Artificial and Computational Intelligence

Problem statement

Assume that you are in charge of an online e-commerce site which collects goods from vendors, stores them in godowns and later deliver them based on order placed. You have been assigned with robots that pick up packets from the front office and stores them in appropriate rooms. The packets are of different weights. The robot can carry 10 Kgs at a time. As in charge of the facility, you are required to give specific instructions to the robot to carry packets and store them in the rooms which are of different capacities.

The problem here is to find the correct combination of packets that can be carried by the robot so as to minimize the number of commutes by the robot to complete the job, the number of rooms used by the robot to store the contingency, and the remaining storage space.

The following figure gives the environment of the robot:

The below table gives the Packet weights and number of packets:

1. Explain the heuristic that can be used to solve the problem? Justify your choice.
2. Explain the cost function associated with your search in reaching the goal.
3. Choose the correct algorithm suitable for this grid search.
4. Implement the algorithm in PYTHON
5. Print the number of commutes by the robot, the weight carried by the robot in each commute, the number of rooms used to store the contingency and the remaining space for the store keepers reference.

The assignment will be evaluated on the following points

1. Explanation of the heuristic and algorithm chosen [20% weightage]
2. Representation of the environment, fringe and the data structures used [20% weightage]
3. Implementation of the algorithm in PYTHON [40% weightage]
4. The details given by the algorithm to the store keeper. [20% weightage]

Note:

1. Use suitable data structures to represent the environment, the solution space and the solution.
2. Python template is provided for your reference.

Hint:

Represent the number of commutes, the weight carried by the robot at each commute and the available room capacity as an objective vector. Use these features to define your evaluation function. Find the optimum combination to reach the goal of storing the contingency with minimum number of commutes and less number of rooms

Topics (Choose any one)

1. Uninformed search

2. Informed Search

3. Local search

Submission requires below two documents.

a) code file in format: .ipynb

b) word document: This should have justification towards the choices of algorithm/data structures/heuristic/fitness function wherever appropriate written in your own words. A screenshot of the output should be appended in the document and the code execution too.

PacketDeliveryAgent.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Artificial and Computational Intelligence Assignment " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Problem solving using Local search ", " ", "Things to follow ", " ", "Use appropriate data structures to represent the graph and the path using python libraries ", "Provide proper documentation ", "Find the path and cost associated with the path and print these " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Coding begins here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the robot environment here" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Definition of PEAS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define cost function here" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Definition of cost function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Implementation of Local search algorithm" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Code block 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Code block 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calling the search algorithm" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Calling the defined functions for search" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The agent should provide the following information" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "1. The number of commutes to reach the goal ", "2. The weight of packets carried during each commute ", "3. The remaining capacity of the godown and each room after completion of task" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }

5 kg 28 Kg 20 Kg Pathway Agent' s Direction Agent's 25 Kg 35 kg 46 Kg Pathway Front Office 15 Packet weight (Kg) No. of packets Total Weight (Kg) 1 15 2 5 10 4 7 28 5 2 10 6 3 18 7 2 14 9 4. 36 Total 38 packets 131 Kgs A1-Q3 Criteria Ratings Pts Explanation of the heuristic and algorithm chosen 3 pts O pts Full Marks No Marks 3 pts Representation of the environment, fringe and the data structures used O pts 3 pts Full Marks No Marks 3 pts Implementation of the algorithm in PYTHON O pts 5 pts Full Marks No Marks 5 pts Details given by the algorithm to the store keeper 2 pts Full Marks O pts No Marks 2 pts Total Points: 13 5 kg 28 Kg 20 Kg Pathway Agent' s Direction Agent's 25 Kg 35 kg 46 Kg Pathway Front Office 15 Packet weight (Kg) No. of packets Total Weight (Kg) 1 15 2 5 10 4 7 28 5 2 10 6 3 18 7 2 14 9 4. 36 Total 38 packets 131 Kgs A1-Q3 Criteria Ratings Pts Explanation of the heuristic and algorithm chosen 3 pts O pts Full Marks No Marks 3 pts Representation of the environment, fringe and the data structures used O pts 3 pts Full Marks No Marks 3 pts Implementation of the algorithm in PYTHON O pts 5 pts Full Marks No Marks 5 pts Details given by the algorithm to the store keeper 2 pts Full Marks O pts No Marks 2 pts Total Points: 13