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Artificial Intelligence (AI) :

It is a pocket Engineering Book and wherever you can read this. In this book cover most of topics and explain with figure, tables etc.

It covers more than 600 topics of Artificial Intelligence, Automata, Real-time systems & Neuro fuzzy in detail. The topics are divided into 5 units.

It is field of study on creating computer & software capable of intelligent behavior.

The App is a Handbook for easy understanding the Artificial Intelligence (AI). It covers 142 around topics of Artificial Intelligence in detail.

The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.

Some of the topics Covered in this application are :

1. Turing test
2. Introduction to Artificial Intelligence
3. History of AI
4. The AI Cycle
5. Knowledge Representation
6. Typical AI problems
7. Limits of AI
8. Introduction to Agents
9. Agent Performance
10. Intelligent Agents
11. Structure Of Intelligent Agents
12. Types of agent program
13. Goal based Agents
14. Utility-based agents
15. Agents and environments
16. Agent architectures
17. Search for Solutions
18. State Spaces
19. Graph Searching
20. A Generic Searching Algorithm
21. Uninformed Search Strategies
22. Breadth-First Search
23. Heuristic Search
24. Mathematical formulation of the inductive learning problem
25. Search Tree
26. Depth first Search
27. Properties of Depth First Search
28. Bi-directional search
29. Search Graphs
30. Informed Search Strategies
31. Methods of Informed Search
32. Greedy Search
33. Proof of Admissibility of A*
34. Properties of Heuristics
35. Iterative-Deepening A*
36. Other Memory limited heuristic search
37. N-Queens eample
38. Adversarial Search
39. Genetic Algorithms
40. Games
41. Optimal decisions in Games
42. minimax algorithm
43. Alpha Beta Pruning
44. Backtracking
45. Consistency Driven Techniques
46. Path Consistency (K-Consistency)
47. Look Ahead
48. Propositional Logic
49. Syntax of Propositional Calculus
50. Knowledge Representation and Reasoning
51. Propositional Logic Inference
52. Propositional Definite Clauses
53. Knowledge-Level Debugging
54. Rules of Inference
55. Soundness and Completeness
56. First Order Logic
57. Unification 58. Semantics
59. Herbrand Universe
60. Soundness, Completeness, Consistency, Satisfiability
61. Resolution
62. Herbrand Revisited
63. Proof as Search
64. Some Proof Strategies
65. Non-Monotonic Reasoning
66. Truth Maintenance Systems
67. Rule Based Systems
68. Pure Prolog
69. Forward chaining
70. backward Chaining
71. Choice between forward and backward chaining
72. AND/OR Trees
73. Hidden Markov Model
74. Bayesian networks
75. Learning Issues
76. Supervised Learning
77. Decision Trees
78. Knowledge Representation Formalisms
79. Semantic Networks
80. Inference in a Semantic Net
81. Extending Semantic Nets
82. Frames
83. Slots as Objects
84. Interpreting frames
85. Introduction to Planning
86. Problem Solving vs. Planning
87. Logic Based Planning
88. Planning Systems
89. Planning as Search
90. Situation-Space Planning Algorithms
91. Partial-Order Planning
92. Plan-Space Planning Algorithms
93. Interleaving vs. Non-Interleaving of Sub-Plan Steps
94. Simple Sock/Shoe Example
95. Probabilistic Reasoning
96. Review of Probability Theory
97. Semantics of Bayesian Networks
98. Introduction to Learning
99. Taxonomy of Learning Systems


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