1.206J/16.77J/ ESD. 215 Airline Schedule planning: multi-commodity Flows Outline Applications Problem definition Formulations Solutions Results

1.206J/16.77J/ESD.215J Airline Schedule Planning: Multi-commodity Flows Outline Applications Problem definition Formulations Solutions Computational results Integer multi-commodity network flow

5.1 Landsat 系列 5.2 Spot系列 5.3 气象卫星系列 5.4 海洋卫星系列

Markov Decision Processes and Reinforcement Learning · Motivation earning policies through reinforcement Q values

Robot Localization using SIR Take sample set X+_11 ll. iterate For each sample xt-1 1) Sample from p(X,1 X+1, a) 2) Attach importance weights

Assignment problem Persons and tasks connected by links Objective: Minimize total task time minutes Carburetor Manny 4 minutes Valves

Integer Programs LP: Maximize 3x1+ 4x2 IP: Maximize 3x1+ 4x2 Subiect to: Subject te

WORLD observations Plant actions P(S sense act Diagnostic Agent Monitors diagnoses AGENT Repairs avoids Probes and tests

Assignment Reading Model-based Diagnosis-Lecture notes Propositional Logic AIMA Chapter 6 Problem set Model-based Diagnosis implementation Propositional Logic 16.413: Hello World implementation Due Monday, November 17th

Histol · Kautz and selman.1992 Inspired by improvements in satisfiabity algorithms Big Idea Encode planning problem as a(very large) logical formula Initial-state all-possible-actions goal Find a satisfying assignment to action-time propositions, and we have a plan