Artificial Intelligence Tutorial

Introduction to Artificial Intelligence Intelligent Agents

Search Algorithms

Problem-solving Uninformed Search Informed Search Heuristic Functions Local Search Algorithms and Optimization Problems Hill Climbing search Differences in Artificial Intelligence Adversarial Search in Artificial Intelligence Minimax Strategy Alpha-beta Pruning Constraint Satisfaction Problems in Artificial Intelligence Cryptarithmetic Problem in Artificial Intelligence

Knowledge, Reasoning and Planning

Knowledge based agents in AI Knowledge Representation in AI The Wumpus world Propositional Logic Inference Rules in Propositional Logic Theory of First Order Logic Inference in First Order Logic Resolution method in AI Forward Chaining Backward Chaining Classical Planning

Uncertain Knowledge and Reasoning

Quantifying Uncertainty Probabilistic Reasoning Hidden Markov Models Dynamic Bayesian Networks Utility Functions in Artificial Intelligence


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Differences in Artificial Intelligence

Difference between Intelligence and Artificial Intelligence

Intelligence Artificial Intelligence
It is a natural process or quality given to human beings. It is programmed using human intelligence.
It is an actual hereditary. It is not hereditary but a copy of human intelligence.
A human brain does not require any electricity to show his intelligence. Artificial intelligence requires electricity to get an output.
Execution speed of a human brain is less. Execution speed is higher than the human brain.
Human intelligence can handle different situations in a better way. It is designed to handle only a few types of problems.
A human brain is analog. An artificial brain is digital.

Difference between Strong AI and Weak AI

Strong AI Weak AI
A computer machine gets capable of thinking atleast equal to human beings. A computer machine gets a ‘thinking’ like feature to make it more powerful.
A machine can perform tasks on its own, just like human beings. A machine can perform tasks but need human intervention.
A computer program adds an algorithm itself to act in different situations. Here, tasks to be performed are added manually.
This type of AI allows machines to solve problems in an unlimited domain. This type of AI allows machines to solve problems in a limited domain.  
It is an independent AI which can take the decision on its own. It is dependent on humans and can simulate like human beings.
Currently, strong AI does not exist in the real world. Weak AI is in its advance phase of working.
There is no proper example of it. Example: Automatic car, APPLE Siri, etc.

Difference between Uninformed Search and Informed Search

Uninformed Search Informed Search
It is a search strategy with no additional information. It only contains the current state information. It is a search strategy which carries some additional information with the current state information.
It is less efficient to use uninformed search technique. It is more efficient search technique.
It may take more time to reach the goal node. It mostly reaches the goal state in limited time.
May or may not give an optimal solution. Mostly provides an optimal solution.
It is also known as Blind Search because it searches the goal blindly without having the prior knowledge. It is also known as Heuristic Search as it searches the goal with the help of some prior knowledge.
An uninformed search requires more computation. An informed search require less computation.
BFS, DFS, Uniform cost search are types of uninformed search. Best first search, A* search are types of informed search.

Difference between BFS and DFS

It extends for Breadth-first search. It extends for Depth-first search.
It searches a node breadthwise, i.e., covering each level one by one. It searches a node depthwise, i.e., covering one path deeply.
It uses the queue to store data in the memory. It uses the stack to store data in the memory.
BFS is a vertex-based algorithm. DFS is an edge-based algorithm.
The structure of a BFS tree is wide and short. The structure of a DFS tree is narrow and long.
The oldest unexpanded node is its first priority to explore it. The nodes along the edge are explored first.
BFS is used to examine bipartite graph, connected path as well as shortest path present in the graph. DFS is used to examine a two-edge connected graph, acyclic graph, and also the topological order.

Difference between A* search and AO* search

A* search AO* search
A* search is an OR graph algorithm. AO* search is an AND/OR graph algorithm.
It finds only one solution. It finds more than one solution by combining one or more branches.
It follows the best-first search strategy. It also follows the best-first search strategy.