Artificial Intelligence – Logic & Algorithms for problem solving Volume 2
- Length: 701 pages
- Edition: 1
- Language: English
- Publication Date: 2021-06-02
- ISBN-10: B096KYY5RX
- Sales Rank: #261575 (See Top 100 Books)
Artificial Intelligence – Logic & Algorithms for problem solving Volume 2
You can also find “Artificial Intelligence – Logic & Algorithms for problem solving Volume 1” in our store.
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 list of rules a computer should follow to solve a problem), to discover trends in data and provide insights for decision-making.
ABOUT THE BOOK:
This book will give you practical knowledge about different logics and algorithm to 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 with incomplete information Fault tolerance AI Module 2 State Space Search I Introduction State 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 Introduction Guided and unguided search Generate and test Breadth first search (BFS) Depth first search (DFS) Depth bounded DFS (DBDFS) Comparison AI Module 5 Heuristic search methods Introduction Heuristic function Hill climbing Best first search Branching factor Solution space search AI module 6 Other Search methods Introduction Variable neighbourhood decent Beam search Tabu search Simulated annealing AI module 7 Problems with search methods and solutions Introduction Local 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 Summary AI module 9 Neural networks Introduction Brain and CPU works 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 number of nodes at each layer AI module 11 Introduction Learning in Back Propagation network Geometrical view of the learning process Content addressability and Hopfield networks Summary AI module 12 Ant Colony Optimization, branch and bound, refinement search. Introduction 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* Algorithm Introduction Prerequisites 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 graphs Introduction Admissibility The effect of g The effect of h’ Agenda Driven Search The AND-OR graphs AI Module 15 Iterative deepening A*, Recursive Best First, Agents Introduction IDA* 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 Introduction Progression Relevant and non-relevant actions Regression for goal directed reasoning Goal Stack Planning (GSP) GSP example Testing the validity of a plan Summary AI Module 18 Introduction Problem with GSP Sussman’s Anomaly Another route Plan space planning Solving Sussman’s anomaly Summary AI module 19 Game Playing Algorithms Introduction Characteristics of game playing algorithms History Types of Games Game trees Summary AI module 20 Prerequisites to MiniMax and other algorithms Introduction The process of MiniMax Static Evaluation Function Summary AI module 21 MiniMax algorithm Introduction Functioning of MiniMax Algorithm MiniMax Algorithm The process Need for improvement Summary AI Module 22 Alpha Beta cutoffs Introduction MiniMax with Alpha Beta Pruning Algorithm The process Futility cutoff Summary AI Module 23 Other Refinements Introduction Waiting for stability Look Beyond the Horizon Using predetermined moves Use other algorithms State Space Search *(SSS*) B* search Summary AI Module 24 Propositional and Predicate logic Introduction Formal Logic Entailment in Formal Logic Proportional logic Need for Predicate logic Predicate Structure Using Universal and Existential quantifiers Representing facts and rules Summary AI Module 25 Using Predicate logic Introduction The impact of universal and existential quantifiers Incomplete information Answering a question Using functions Rules that do not work Unification process Summary AI Module 26 Resolution Introduction Conversion to Clausal form Producing a proof Proving using resolution Summary AI Module 27 Knowledge representation using NMRS and Probability Introduction Problems 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 Summary AI Module 28 Using Fuzzy logic, Frames and Semantic Net for knowledge representation Introduction The need for Fuzzy logic Fuzzy sets and fuzzy logic Using multiple Fuzzy Sets to implement rule Frames Frame Systems Semantic Networks The importance of indicating objects Representing quantification AI Module 29 Stronger knowledge representation methods: Conceptual Dependency Introduction Conceptual Dependency Primitives actions for CD Conceptual categories Conceptual Roles and Tenses Syntactical Rules Summary AI Module 30 Syntactical rules for CD and CD’s Introduction Syntax rules Using fuzzy names Some complex cases Advantages and Shortcomings of CD AI Module 31 Scripts Introduction Scripts Some other similar attempts Summary AI Module 32 Introduction to Expert Systems Introduction ES Tasks What ES entails The ES Problem Solving Two different types of ES knowledge Types of domain knowledge Summary AI Module 33 ES architecture and Knowledge Engineering Introduction ES Architecture Query processor and client modelling Interface Knowledge storage and maintenance Knowledge Engineering The inference logic Updating Knowledge Explanation system ES levels Summary AI Module 34 ES Development process-I Introduction SE challenges ES Development steps Identification Identifying the problem Assessment of applicability Availability of the expert Defining the scope Economic feasibility Final Selection Summary AI Module 35 ES Development process-II Introduction Prototype Construction and Conceptualization Formalization Project planning Test Planning Product release planning Support planning Implementation Planning Implementation Testing and Evaluation Performance assessment Summary AI Module 36 Machine Learning Introduction Machine Learning The process of learning The ingredients of machine learning process Supervised and Unsupervised learning Training testing and generalization Naïve Bayesian classifier Hidden Markov Model(HMM) Concept learning Clustering Deep Learning Summary
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