Discussion
Why did Monte Carlo tree search succeed in go where minimax had not?
Answer: B
Decades of effort failed to produce a static evaluation of a go position accurate enough to be useful, whereas random playouts to the end supply an estimate needing no such knowledge. Go's branching factor of around two hundred and fifty is far larger than chess's, exact evaluation is intractable, and minimax applies perfectly well in principle. A method that sidesteps the component that could not be built is a different kind of solution from one that builds it better.
No comment is present. Be the first to comment.
Loading…
Post your comment
Please log in or register to post a comment.
Examination Categories
- SSC Exams
- Banking Exams
- Teaching Exams
- Civil Services Exams
- Railways Exams
- Engineering Recruitment Exams
- Defence Exams
- State Government Exams
- Police Exams
- Insurance Exams
- Nursing Exams
- Other Government Exams
- NRA CET
- PG Entrance Exams
- Campus Placement
- MBA Entrance Exams
- CUET and UG Entrance
- Judiciary Exams
- Regulatory Body Exams
- Software Development