INFO 372:
Explorations in Artificial Intelligence
Prof. Carla P. Gomes gomes@cs.cornell.edu
Module 2
Example Applications

Previous Lecture:
What's involved in Intelligence?
A) Ability to interact with the real world to perceive, understand, and act speech recognition and understanding image understanding (computer vision)
INFO 372
B) Reasoning and Planning modelling the external world problem solving, planning, and decision making ability to deal with unexpected problems, uncertainties
C) Learning and Adaptation
We are continuously learning and adapting.
We want systems that adapt to us!

Previous Lecture
• AI goals:
– understand “intelligent” behavior
– build “intelligent” agents
• Intelligence  not necessarily human like

Issue: The Hardware
• The brain
– a neuron, or nerve cell, is the basic information
– processing unit (10^11 )
– many more synapses (10^14) connect the neurons
– cycle time: 10^(-3) seconds (1 millisecond)
• How complex can we make computers?
– 10^8 or more transistors per CPU
– supercomputer: hundreds of CPUs, 10^10 bits of RAM
– cycle times: order of 10^(-9) seconds (1 nanosecond)

Computer vs. Brain

• Conclusion
– In near future we can have computers with as many processing elements as our brain, but: far fewer interconnections (wires or synapses) much faster updates.
Fundamentally different hardware may require fundamentally different algorithms!
– Very much an open question.

Previous Lecture
• AI goals:
– understand “intelligent” behavior
– build “intelligent” agents
• Intelligence  not necessarily human like
Developing methods to match or exceed human performance in certain domains, possibly by very different means
 e.g., Deep Blue;
Focus of INFO372 (most recent progress).

1997:
Deep Blue beats the World Chess Champion vs.
I could feel human-level intelligence across the room
Gary Kasparov, World Chess Champion (human…)

How Intelligent is Deep Blue?
• Saying Deep Blue doesn't really think about chess is like saying an airplane doesn't really fly because it doesn't flap its wings.
- Drew McDermott

On Game 2
(Game 2 - Deep Blue took an early lead.
Kasparov resigned, but it turned out he could have forced a draw by perpetual check.)
This was real chess. This was a game any human grandmaster would have been proud of.
Joel Benjamin grandmaster, member Deep Blue team

Kasparov on Deep Blue
• 1996: Kasparov Beats Deep Blue
“I could feel --- I could smell --- a new kind of intelligence across the table.”
• 1997: Deep Blue Beats Kasparov
“Deep Blue hasn't proven anything.”

Game Tree Search
• How to search a game tree was independently invented by Shannon (1950) and Turing (1951).
• Technique called:
MiniMax search.
• Evaluation function combines material & position.

Game Tree Search

History of Search Innovations
•Shannon, Turing Minimax search
1950
•Kotok/McCarthy Alpha-beta pruning1966
•MacHack
•Chess 3.0+
Transposition tables1967
Iterative-deepening1975
•Belle
•Cray Blitz
•Hitech
Special hardware
Parallel search
1978
1983
Parallel evaluation 1985
•Deep Blue
All of the above 1997

Transposition Tables
• Introduced by Greenblat's Mac Hack (1966)
• Basic idea: caching
– once a board is evaluated, save it in a hash table, avoid reevaluating.
– called “transposition” tables, because different orderings
(transpositions) of the same set of moves can lead to the same board.
– Form of root learning (memorization)
– Don’t repeat blunders  can’t beat the computer twice in a row using same moves
Deep Blue --huge transposition tables (100,000,000+), must be carefully managed.

Search Depth Positions
6
8
2
4
10 (<1 second DB)
12
14 (5 minutes DB)
16
60
2,000
60,000
2,000,000
60,000,000
2,000,000,000
60,000,000,000
2,000,000,000,000
How many lines of play does a grand master consider?
Around 5 to 7

Special-Purpose and Parallel Hardware
• Belle (Thompson 1978)
• Cray Blitz (1993)
• Hitech (1985)
• Deep Blue (1987-1996)
– Parallel evaluation: allows more complicated evaluation functions
– Hardest part: coordinating parallel search
– Deep Blue never quite plays the same game, because of “noise” in its hardware!

