## Uninformed Search Strategies: Breadth-First Search

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 →

## Procedure For Graph Search, reordering the open list

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 →

## Evaluating Search Strategies

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 →

## Cost Of Solution

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 →

## Tree , Graph searching for problem solving as state space, Formalizing search in a graph,

•   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 →

## Notion of path in a graph

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

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 →

## Goal of decomposable production system

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 →

## AND-OR Graph

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 →

## Graph Search Strategy:

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 →