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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

Review: Constraint-based Interval Planning Simple Temporal Networks Schedulability and Scheduling Review: Temporal, Model-based Programming Managing Execution Uncertainty Execution with Dynamic Scheduling Execution with Explicit Models of Uncertainty

Defining problem and model so| ution: Minimizing localization error Comb imize gain in explored map bined Information Utilities Integrated Adaptive Information-based Exploration Algorithm

Outline Lightning Summary Black Box Model of SIFT SLAM Vision System Challenges in Computer Vision What these challenges mean for visual SLAM How SIFT extracts candidate landmarks How landmarks are tracked in SIFT SLAM Alternative vision-based SLAM systems Open questions

Vision-based SLAM Mobile Robot Localization And Mapping With Uncertainty using Scale-Invariant Visual Landmarks -e,lowe, Little Vikash Mansinghka Spren Riisgaard Outline

Leslie Pack Kaelbling, Michael. Littman and Anthony R. Cassandra, \Planning and Acting in Partially Observable Stochastic Domains,\ Artificial Intelligence, Vol. 101, 1998. j. Pineau,g. Gordon&s. Thrun\Point-based- value iteration: An anytime algorithm for POMDPs\. International Joint Conference on Artificial Intelligence(JCAI) Acapulco, Mexico. Aug

Outline Model-based programming The need for model-based reactive planning The Burton model-based reactive planner Artificial Intelligence Space Systems