Artificial Intelligence

The app is a complete free handbook of Artificial Intelligence with diagrams and graphs. It is part of Computer science or software engineering education which brings important topics, notes, news & blog on the subject. The App serves as a quick reference guide on this engineering subject.

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

Each topic is complete with diagrams, equations and other forms of graphical representations for better learning and quick understanding. The App will provide faster learning and quick revisions on the subject.

Few Additional subjects which have been included in the app are

Automata

Neural network fuzzy systems

Real-time Systems

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. A∗ Search

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

100. Mathematical formulation of the inductive learning problem

AI is going to be one of the most important technologies in the coming days. It is a must have study for engineering, computer science, software engineering and other cognitive science students. It also going to be extremely important for mechanical, Automotive & electrical engineering students and Professionals.

Download the app for the introduction to AI and related technology.

应用程序是人工智能的一个完整的自由与手册图表和图形。它带来的重要课题，笔记，新闻和博客的主题计算机科学或软件工程教育的一部分。该应用程序作为这个工程学科的快速参考指南。

它涵盖了600多个主题人工智能，自动控制，实时系统和神经的细节模糊。的主题被分成5个单位。

每个主题都配有图表，方程式等形式更好地学习和快速了解图形表示的。该应用程序将提供更快的学习和关于这个问题的快速修正。

已被列入应用一些其他科​​目

自动机

神经网络模糊系统

实时系统

一些在此应用中讨论的主题有：

1.图灵测试

2.介绍人工智能

3. AI的历史

4. AI周期

5.知识表示

6.典型的AI问题

7. AI的限制

8.介绍代理

9.座席绩效

10.智能代理

11.结构智能代理

12.代理程序的类型

13.基于目标代理

14.基于效用的药剂

15.代理和环境

16.代理体系结构

17.搜索解决方案

18.国家空间

19.搜索图表

20.通用搜索算法

21.不知情搜索策略

22.广度优先搜索

23.启发式搜索

24.¢AAˆ–搜索

25.搜索树

26.深度优先搜索

27.深度优先搜索的性质

28.双向搜索

29.搜索图

30.知情搜索策略

知情搜索方法31.

32.贪婪搜索

33.证明的一个可容许的*

34.启发式的性质

35.迭代加深A *

36.其他内存有限启发式搜索

37. N皇后eample

38.对抗性搜索

39.遗传算法

40.游戏

在游戏41.最佳决策

42.极大极小算法

43.α+β修剪

44.回溯

45.一致性驱动技术

46.路径一致性（K-一致性）

47.前瞻

48.命题逻辑

命题演算的语法49。

50.知识表示和推理

51.命题逻辑推理

52.命题定条款

53.知识级调试

推理规则54.

55.可靠性和完备性

56.一阶逻辑

57.统一58.语义学

59. Herbrand宇宙

60.健全性，完整性，一致性，可满足

61.决议

62. Herbrand再访

63.作为证明搜索

64.一些证明策略

65.非单调推理

66.真值维护系统

67.基于规则的系统

68.纯的Prolog

69.正向推理

70.反向链接

正向和反向链接的71选

72.和/或树木

73.隐马尔可夫模型

74.贝叶斯网络

75.学习问题

76.监督学习

77.决策树

78.知识表示形式主义

79.语义网络

80.推理在语义网

81.扩展语义网

82.框架

83.插槽对象

84.解释框架

85.介绍规划

86.解决问题与规划

基于87.逻辑规划

88.规划系统

89.作为规划搜索

90.情景的空间规划算法

91.偏序规划

92.规划，空间规划算法

子计划步骤93.交织与非交织

94.简单的袜子/鞋为例

95.概率推理

96.回顾概率论

贝叶斯网络语义97

98.介绍学习

学习系统的99分类

100.归纳学习问题的数学公式化

AI将是在未来的日子中最重要的技术之一。它是工程学，计算机科学，软件工程等认知科学的学生必须具备的研究。这也将是机械，汽车及电气工程学生和专业人士极为重要。

下载的应用程序引进到AI和相关技术。