search tree. It explains how alpha-beta pruning works by setting bounds (alpha and beta) to eliminate branches that cannot possibly influence the final decision. The speaker uses a detailed example to demonstrate how the algorithm skips unnecessary evaluations, thus saving computational resources.

30:14

🌱 Deepening the Search with Progressive Deepening

This section discusses the concept of progressive deepening, where the search algorithm starts with a shallow search and gradually increases the depth, ensuring that a move is always available within a given time constraint. The paragraph explains the insurance policy approach, where calculations are made at each level as a backup, and how it aligns with the anytime algorithm concept, which always has a ready answer.

35:17

🏆 The Evolution of Game-Playing Programs

The speaker reflects on the evolution of game-playing programs, highlighting the use of minimax, alpha-beta pruning, and progressive deepening as foundational techniques. The paragraph also touches on the importance of adapting these techniques to the specific characteristics of the game being played, such as varying branching factors and the need for insurance policies at different levels of search.

40:17

🤖 Deep Blue's Strategies and Comparison to Human Intelligence

The final paragraph contrasts the raw computational power of Deep Blue, which used parallel computing and uneven tree development to evaluate positions deeply, with the more pattern-recognition-based approach of human chess players. It questions whether Deep Blue's method of playing chess is a model for human intelligence or a different form of intelligence altogether, suggesting that while it demonstrates capability, it lacks the sophistication of human strategic thought.

Mindmap

Keywords

💡Minimax

The minimax algorithm is a decision-making strategy used in game theory, particularly in two-player, zero-sum games like chess. It involves players assessing possible moves and counter-moves to maximize their own outcome while minimizing the opponent's. In the context of the video, minimax is foundational to how computers evaluate and choose the best moves in games. The script illustrates how the algorithm works by having a player (maximizer) aim for the highest score while their opponent (minimizer) tries to select the option that results in the lowest score for the maximizer.

💡Alpha-Beta Pruning

Alpha-beta pruning is an optimization technique for the minimax algorithm that reduces the number of nodes evaluated in the search tree. It eliminates branches that cannot possibly influence the final decision. The video explains alpha-beta pruning as a method to increase efficiency by not exploring certain paths in the game tree that are guaranteed to be worse than others. This concept is crucial for the practical application of minimax in computer programs, as it significantly speeds up the decision-making process without sacrificing the quality of the outcome.

💡Branching Factor

In game theory and particularly in the context of game trees, the branching factor refers to the average number of legal moves or children of a node. The script uses the branching factor to discuss the complexity of games like chess, where a high branching factor means many possible moves at each turn, leading to an exponential growth in the number of positions to evaluate. Understanding the branching factor is key to appreciating the computational challenges faced by AI in games.

💡Static Evaluation Function

A static evaluation function in the context of game-playing AI is a heuristic that assigns a numerical value to a game state to determine its desirability. The video discusses how these functions are used to evaluate board positions in games like chess, considering features such as piece safety, material advantage, and king safety. The script explains that these evaluations are used at the leaf nodes of a game tree to inform the minimax decision process.

💡Game Tree

A game tree is a diagram used to depict all possible sequences of moves and counter-moves in a game. Each node represents a game state, and branches represent possible moves from that state. The video script uses the game tree as a central concept to explain how AI traverses through possible game scenarios to decide the best move. The depth and branching factor of the tree are critical factors in determining the computational complexity of searching for an optimal move.

💡Progressive Deepening

Progressive deepening is a search strategy used in AI where the search depth is gradually increased until the available time is exhausted or a satisfactory solution is found. The video describes progressive deepening as a method to ensure that a game-playing program always has a move ready, even if it hasn't completed the entire search. This approach is particularly useful for time-constrained games, providing a balance between move quality and response time.

💡Dead Horse Principle

The dead horse principle, as mentioned in the video, refers to the idea of not wasting computational resources on parts of the search tree that have been determined to be irrelevant to the final decision. This concept is closely related to the efficiency gains provided by alpha-beta pruning. The video uses the term to emphasize the importance of focusing computational efforts on promising areas of the game tree, thereby avoiding unnecessary work.

💡Raw Power vs. Sophistication

This concept from the video contrasts the brute force computational approach of AI like Deep Blue with the sophisticated pattern recognition and experience-based strategies used by human players. The script suggests that while AI can evaluate a vast number of positions due to its raw computational power, human intelligence in games often relies on deeper understanding and learned patterns, which is a different kind of intelligence.

💡Buldozer Intelligence

The term 'buldozer intelligence' is used in the video to describe the approach of AI in games, which uses massive computational power to evaluate many possible moves, much like a bulldozer's raw power in moving materials. It contrasts with the more nuanced and learned strategies that human players employ. The video suggests that while this approach is effective, it represents a different kind of intelligence than human strategic thinking.

💡Heuristic

A heuristic is a problem-solving strategy that employs a practical but not necessarily optimal method to find a satisfactory solution. In the video, heuristics are used in the form of static evaluation functions to estimate the value of game positions. These heuristics allow the AI to make decisions more efficiently by providing quick, though not always perfect, evaluations of board states.

Highlights

In 1963, philosopher Hubert Dreyfus claimed computers couldn't play chess, but later lost to a chess machine.

Seymour Papert playfully rebutted Dreyfus' paper with one titled 'Dreyfus Can't Play Chess Either'.

David Levy bet John McCarthy that no computer would beat the world chess champion within 10 years in 1968.

McCarthy conceded the bet in 1973, believing computers wouldn't beat humans at chess the way he envisioned.

IBM's Deep Blue defeated world chess champion Garry Kasparov in 1997, marking a significant milestone in AI.

Game-play elements can model aspects of human intelligence, making the study of AI in games valuable.

Approach one for computer chess involves mimicking human board analysis and strategy, which remains elusive.

The second approach uses if-then rules for move selection, but lacks strength for a strong chess player.

A third approach looks ahead and evaluates possible board positions to determine the best move.

The minimax algorithm is introduced as a method for decision-making in zero-sum games like chess.

Alpha-beta pruning is explained as an optimization of the minimax algorithm to reduce search space.

The branching factor and depth are key concepts in evaluating the complexity of game trees.

Static evaluation of board positions is crucial for the minimax algorithm to determine move values.

Deep Blue's victory over Kasparov was achieved through a combination of minimax, alpha-beta, and progressive deepening.

Progressive deepening ensures that a game AI always has a move ready, regardless of time constraints.

Uneven tree development allows AI to focus computational resources on more promising game paths.

Deep Blue's intelligence is a form of 'bulldozer intelligence', different from human strategic thinking.

Human chess masters rely on pattern recognition and chess knowledge, not deep recursive search.