Conclusion

• These are often useful techniques, but they don't work for every game.
• The search space for Go is so big, and the evaluation function so poorly understood that Alpha-Beta doesn't work. That was the case when I wrote this slide in 2015. Now, deep learning (Alpha-Go) has learned a good enough evaluation function to win.
• Take Home Points
  1. Adversarial games can be represented by a game tree.
  2. Minimax is a good way to play these.
  3. In all but the simplest game, you need a heuristic evaluation function.
  4. Minimax can be improved (without loss) by alpha-beta pruning and iterative deepening.
• Reading: For this week is Russell and Norvig's Adversarial Search chapter through section 4 (pp. 164-179).
• Reading: for next week skim Janet Kolodner's Case-Based Reasoning and read the Case Based Reasoning Wiki.