AI breadth-first method to solve complex problems

The number one quality in AI also called artificial intelligence is problem solving. This idea is one kind of case study so that you can do a small project on problem solving. This case study you can use it also for search and find the solution concept. Here you will get guidance on how to solve problem with AI.

Let’s set up a scenario to examine the various facets of how to solve this problem.

Consider a road trip between Boston and New York City. There are a variety of ways to make this trip. There are many paths between Boston and New York, because it is a heavily populated corridor with many towns and cities between the two locations.

Also read: Breadth-first search example

There will be some common-sense guidelines applied, including that any town or city on the path may be visited only once in a trip. It would not make much sense to repeatedly loop through a specific town or city during a trip. The key realistic points to consider in making¬†the trip’s path selections are the costs that are manifested: travel time, path length, fuel costs, tolls and traffic density, which are actual or anticipated delays.

As per below picture, finding the shortest route is ‘AI’.

Short route
Short route

These costs are often dependent because a longer path will increase fuel expenses, but not necessarily travel time because an alternate path could use a super highway, on which the car maintains a higher consistent speed, as compared to traveling through backroads and going through many small towns. But, a super highway can be congested, reducing overall speed, and there may even be tolls to add to the misery.

The above approach is called breadth-first approach.

The breadth-first approach starts by considering all possible paths between Boston and New York City, and computing and accumulating the total costs incurred while progressing through the various paths.


Author: Srini

Experienced software developer. Skills in Development, Coding, Testing and Debugging. Good Data analytic skills (Data Warehousing and BI). Also skills in Mainframe.