Deep Blue
• Hardware
– 32 general processors
– 220 VSLI chess chips
• Overall:
200,000,000 positions per second
– 5 minutes = depth 14
• Selective extensions - search deeper at unstable positions
– down to depth 25 !

Tactics into Strategy
• As Deep Blue goes deeper and deeper into a position, it displays elements of strategic understanding . Somewhere out there mere tactics translate into strategy.
This is the closet thing I've ever seen to computer intelligence. It's a very weird form of intelligence, but you can feel it. It feels like thinking.
– Frederick Friedel (grandmaster), Newsday, May 9, 1997

Goals of INFO 372
Introduce the students to a range of computational modeling approaches and solution strategies using examples from AI and Information Science.
Formalisms:
Logical representations;
Constraint-based languages,
Mathematical programming – Linear and Integer programming;
Multi-agent formalisms (including adversarial games);
Solution strategies:
Logical inference;
General complete backtrack search; (e.g., Iterative Deepening)
Local search;
Dynamic Programming;
Game tree search (e.g., alpha-beta pruning)

Goals of INFO 372
Special models:
Satisfiability (SAT); Maximum SAT; Horn
Constraint Satisfaction; Binary Constraint Satisfaction;
Mixed Integer Programming, Linear Programming and
Network Flow Models;
Themes:
Expressiveness and efficiency tradeoffs of the various representation formalisms
 Students learn about the tradeoffs in modeling choices.
;
Concrete examples to move from one representation modeling formalism to another formalism;

Example of a reasoning formalism:
Constraint Satisfaction

Escher:
Waterfall, 1961

Escher:
Belvedere, May 1958

Escher:
Ascending and Descending, 1960

How do we Interpret the
Scenes in Escher’s Worlds?
Analysis of Polyhedral Scenes origins of Constraint Reasoning
 researchers in computer vision in the 60s-70s were interested in developing a procedure to assign 3dimensional interpretations to scenes;
They identified
Three types of edges
Four types of junctions

Edge Types
Hidden – if one of its planes cannot be seen represented with arrows:
 or

Convex – from the viewer’s perspective represented with
+
Concave – from the viewer’s perspective represented with
-
Huffman-Clowes
Labeling

Types of Junctions
Type of junction: L Fork T Arrow

Variables

Edges;
Domains  {+,-,  ,  }
Constraints:
Scene Interpretation
Constraint Reasoning Problem:
1- The different type junctions define constraints:
L, Fork, T, Arrow;
L = {(  ,  ) , (  ,  ), (+,  ), (  ,+), (-,  ), (  ,-)}
L(A,B)  the pair of values assigned to variables A,B has to belong in the set L;
Fork = { (+,+,+), (-,-,-), (  ,  ,-), (  ,-,  ),(-,  ,  )}
Fork(A,B,C)  the trio of values assigned to variables A,B,C has to belong in the set Fork;

CSP Model
• T = {( 
,

,

) , (

,

,

), (

,

,+), (

,-)}
T(A,B,C)  the trio of values assigned to variables A,B,C has to belong in the set T;
• Arrow = { ( 
,

,+), (+,+,-), (-,-,+)}
Arrow(A,B,C)  the trio of values assigned to variables A,B,C has to belong in the set Arrow;
2- For each edge XY its reverse YX has a compatible value
Edge = { +,+), (-,-), (  ,  ),(  ,  )}
Edge(A,B)  the pair of values assigned to variables A,B has to belong in the set Edge;

CSP Model - Cube
A
C
E F
B
G
D
How to label the cube?

