Uninformed Search Strategies 1. Breadth-First Search To understand this Breadth- First Search lets take a graph with nodes A, B, C, D, E,F and G . here I expand the shallowest unexpanded node. Here the fringe you see is the FIFO(First In First Out) queue .i.e., New successors go atRead More →
1. Create a search graph G consisting a start node S. 2. Create two lists open and close 3. Initially start node S is placed in open list. 4. Check whether the Sis goal node itself if it is the goal node then place S in the close list. If Read More →
We have many searching techniques that we have discussed as uninformed but to evaluate such search strategies we talk about the following four Completeness Completeness is a guarantee of finding a solution whenever one exists.This means that if a solution exists andif you are searching technique can find that solutionRead More →
1.Cost of solution is the sum of the arc costs on the solution path. • If all arcs have the same (unit) cost, then the solution cost is just the length of solution (number of steps/state transitions).then the solution cost is just the length of the solution or the numberRead More →
• A tree is graph connected without any cycles. • Trees comes with different types: tournament brackets, family trees, organizational charts and decision trees. In the above figure there is a path between a to b and b to a this forms a cycle. Here in tree no such cycles areRead More →
The notion of a path in a graph is clear and is shown below • Suppose G (V,E) is a graph with vertices v¬0, v1,v2,……,vk. and edges are e1,e2,……..,ek in which edge ei={vi-1,vi} for i= 1,…,k. • The alternating sequence that I get of vertices and edges , starting fromRead More →
Uninformed search does not have any domain knowledge. Here we are going to learn about some uniformed search strategies. Before getting into this take a review on problem solving as search, state spaces, graph searching and generic searching algorithms. We have previously learnt about the general definitions of problem solvingRead More →
The main goal of Decomposable Production System is to replace a problem goal by set of sub goals .If the sub goals are solved then the main goal is also solved Explaining problems in terms of decomposable production system allows us to be indefinite about whether we are decomposing problemRead More →
Nodes labeled by component databases have sets of successor nodes each labeled by one of the components. These nodes are called AND nodes because in order to process the compound database to termination all of the component database must be processed to termination. In the above figure 2, this nodeRead More →
A graph search control strategy might explore many equivalent paths in producing a database containing only M’s. Redundant Paths can lead to inefficiencies because the control strategy might attempt to explore all of them; worse it might do work that is wasted ultimately in exploring paths that do not terminate.Read More →