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This project was aimed at exploring variations of greedy hill climbing and local search in-order to optimise a real world example.

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NikhilGupta1997/Artificial-Intelligence-Search

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Artificial-Intelligence-Search

Overview

The objective is to reach the goal state or reach as close as possible to the goal state. In our case we have been given a list of companies, each one bidding for regions to control for a certain price. Our goal is to allocate the regions based on the bids to different companies in order to maximise our profit.

Approach

The bids are modified by adding better bids to our chosen set of bids and removing the conflicting ones. This is done by Greedy hill climbing implementation of Local Search. Every time we try to climb the hill there is a small probability ~1% of taking a Random Restart where we completely pick a new set of chosen bids to start from. There is also a small probability of taking a Random Walk where any bid random bid is added to our chosen set instead of choosing comparitively better bid.

Steps to Run

Compile using compile.sh
Run using run.sh input.txt output.txt

  • input.txt contains the company bids
  • output.txt will contain the best found profit

Authors

Course Project under Prof. Mausam

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This project was aimed at exploring variations of greedy hill climbing and local search in-order to optimise a real world example.

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