Intro
Preface
Towards a Characterization
History andWorking Groups
Problems with the Term ""Artificial Intelligence
Problems with the Term ""Computational Intelligence
Algorithmic Intelligence: Towards a Trade-off
Organization of the Book
Basics
Big Data
Research Areas
Application Areas
Contents
Part I Basics
Chapter 1 Programming Primer
1.1 Recursion
1.1.1 Divide-and-Conquer
1.1.2 Recursion on Texts
1.1.3 Factorial Numbers
1.1.4 Fibonacci Numbers
1.1.5 Ackermann Numbers
1.1.6 Ulam Numbers
1.2 Calculus
1.2.1 Square Roots

1.2.2 Euclid's Algorithm
1.2.3 Pascal's Triangle
1.2.4 Prime Factorization
1.2.5 Gaussian Elimination
1.2.6 Min-Max Problem
1.2.7 Quickselect and Quicksort
1.3 Backtracking
1.3.1 Post's Correspondence Problem
1.3.2 Towers-of-Hanoi
1.3.3 Mazes
1.3.4 The Queens Problem
1.3.5 Sudoku
1.4 Heuristic Search
1.4.1 Number Partitioning
1.4.2 The 15-Puzzle
1.4.3 Ranking and Unranking
1.4.4 Peg Solitaire
1.4.5 Traveling Salesman Problem
1.5 Randomization
1.5.1 Randomized Prime Number Tests
1.5.2 Mister X
1.5.3 Mastermind
1.5.4 Nim

1.5.5 Snake
1.5.6 PacMan
1.6 Bibliographic Notes
Chapter 2 Shortest Paths
2.1 Introduction
2.2 Dijkstra's Algorithm
2.3 General Priority Queues
2.3.1 k-ary Heaps
2.3.2 Fibonacci Heaps
2.3.3 Pairing Heaps
2.4 Bucket Priority Queues
2.4.1 Radix Heaps
2.4.2 Bucket Maps
2.4.3 Factorized Heaps
2.5 Cache-Efficient Flood-Filling
2.6 Experiments
2.7 Summary
2.8 Bibliographic Notes
Chapter 3 Sorting
3.1 Introduction
3.2 Heapsort
3.3 Strong Heapsort
3.3.1 Strong Heap Construction
3.3.2 Sorting with Strengthened Heaps

3.4 Improving Quicksort
3.5 Block Quicksort
3.6 Quick Mergesort
QuickXSort
QuickMergesort
3.7 Summary
3.8 Bibliographic Notes
Chapter 4 Deep Learning
4.1 Introduction
4.2 Case Study: TicTacToe
4.3 Case Study: Same Game
4.4 Case Study: Sokoban
4.4.1 Designing Loss Functions
4.4.2 Definition of L*
4.5 Greedy Best-First Search: Optimizing Rank
4.6 Summary
4.7 Bibliographic Notes
Chapter 5 Monte-Carlo Search
5.1 Introduction
5.2 Monte-Carlo Search
5.2.1 Upper Confidence Bounds Applied to Trees
5.2.2 Parallel Monte-Carlo Search

5.2.3 Nested Monte-Carlo
5.2.4 Nested Rollout Policy Adaptation
5.3 Beam NRPA
5.4 Refinements
5.5 Improving the Diversity
5.5.1 Improving Diversity in the NRPA Driver
5.5.2 Improving Diversity in the Policy Adaptation
5.6 Case Study: SameGame
5.7 Case Study: Snake-in-the-Box
5.8 Case Study: Vehicle Routing
5.9 Summary
5.10 Bibliographic Notes
Part II Big Data
Chapter 6 Graph Data
6.1 Introduction
6.2 Mathematical Encoding
6.3 PDDL Encodings
6.4 SAT Encodings
6.5 Game Encodings
6.6 Experiments
6.6.1 Clique
6.6.2 Graph Coloring