GAME-PLAYING AND GAME-LEARNING AUTOMATA

By Donald Michie. 1966

Subfields: Reinforcement Learning, Game & Decision Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. Sergey Levine
2. Pieter Abbeel
3. Karl Tuyls
4. Yuxi Li
5. Julien Pérolat
6. Marc Lanctot
7. Pedro A. Ortega
8. Jean-Baptiste Lespiau

In Game & Decision Theory

1. Karl Tuyls
2. Julien Pérolat
3. Jean-Baptiste Lespiau
4. Shayegan Omidshafiei
5. Marc Lanctot
6. Vinícius Flores Zambaldi
7. Daniel Hennes
8. Dustin Morrill

Citations in the corpus

• OpenSpiel: A Framework for Reinforcement Learning in Games (2019, methodology): “The Python implementation of minimax includes expectiminimax [41] as well, which also backs up expected values at chance nodes.”
• Causally Correct Partial Models for Reinforcement Learning (2020, methodology): “We will use the classical expectimax search (Michie, 1966; Russell & Norvig, 2009) or a variant of MuZero Monte-Carlo Tree Search (MCTS) (Schrittwieser et al., 2019).”
• Computer shogi (2002, background): “These routines are often referred to as SOMA (“Swapping Off Material Analyzer”) [39,43].”
• Free Energy and the Generalized Optimality Equations for Sequential Decision Making (2012, background): “In these cases, the agent bases its decisions on the Expectiminimax rule (Michie, 1966).”