The Lumberjack Algorithm for Learning Linked Decision Forests

By William T. B. Uther, Manuela M. Veloso. 2000

13 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

How researchers see this paper

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

In Artificial Intelligence

1. Nicholay Topin
2. Pooyan Jamshidi
3. Aaron M. Roth
4. Manuela M. Veloso
5. William T. B. Uther
6. Peter Stone
7. Sergey Levine
8. Ziyang Tang

In Computational Theory and Mathematics

1. Pooyan Jamshidi
2. Nicholay Topin
3. Aaron M. Roth
4. Manuela M. Veloso
5. William T. B. Uther
6. Sebastian Thrun

In Information Systems

1. Pooyan Jamshidi
2. Nicholay Topin
3. Aaron M. Roth
4. Manuela M. Veloso
5. William T. B. Uther

Citations in the corpus

• Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy (2019, methodology): “The Lumberjack algorithm [Uther and Veloso, 2000] constructs a Linked Decision Forest—like a decision tree but without the need for repeated internal structure which would otherwise occur.”
• TTree: Tree-Based State Generalization with Temporally Abstract Actions (2002, background): “We have recently published work on decomposing policies in a supervised learning setting [12].”