Partially Observable Markov Decision Processes Part II Additional reading: Anthony R. Cassandra. Exact and Approximate Algorithms for Partially Observable Markov Decision Processes. Ph. D. Thesis. Brown University Department of Computer Science, Providence, RI
Defining problem and model so| ution: Minimizing localization error Comb imize gain in explored map bined Information Utilities Integrated Adaptive Information-based Exploration Algorithm
Vision-based SLAM Mobile Robot Localization And Mapping With Uncertainty using Scale-Invariant Visual Landmarks -e,lowe, Little Vikash Mansinghka Spren Riisgaard Outline
Outline Model-based programming The need for model-based reactive planning The Burton model-based reactive planner Artificial Intelligence Space Systems
Massachusetts Institute of Technology 16.412/6.834 Cognitive Robotics Distributed: Monday, 3/31/04 Objective The purpose of the following handout is to walk you step by step through the execution of the FF planning algorithm, on a simple example. The FF algorithm is presented in the paper: