Probabilistic methods refer to approaches that utilize probability theory to model, reason, and make decisions under uncertainty. These methods provide a framework for quantifying and managing uncertainty by assigning probabilities to different events or outcomes. By incorporating probabilistic reasoning, these methods enable a more robust and quantitative analysis of uncertain situations. Here are some key …
Fuzzy logic is a mathematical framework used to handle and reason with uncertainty and imprecision in information. It provides a way to represent and manipulate vague or fuzzy concepts that do not have precise boundaries. Fuzzy logic extends classical binary logic by allowing intermediate degrees of truth or membership between true and false. In classical …
Uncertainty management in expert systems refers to the techniques and methods employed to handle and reason about uncertain or incomplete information within the knowledge base of an expert system. Expert systems are computer-based systems that aim to emulate human expertise in a specific domain by capturing knowledge and using it to make decisions or provide …
Hill climbing is a local search algorithm used to find an optimal solution in optimization problems. It starts with an initial solution and iteratively improves it by making incremental changes. The algorithm explores neighboring solutions and moves towards the solution that maximizes or minimizes the objective function, depending on whether it is a maximization or …
A* (pronounced “A-star”) is a popular heuristic search algorithm used for pathfinding and graph traversal problems. It is an extension of the uniform-cost search algorithm, combining the advantages of both uniform-cost search and greedy best-first search. The A* algorithm uses a heuristic function, denoted as h(n), which provides an estimate of the cost from a …
Heuristic search is a problem-solving technique used in artificial intelligence (AI) and computer science to find an optimal or near-optimal solution in a complex search space. It involves using heuristics, which are approximate methods or rules of thumb, to guide the search process towards potentially promising areas. In heuristic search, a search algorithm explores a …
Iterative Deepening is a graph search algorithm that combines the benefits of both breadth-first search (BFS) and depth-first search (DFS) algorithms. It is particularly useful when the search space is large and the depth of the solution is not known in advance. The main idea behind Iterative Deepening is to repeatedly perform DFS with an …
BFS stands for Breadth-First Search. It is a graph traversal algorithm used to explore or search through all the vertices of a graph in breadth-first order, i.e., exploring all vertices at the same depth level before moving to vertices at the next depth level. Here’s a step-by-step explanation of how BFS works: Start with a …
Uninformed search is a class of general-purpose search algorithms which operates in brute force-way. Uninformed search algorithms do not have additional information about state or search space other than how to traverse the tree, so it is also called blind search. Following are the various types of uninformed search algorithms: Breadth-first Search Depth-first Search Depth-limited …
Uninformed search, also known as blind search, is a search technique used in artificial intelligence and computer science to explore a problem space without having any prior knowledge about the structure or characteristics of the space. Uninformed search algorithms make decisions solely based on the available information and do not incorporate domain-specific knowledge or heuristics. …
