FAQ on Artificial Intelligence

The objective of an Inductive Logic Programming is to come up with a set of sentences for the hypothesis such that the entailment constraint is satisfied. In Inductive Logic Programming (ILP), the primary objective is to learn logical rules or hypotheses from examples provided in the form of positive and negative instances. Therefore, the key … Read more

Inductive logic programming combines inductive methods with the power of first order representations. The correct answer to this question would be “Inductive Logic Programming (ILP).” Inductive Logic Programming is a subfield of machine learning that combines inductive methods with the expressive power of first-order logic representations. It aims to learn logical representations or rules from … Read more

If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query. The correct answer to the question “How can Bayesian networks be used?” would depend on the context and the specific application domain. However, a comprehensive response might include the following points: … Read more

While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors. The relationship between a node and its predecessors in a Bayesian Network is crucial for understanding probabilistic dependencies within the network. In Bayesian Networks, each node represents a random variable, and the … Read more

For building a Bayes model in AI, three terms are required; they are one conditional probability and two unconditional probability. For building a Bayes model, typically two main terms are required: Prior Probability: This represents our initial belief about the probability of an event occurring before we have observed any evidence. It is denoted as … Read more