Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: A Rule-Based Approach.
Giulio MazziAlberto CastelliniAlessandro FarinelliPublished in: AAMAS (2021)
Keyphrases
- monte carlo
- partially observable
- state space
- markov decision processes
- reinforcement learning
- partial observability
- decision problems
- markov chain
- dynamical systems
- planning domains
- markov decision problems
- belief space
- particle filter
- monte carlo simulation
- partial observations
- infinite horizon
- importance sampling
- belief state
- decision making
- partially observable markov decision processes
- planning problems
- temporal difference
- heuristic search
- monte carlo tree search
- state variables
- finite state
- optimal strategy
- ai planning
- decision makers
- visual tracking
- dynamic programming