Edcorner Learning, Edcredibly Team. Artificial Intelligence - Logic & Algorithms for problem solving. Volume 2

Independently published, 2021. — 701 p. — ASIN B096KYY5RX.Artificial intelligence is, at its core, a system that can perform a task using intelligence that mirrors (or is better than) human intelligence. Theoretically, any task that requires human intelligence to accomplish could instead be performed by artificial intelligence assuming the system has the adequate information and capabilities programmed. It accomplishes this by utilizing processes such as machine learning to scour sets of data and utilizing algorithms (instructions, or a list of rules a computer should follow to solve a problem), to discover trends in data and provide insights for decision-making. This book will give you practical knowledge about different logic and algorithm for more than 140+ Problems than can be solved by AI.AI Module 1 Introduction
The problems that need special attention.
Why study AI.
AI techniques.
Heuristic-based search.
Knowledge representation and inference.
Reason for incomplete information.
Fault tolerance.
AI Module 2 State Space Search IState Space.
Solving AI Problems.
Examples of production rules.
Applying rules to solve the problem.
AI Module 3 State Space Search II
A state space search for a problem with more prerequisite.
A missionary cannibal problem.
The farmer fox chicken grain problem.
The combinatorial explosion.
AI module 4 Guided and unguided searchGuided and unguided search.
Generate and test.
Breadth-first search (BFS).
Depth-first search (DFS).
Depth bounded DFS (DBDFS).
Comparison.
AI Module 5 Heuristic search methodsHeuristic function.
Hill climbing.
Best first search.
Branching factor.
Solution space search.
AI module 6 Other Search methodsVariable neighborhood decent.
Beam search.
Tabu search.
Simulated annealing.
AI module 7 Problems with search methods and solutionsLocal and global heuristic functions.
Plateau and ridge.
Frame problem.
Problem decomposability and dependency.
Independent or Assisted Search.
Search for Explanation.
Iterative Hill Climbing.
AI Module 8 Genetic algorithm & Travelling salesman problem
Genetic Algorithms.
Basic operations.
Selection.
Recombination.
Mutation.
Traveling salesman problem.
PMX (Partially Mapped Crossover).
OX (Order Crossover).
CC (Cyclic Crossover).
Other Representations.AI module 9 Neural networksBrain and CPU work differently.
The artificial neural networks (ANNs).
The Neuron and the ANN.
The process of learning.
Learning for correct values and speed of learning.
Generalization.
The black box of reasoning.
Unsupervised Learning.
AI module 10 Multi-layer feed-forward networks and learning
Prerequisites to the Backpropagation algorithm.
Choosing the number of nodes at each layer.
AI module 11 Learning in Back Propagation networkLearning in Back Propagation network.
Geometrical view of the learning process.
Content addressability and Hopfield networks.AI module 12 Ant Colony Optimization, branch and bound, refinement search.Ant Colony Optimization.
How ants discover optimal paths.
Solving TSP problem using ACO.
Calculating the pheromone value.
Branch and bound.
AI module 13 The A* AlgorithmPrerequisites for A*.
The Graph Exploration using A*.
A* algorithm version 1.
What if the g value is not identical for the entire path? How.
paths are explored.
The A* algorithm version 2.
The back propagation of estimates.
Why h’ and not h?
AI Module 14 Admissibility of A*, Agendas, and AND-OR graphsAdmissibility.
The effect of g.
The effect of h’.
Agenda Driven Search.
The AND-OR graphs.
AI Module 15 Iterative deepening A*, Recursive Best First, AgentsIDA*.
IDA* algorithm.
Limitations of IDA*.
Recursive best-first search.
Agents.
Agent Environment.
Rationality.
Learning.
AI Module 16 Introduction
Objectives and planning.
Types of planning.
Agent-Based Planning.
Forward planning.
Backward planning.
Choosing between forward and backward reasoning.
AI Module 17 ProgressionProgression.
Relevant and non-relevant actions.
Regression for goal-directed reasoning.
Goal Stack Planning (GSP).
GSP example.
Testing the validity of a plan.AI Module 18 Problem with GSPProblem with GSP.
Sussman’s Anomaly.
Another route.
Plan space planning.
Solving Sussman’s anomaly.AI module 19 Game Playing AlgorithmsCharacteristics of game-playing algorithms.
History.
Types of Games.
Game trees.AI module 20 Prerequisites to MiniMax and other ralgorithmsThe process of MiniMax.
Static Evaluation Function.AI module 21 MiniMax algorithmFunctioning of MiniMax Algorithm.
MiniMax Algorithm.
The process.
Need for improvement.AI Module 22 Alpha Beta cutoffsMiniMax with Alpha Beta Pruning.
Algorithm.
The process.
Futility cutoff.AI Module 23 Other Refinements.Waiting for stability.
Look Beyond the Horizon.
Using predetermined moves.
Use other algorithms.
State Space Search *(SSS*).
B* search.AI Module 24 Propositional and Predicate logicFormal Logic.
Entailment in Formal Logic.
Proportional logic.
Need for Predicate logic.
Predicate Structure.
Using Universal and Existential quantifiers.
Representing facts and rules.AI Module 25 Using Predicate logicThe impact of universal and existential quantifiers.
Incomplete information.
Answering a question.
Using functions.
Rules that do not work.
Unification process.AI Module 26 ResolutionConversion to Clausal form.
Producing a proof.
Proving using resolution.AI Module 27 Knowledge representation using NMR Sand ProbabilityProblems with predicate logic.
Non-monotonic Reasoning system.
The basis for non-monotonic reasoning.
NMRS Processing.
Uncertainty and related issues.
Statistical reasoning and Probability.
Bay’s formula.
Certainty factors.AI Module 28 Using Fuzzy logic, Frames, and Semantic Net for knowledge representationThe need for Fuzzy logic.
Fuzzy sets and fuzzy logic.
Using multiple Fuzzy Sets to implement the rule.
Frames.
Frame Systems.
Semantic Networks.
The importance of indicating objects.
Representing quantification.
AI Module 29 Stronger knowledge representation methods: Conceptual DependencyConceptual Dependency.
Primitives actions for CD.
Conceptual categories.
Conceptual Roles and Tenses.
Syntactical Rules.AI Module 30 Syntactical rules for CD and CD’sSyntax rules.
Using fuzzy names.
Some complex cases.
Advantages and Shortcomings of CD.
AI Module 31 ScriptsScripts.
Some other similar attempts.AI Module 32 Introduction to Expert SystemsES Tasks.
What ES entails.
The ES Problem Solving.
Two different types of ES knowledge.
Types of domain knowledge.AI Module 33 ES architecture and Knowledge EngineeringES Architecture.
Query processor and client modeling.
Interface.
Knowledge storage and maintenance.
Knowledge Engineering.
The inference logic.
Updating Knowledge.
Explanation system.
ES levels.AI Module 34 ES Development process-ISE challenges.
ES Development steps.
Identification.
Identifying the problem.
Assessment of applicability.
Availability of the expert.
Defining the scope.
Economic feasibility.
Final Selection.AI Module 35 ES Development process-IIPrototype Construction and Conceptualization.
Formalization.
Project planning.
Test Planning.
Product release planning.
Support planning.
Implementation Planning.
Implementation.
Testing and Evaluation.
Performance assessment.AI Module 36 Machine LearningMachine Learning.
The process of learning.
The ingredients of the machine learning process.
Supervised and Unsupervised learning.
Training testing and generalization.