What is Fuzzy Logic Implementation?
Basically, it can be implemented in systems with various sizes and capabilities. That should be range from mall micro-controllers to large. Also, it can be implemented in hardware, software, or a combination of both in artificial intelligence.
How would you explain a uniform cost search algorithm?
In a uniform cost search algorithm, you start from the initial state and go to the neighbouring states to choose the ‘least costly’ state. From there, you’ll select the next least costly state from the unvisited neighbouring states and the visited states. You’d keep looking for the goal state in this manner, and even if you do, you’ll look for other potential states. If every iteration of a breadth-first search algorithm had the same cost, it would become a uniform cost search algorithm.
What is a heuristic function, and where is it used?
The heuristic function is used in Informed Search, and it finds the most promising path. It takes the current state of the agent as its input and produces the estimation of how close the agent is from the goal. The heuristic method, however, might not always give the best solution, but it guaranteed to find a good solution in a reasonable time. Heuristic function estimates how close a state is to the goal. It is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The value of the heuristic function is always positive.
Which algorithm is used for solving temporal probabilistic reasoning?
To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model.
Explain the term “Q-Learning.”
Q-learning is a popular algorithm used in reinforcement learning. It is based on the Bellman equation. In this algorithm, the agent tries to learn the policies that can provide the best actions to perform for maximining the rewards under particular circumstances. The agent learns these optimal policies from past experiences.
In Q-learning, the Q is used to represent the quality of the actions at each state, and the goal of the agent is to maximize the value of Q.
What is Travelling Salesman Problem?
The main goal of this problem is to find a low-cost tour. That starts from a city, visits all cities en-route exactly once and ends at the same starting city.
Start
Find out all (n -1)! Possible solutions, where n is the total number of cities.
Further, determine the minimum cost by finding out the cost of each of these (n -1)! solutions.
Finally, keep the one with the minimum cost.
What are Educational Requirements for Career in Artificial Intelligence?
- Various level of math, including probability, statistics, algebra, calculus, logic, and algorithms.
- Bayesian networking or graphical modeling, including neural nets.
- Physics, engineering, and robotics.
- Computer science, programming languages, and coding.
- Cognitive science theory.
List down the techniques or algorithms mostly used in AI?
In general, there are certain algorithms that are mostly used, or we can say that they are the first one to approach to understand the complex scenarios. Here are some of them.
- Neural Network
- Generic Algorithms
- Reinforcement Learning
Explain in brief Artificial Intelligence?
According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Also, intelligence distinguish us from everything in the world. As it has the ability to understand, apply knowledge. Also, improve skills that played a significant role in our evolution. We can define AI as the area of computer science. Further, they deal with the ways in which computers can be made. As they made to perform cognitive functions ascribed to humans.
What is reinforcement learning?
Reinforcement learning is a type of machine learning. In this, an agent interacts with its environment by producing actions, and learn with the help of feedback. The feedback is given to the agent in the form of rewards, such as for each good action, he gets a positive reward, and for each bad action, he gets a negative reward. There is no any labeled data or supervision is provided to the agent. In RL, the agent continuously does three things(performing actions, changing state, and getting the feedback) to explore the environment.
The popular reinforcement learning algorithms are:
- Q-Learning
- SARSA(State Action Reward State Action)
- Deep Q Neural Network