CSP Model
Variables : Edges: AB, BA,AC,CA,AE,EA,CD,
DC,BD,DB,DG,GD,GF,FG,EF,FE,AE,EA;
Domains  {+,-,  ,  }
Constraints:
L(AC,CD); L(AE,EF); L(DG,GF);
Arrow(AC,AE,AB); Arrow(EF,FG,BF); Arrow(CD,DG,DB);
Fork(AB,BF,BD);
Edge(AB,BA); Edge(AC,CA); Edge(AE,EA);
Edge(EF,FE); Edge(BF,FB); Edge(FG,GF);
A
Edge(CD,DC); Edge(BD,DB); Edge(DG,GD);
E
C
F
B
G
D

CSP Model
Variables : Edges: AB, BA,AC,CA,AE,EA,CD,
DC,BD,DB,DG,GD,GF,FG,EF,FE,AE,EA;
Domains  {+,-,  ,  }
Constraints:
L(AC,CD); L(AE,EF); L(DG,GF);
Arrow(AC,AE,AB); Arrow(EF,FG,BF); Arrow(CD,DG,DB);
Fork(AB,BF,BD);
A
Edge(AB,BA); Edge(AC,CA); Edge(AE,EA);
Edge(EF,FE); Edge(BF,FB); Edge(FG,GF);
Edge(CD,DC); Edge(BD,DB); Edge(DG,GD);
C
E
+
+
B
+
F
G
D
One (out of four) possible labelings

The Impossible Objects is Escher’s Worlds
Penrose & Penrose Stairs
Penrose Triangle

Impossible Objects:
No labeling!

Other examples using a Constraint Satisfaction formalism

Sudoku
Constraint Satisfaction Problem (CSP) and Satisfiability and Integer
Programming
9 55 ~ 3x 10 52 possible completions

Latin Squares
Given an N X N matrix, and given N colors, a Latin Square of order N is a a colored matrix, such that:
-all cells are colored.
- each color occurs exactly once in each row.
- each color occurs exactly once in each column.
Quasigroup or Latin Square
(Order 4)
Constraint Satisfaction Problem (CSP) and Satisfiability and Integer
Programming

Latin Squares
Given an N X N matrix, and given N colors, a Latin Square of order N is a a colored matrix, such that:
-all cells are colored
-a color is not repeated in a row
-a color is not repeated in a column
Quasigroup or Latin Square
(Order 4)
Constraint Satisfaction Problem (CSP) and Satisfiability and Integer
Programming

Latin Square Completion Problem
Given a partial assignment of colors (10 colors in this case), can the partial latin square be completed so we obtain a full Latin square ?
Example:
32% preassignment 10 68 possible completions

Wavelength
Division
Multiplexing (WDM) the most promising technology for the next generation of wide-area backbone networks.
Fiber Optic Networks
Nodes connect point to point fiber optic links
Each fiber optic link supports a large number of wavelengths
Nodes are capable of photonic switching
--dynamic wavelength routing -which involves the setting of the wavelengths.

Input Ports
3
4
1
2
Routing in Fiber Optic Networks preassigned channels
3
4
1
Output Ports
2
Routing Node
How can we achieve conflict-free routing in each node of the network?
Dynamic wavelength routing is a NP-hard problem.

LSCP Application Example:
Routers in Fiber Optic Networks
Dynamic wavelength routing in Fiber Optic Networks can be directly mapped into the Latin Square Completion Problem.
• each channel cannot be repeated in the same input port (row constraints);
• each channel cannot be repeated in the same output port (column constraints);
Output ports
Input Port
1
2
3
4
Output Port
1
2
3
4
CONFLICT FREE
LATIN ROUTER

Design of Statistical Experiments
We have 5 treatments for growing beans. We want to know what treatments are effective in increasing yield, and by how much.
The objective is to eliminate bias and distribute the treatments somewhat evenly over the test plot.
Latin Square Analysis of Variance
A D E B C
C B A E D
D C B A E
E A C D B
B E D C A
(*) Already in use in this sub-plot

Spatially Balanced Latin Squares
Really hard to build balanced LS’s

Timetabling:
Constraint Satisfaction Problem (CSP) and
Integer Programming
The problem of generating schedules with complex constraints (in this case for sports teams).
An 8 Team Round Robin Timetable
Period 1
Period 2
Period 3
Period 4
Week 1
0 vs 1
2 vs 3
4 vs 5
6 vs 7
Week 2
0 vs 2
1 vs 7
3 vs 5
4 vs 6
Week 3
4 vs 7
0 vs 3
1 vs 6
2 vs 5
Week 4
3 vs 6
5 vs 7
0 vs 4
1 vs 2
Week 5
3 vs 7
1 vs 4
2 vs 6
0 vs 5
Week 6
1 vs 5
0 vs 6
2 vs 7
3 vs 4
Week 7
2 vs 4
5 vs 6
0 vs 7
1 vs 3
28 28 ~ 3.3 x 10 40 possibilities

