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

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

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

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

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/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.215J The Passenger Mix Problem Outline -Definitions Formulations Column and Row Generation -Solution Approach Results Applications and Extensions

Summary Lecture #1 Airline schedules (Aircraft, crew, passengers) are optimized leading to: Little slacksidle time) Schedule dependencies Delay chain effects Causes of schedule disruptions Shortages of airline resources Shortages of airport resources Complex airline resource regulations Aircraft maintenance

The Extended Crew Pairing Problem with Aircraft Maintenance Routing Outline Review of Individual Problems Interdependence and motivation for an alternative approach Sequential Approaches Integrated Approaches Comparison of Models

Summary Lecture #2 Achieving good passenger service reliability at an acceptable operating costs Disrupted passengers suffer long delays on average(320 minutes) versus non disrupted passengers(14 minutes) Connecting itineraries have a much higher risk of being disrupted than local itineraries(2.7x) Late disruptions are often difficult to recover the