1.206J/16.77J/FSD215J The Crew Scheduling problem Outline Problem definition Sequential Solution Approach Crew pairing optimization model Branch-and-Price solution Branching strategies

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

1.206J/16.77J/ESD215 Multi-commodity network Flows A Keypath Formulation utline Path formulation for multi-commodity flow problems revisited Keypath formulation Example Keypath solution algorithm Column generation Row generation

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

1.206J/16.77J/ESD. 215] Airline Schedule planning Outline Sign-up Sheet syllabus The Schedule planning process Flight Networks Time-line networks · Connection networks

Outline Objectives Agents and Their building Blocks Principles for building agents Modeling formalisms Algorithmic Principles Building an agent: The Mars Exploration rover

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

How Might a mouse search a Maze for Cheese? heese · State Space Search? As a Constraint Satisfaction Problem? Goal-directed Planning As a rule or production System? What is missing? Ideas in this lecture Objective is to accumulate rewards rather than goal states Task is to generate policies for how to act in all situations rather than a plan for a single starting situation

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

Probabilistic model Integrate over al!p(x1 Bel(x,)=ap(=;|x,)p(x1a1,=12a1-2,=0) ap(E, x )p(x, Ix, 1,a, p(|x,)p(x,1x1,a1)p(x1)d