Why Sports Scheduling???
Source:
Mike Trick
Big Business!
US National TV pays $500 million / year for baseball
College basketball conferences get up to $30 million
Manchester United has (had) a market cap of £400 million
No rights holder wants to pay those sums and then get a “bad” schedule
Difficult to automate:
Huge variety of problem types
Small instances are difficult
Strong break between easy/hard (for all algorithms)
Significant theoretical background
CP and IP differ in modeling
CP has clean models with [1..n] variables
IP uses 0-1 variables reasonably naturally
Practical interest in instances at the easy/hard interface

Graph Coloring n n ~ possible colorings for n nodes
Coloring the nodes of the graph:
What’s the minimum number of colors such that any two nodes connected by an edge have different colors?

Constraint Satisfaction Problem (CSP) and Satisfiability and Integer Programming
Graph Coloring

Another example of a reasoning formalism
A restricted form of Constraint Satisfaction:
Satisfiability

Propositional Satisfiability problem
Satifiability (SAT): Given a formula in propositional calculus, is there an assignment to its variables making it true?
We consider clausal form, e.g.:
( a
OR
NOT b
OR
NOT c )
AND
( b
OR
NOT c )
AND
( a
OR c )
2 n possible assignments
SAT: prototypical hard combinatorial search and reasoning problem. Problem is NP-Complete. (Cook 1971)
Surprising “power” of SAT for encoding computational problems.

Significant progress in
Satisfiability Methods
Software and hardware verification – complete methods are critical - e.g. for verifying the correctness of chip design, using
SAT encodings
Going from 50 variable, 200 constraints to 1,000,000 variables and 5,000,000 constraints in the last 10 years
Applications:
Hardware and
Software Verification
Planning,
Protocol Design, etc.
Current methods can verify automatically the correctness of > 1/7 of a Pentium IV.

A “real world” example

Bounded Model Checking instance: i.e.
((not x
1
) or x
7
) and ((not x
1
) or x and … etc.
6
)

10 pages later:
…
(x
177 or x
169 or x
17 or x or x
9
161 or x or x
1
153
… or (not x
185
)) clauses / constraints are getting more interesting…

!!!
a 59-cnf clause…
…
4000 pages later:

Finally, 15,000 pages later:
Note that:
The Chaff SAT solver solves this instance in less than one minute.
… !!!

Another example of a reasoning formalism
Integer Programming

Knapsack Problem (one resource)
A hiker trying to fill her knapsack to maximum total value. Each item she considers taking with her has a certain value and a certain weight. Goal – maximize the value of the contents of the knapsack considering the overall weight constraint.
• This problem is an abstraction with many practical applications:
Project selection and capital budgeting allocation problems
Storing a warehouse to maximum value given the indivisibility of goods and space limitations
Sub-problem of other problems e.g., generation of columns for a given model in the course of optimization – cutting stock problem (beyond the scope of this course)

Investment budget = $14,000
Capital Budgeting Example
Investment 1 2 3 4 5 6
Cash
Required
(1000s)
$5 $7 $4 $3 $4 $6
NPV added $16 $22
(1000s) maximize 16x
1
+ 22x
2
$12
+ 12x
3
$8
+ 8x
4
$11
+11x
5
$19
+ 19x
6 subject to 5x
1
+ 7x
2
+ 4x
3
+ 3x
4
+4x
5
+ 6x
6

14 x j binary for j = 1 to 6

Binary Optimization:
Applications in Regional
Planning
Zevi Azzaino
Jon Conrad
Carla Gomes
Models
Knapsack and Variants
Maximize
I  i

