FAQ on Artificial Intelligence

To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model. The correct answer to this question would be the “Hidden Markov Models (HMMs)” algorithm. Hidden Markov Models are commonly used for solving temporal probabilistic reasoning problems, particularly in scenarios where there is uncertainty about the state of a … Read more

Biagram model gives the probability of each word following each other word in speech recognition. The correct answer to this question would be the “n-gram language model.” An n-gram language model is a statistical model that predicts the probability of the next word in a sequence given the previous n-1 words. It is widely used … Read more

In speech recognition, Acoustic signal is used to identify a sequence of words. In speech recognition, the primary signal used is an audio signal. This audio signal is captured by a microphone and then processed by algorithms to extract features that are used for recognizing and understanding the spoken words or phrases. These features often … Read more

‘Inverse Resolution’ inverts a complete resolution, as it is a complete algorithm for learning first order theories. The algorithm that inverts a complete resolution strategy is called the “Davis-Putnam” algorithm. This algorithm is commonly used in artificial intelligence for solving logical satisfiability problems and is particularly useful in automated theorem proving and model checking. The … Read more

There are three literals available in top-down inductive learning methods they are a) Predicates b) Equality and Inequality c) Arithmetic Literals In top-down inductive learning methods, the number of literals available depends on the specific approach being used and the characteristics of the dataset being analyzed. However, typically, there are two main types of literals: … Read more