Width Based Planning


Width Based Planning searches for solutions through a general measure of state novelty. When the dynamics of the problem are given through simulator engines such as the Atari games (ALE), GVGAI, or complex robotic and UaV flight simulators, novelty exploration yields state-of-the-art performance compared to known alternatives such as MCTS. Novelty exploration can be combined as well with the exploitation of goal-based heuristics within a general Best First Width Search. BFWS can solve efficiently classical planning problems even when the action model is hidden, opening exciting opportunities to model beyond declarative action representations.



(Disclaimer: this is not a comprehensive list, please get in touch to add relevant work that has been missed)

A position paper ` Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning' was presented as part of the Early Career Spotlight at IJCAI 2021.

Table of Contents

  1. Classical Planning over STRIPS/PDDL
  2. Classical Planning over Simulators
  3. Continous State Simulators
  4. Planning and Learning
  5. Planning over Factored Simulators in Functional STRIPS
  6. MDP over Simulators
  7. Multi Agent (Descentralisd & Privacy Preserving) Planning

Classical Planning over PDDL

Classical Planning over Simulators

Continous State Simulators

Pleanning and Learning

Planning over Factored Simulators in Functional STRIPS

Factored simulators accept finite domain variables, and mixed Declarative and Programatic (simulated) dynamics

MDP over Simulators

Multi Agent Descentralised and Privacy Preserving Planning

Width Based Algorithms in Action

Arcade Learning Environment

More Youtube Atari videos

Nir Lipovetzky
Nir Lipovetzky
Senior Lecturer in Artificial Intelligence

My interests span across research areas in AI planning, search, learning, verification, constraint programming, operations research and intention recognition.