1 c i x i
Subject to
I  i

1 w i x i

M x i

{ 0 , 1 } and i

1 ,...
I
Stream Footage
Phosphorous
Pathogen
Parcel Size
Parcel Value
Budget Constraint
Town of Skaneateles:
-1834 parcels
-12341 acres
52 land use class.
Riparian Buffer in the
Skaneateles Lake Watershed
Objective: Identify the best collection of parcels to include in a riparian buffer subject to a budget constraint
2,345 barns registered in year 2000
464 barns in Finger Lakes Region only.
Contribution to a scenic landscape or agricultural setting
Historic significance
Budget: $2 million; Max of $25,000 grant per barn
Office of Parks, Recreation and
Historic Preservation
Preservation in NY State
Important Natural Community
Geological Importance
Aesthetic/Cultural Qualities
Budget Constraint
Unique Natural Areas in
Tompkins County

Southwestern Airways Crew Scheduling
• Southwestern Airways needs to assign crews to cover all its upcoming flights.
• Simple example  assigning 3 crews based in San Francisco (SFO) to 11 flights.
Question: How should the 3 crews be assigned 3 sequences of flights so that every one of the 11 flights is covered?

Other Integer Programming problems

Seat tl e
(SEA)
San Francis co
(SFO)
Los Angel es
(LAX)
Southwestern Airways Flights
Denver
(DEN)
Chi cago
ORD)

Data for the Southwestern Airways Problem
Flights
1. SFO–LAX
2. SFO–DEN
3. SFO–SEA
4. LAX–ORD
5. LAX–SFO
6. ORD–DEN
7. ORD–SEA
8. DEN–SFO
9. DEN–ORD
10. SEA–SFO
11. SEA–LAX
Cost, $1,000s
Feasible Sequence of Flights (pairings)
1 2 3 4 5 6 7 8 9 10 11 12
1 1 1 1
1 1 1 1
2
1
2
1
3
2
1
3 2
5 5
1
3
3 3 4
3 3 3 3 4
2
2
4 4
2
2
4
2
4
5
2 4
2
4
5
2
2 3 4 6 7 5 7 8 9 9 8 9

Algebraic Formulation
Let x j
= 1 if flight sequence (paring) j is assigned to a crew; 0 otherwise. ( j
= 1, 2, … , 12).
Minimize Cost = 2 x
1
+ 3 x
2
+ 4 x
3
+ 6 x
4
+ 7 x
5
+ 5 x
6
+ 7 x
7
+ 8 x
8
+ 9 x
9
+ 9 x
10
+ 8 x
11
+ 9 x
12
(in $thousands) subject to pairings
Flight 1 covered:
Flight 2 covered:
:
Flight 11 covered:
Three Crews: x
1
+ x
4 x
2
+ x
5
+ x
7
+ x
8
+ x
10
+ x
11
≥ 1
≥ 1
: x
6
+ x
9
+ x
10
+ x
11
+ x
12
≥ 1 x
1
+ x
2
+ x
3
+ x
4
+ x
5
+ x
6
+ x
7
+ x
8
+ x
9
+ x
10
+ x
11
+ x
12
≤ 3 and x j are binary ( j
= 1, 2, … , 12).

Combinatorial Problems

Combinatorial Problems
•
Many computational tasks, such as planning or scheduling, can in principle be reduced to an exploration of a large set of all possible scenarios.
•
Try all possible schedules, try all possible plans, pick the best.
Problem: combinatorial explosion!

AI PLANNING
In AI, planning involves the generation of an action plan (i.e. a sequence of actions) for an agent, such as a robot or a software system or a living artefact, that can alter its surroundings.
Planning implies the notion of synthesis: synthesis of actions, to go from an initial state to a goal state.
Examples:
•plan to perform astronomical observations for the Hubble space telescope;
•plan for a robot to assemble pieces in a factory

Planning Example:
Blocks world
• objects: blocks and a table
• actions: move blocks ‘on’ one object to ‘on’ another object
• goals: configurations of blocks
• plan: sequence of actions to achieve goals
A B
D
C
D
C
B
A
Initial State Goal State
T

