AI enthusiast @Techtrove9172

1. Simple Hill Climbing: Simple hill climbing is the simplest way to implement a hill climbing algorithm. It only evaluates the neighbor node state at a time and selects the first one which optimizes current cost and set it as a current state.

Hill Climbing Algorithm in AI : A hill climbing algorithm is local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. Generate and Test variant Greedy approach No backtracking

2.) A* Search Algorithm: A* search is the most commonly known form of best-first search. It uses heuristic function h(n), and costs to reach the node n from the start state g(n).A* algorithm is similar to UCS except that it uses g(n)+h(n) instead of g(n).

1.Best-first Search Algorithm (Greedy Search): A greedy best-first search algorithm always selects the path which appears best at that moment. It is the combination of depth-first search and breadth-first search algorithms. It uses the heuristic function and search.

Informed Search Algorithms: The informed search algorithm is more useful for large search spaces. It uses the idea of heuristic, so it is also called Heuristic search. It takes the current state of the agent as input and produces the estimation how close agent is from the goal

6. Bidirectional Search Algorithm: Bidirectional search algorithm runs two simultaneous searches, one form initial state called as forward-search and other from goal node called as backward-search, to find the goal node. Bidirectional search requires less memory

5. Iterative deepening depth-first Search: This algorithm performs depth-first search up to a certain "depth limit", and it keeps increasing the depth limit after each iteration until the goal node is found. In IDDFS is that it repeats all the work of the previous phase.

4. Uniform-cost Search Algorithm: Uniform-cost search is a searching algorithm used for traversing a weighted tree or graph. This algorithm comes into play when a different cost is available for each edge. It is optimal because at every state the path with least cost is chosen.

3. Depth-Limited Search Algorithm: A depth-limited search algorithm is similar to depth-first search with a predetermined limit. It can solve the drawback of the infinite path in the Depth-first search. It may not be optimal if the problem has more than one solution.

2. Depth-first Search: Depth-first search is a recursive algorithm for traversing a tree or graph data structure.The process of DFS algorithm is similar to the BFS algorithm. It takes less time to reach to the goal node than BFS algorithm (if it traverses in the right path).

1. Breadth-first Search: Breadth-first search is the most common search strategy for traversing a tree or graph. This algorithm searches breadthwise in a tree or graph, so it is called breadth-first search.Breadth-first search implemented using FIFO queue data structure.

Informed Search: Informed search algorithms use domain knowledge. It can solve many complex problems which could not be solved in another way. An example of informed search algorithms is a traveling salesman problem. 1. Greedy Search 2. A* Search

Uninformed/Blind Search: The uninformed search does not contain domain knowledge such as closeness,location of the goal. It can be divided into five types. Breadth-first search Uniform cost search Depth-first search Iterative deepening depth-first search Bidirectional Search

Search Algorithm Terminologies: Searching is a step by step procedure to solve a search-problem in a given search space. A search problem can have three main factors: 1. Search Space 2. Start State 3. Goal test

Problem-solving agents: In AI, Search techniques are universal problem-solving methods. Rational agents or Problem-solving agents in AI mostly used these search strategies or algorithms to solve a specific problem and provide the best result.

Search Algorithms in Artificial Intelligence: Search algorithms are one of the most important areas of Artificial Intelligence. This topic will explain all about the search algorithms in AI.

PEAS for self-driving cars: Performance: Safety, time, legal drive, comfort Environment: Roads, other vehicles, road signs, pedestrian Actuators: Steering, accelerator, brake, signal, horn Sensors: Camera, GPS, speedometer, odometer, accelerometer, sonar.

PEAS Representation: PEAS is a type of model on which an AI agent works upon. When we define an AI agent or rational agent, then we can group its properties under PEAS representation model. P: Performance measure E: Environment A: Actuators S: Sensors

What is an Agent? Human-Agent: A human agent has eyes, ears, and other organs which work for sensors and hands, legs, and vocal tract work for actuators. Robotic Agent: A robotic agent can have cameras, infrared range finder, NLP for sensors and various motors for actuators.

Features required for a machine to pass the Turing test: 1. Natural language processing 2. Knowledge representation 3. Automated reasoning 4. Machine learning 5. Vision (For total Turing test) 6. Motor Control (For total Turing test)