Chapter
Anticipating Moves and Finding Saddle Points
1:04:48 - 1:09:17 (04:29)
The speaker talks about anticipating opponent's moves in simultaneity games and the optimization method of looking for saddle points.
Clips
This podcast discusses how optimization makes sense for single agents in robotics trying to optimize some objective function, and how there is a branch of optimization that explicitly looks for saddle points, which is uncommon since most optimization problems dislike them.
1:04:48 - 1:07:41 (02:53)
Optimization
Summary
This podcast discusses how optimization makes sense for single agents in robotics trying to optimize some objective function, and how there is a branch of optimization that explicitly looks for saddle points, which is uncommon since most optimization problems dislike them.
ChapterAnticipating Moves and Finding Saddle Points
Episode#74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI
PodcastLex Fridman Podcast
The concept of algorithmic game theory involves anticipating the moves of an opponent in order to find the best strategy.
1:07:41 - 1:09:17 (01:35)
Game Theory
Summary
The concept of algorithmic game theory involves anticipating the moves of an opponent in order to find the best strategy. By predicting the opponent's actions, players can make informed decisions and potentially come out on top.