A
Blocks world: propositional and first order logic representation
B
D
C
T
Knowledge Base:
On(A,T)^On(B,T)^On(C,T)^On(D,C)
^Block(A)^Block(B)^Block(C)^Block(D) )^Table(T)
^Clear(A)^Clear(B)^Clear(D)
Move(A,T,D)
B
A
D
C
T
KB:
On(A,D)^On(B,T)^On(C,T)^On(D,C)
^Block(A)^Block(B)^Block(C)^Block(D) ^Table(T)
^Clear(A) ^Clear(B)

Planning Complexity
Planning (single-agent) : find the right sequence of actions
HARD : 10 actions, 10! = 3 x 10 6 possible plans
Contingency planning (multi-agent) : actions may or may not produce the desired effect!
100 ! = 9.33262154 × 10 157
1 out of 10
2 out of 9
4 out of 8
REALLY HARD : 10 x 9 2 x 8 4 x 7 8 x … x 2 256 = 10 224 possible contingency plans!

Exceptions:
Some Kinds of Networks

Networks are Everywhere
Physical Networks
Road Networks
Railway Networks
Airline traffic Networks
Electrical networks, e.g., the power grid
Computer networks
New areas of application

Social networks - e.g. relationships networks (6 degrees of
Kevin Bacon
 ); communities such as researchers, CEO’s etc
Economic/ technological networks – e.g. patents:

Applications of Network Optimization
Applications
Physical analog of nodes
Physical analog of arcs
Flow
Communication systems
Hydraulic systems
Integrated computer circuits
Mechanical systems
Transportation systems phone exchanges, computers, transmission facilities, satellites
Cables, fiber optic links, microwave relay links
Voice messages,
Data,
Video transmissions
Pumping stations
Reservoirs, Lakes
Gates, registers, processors
Pipelines
Wires
Water, Gas, Oil,
Hydraulic fluids
Electrical current
Joints
Rods, Beams,
Springs
Heat, Energy
Intersections,
Airports,
Rail yards
Highways,
Airline routes
Railbeds
Passengers, freight, vehicles, operators

Network Flow Algorithms:
• “Nice” combinatorial problem (Min Cost Flow) – exception to combinatorial explosition
 polynomial scaling

!
• General formulation for special problems:
– shortest paths
– transportation problem
– assignment problem
– plus more
• Important subproblem of many optimization problems, including multicommodity flows

But most interesting real-world problems are:
NP-Complete and
Start
Goal
Experiment
Design
NP-Hard Problems
Planning and Scheduling
And Supply Chain Management
Satisfiability
(A or B) (D or E or not A )
Capital Budgeting
And Financial Appl.
Combinatorial
Auctions
Data Analysis
& Data Mining
Information
Retrieval
Protein
Folding
And Medical
Applications
EXPLOSIVE
COMBINATORICS
Hard Computational
Software & Hardware
Verification
Fiber optics routing
Many more applications!!!
Problems
Scale Exponentially
Require powerful computational and mathematical tools!
EXPONENTIAL-TIME
ALGORITHMS
EXPONENTIAL
FUNCTION
POLYNOMIAL
FUNCTION

Goals of INFO 372
Introduce the students to a range of computational modeling approaches and solution strategies using examples from AI and Information Science.
Formalisms:
Logical representations;
Constraint-based languages,
Mathematical programming – Linear and Integer programming;
Multi-agent formalisms (including adversarial games);
Solution strategies:
Logical inference;
General complete backtrack search; (e.g., Iterative Deepening)
Local search;
Dynamic Programming;
Game tree search (e.g., alpha-beta pruning)

Goals of INFO 372
Special models:
Satisfiability (SAT); Maximum SAT; Horn
Constraint Satisfaction; Binary Constraint Satisfaction;
Mixed Integer Programming, Linear Programming and
Network Flow Models;
Themes:
Expressiveness and efficiency tradeoffs of the various representation formalisms
 Students learn about the tradeoffs in modeling choices.
;
Concrete examples to move from one representation modeling formalism to another formalism;