Skip to content

Optimizing Campus Route — An AI agent-based solution to minimize walking distance and time for completing tasks across university campus locations using search algorithms like A* and Random Restart Hill Climbing.

Notifications You must be signed in to change notification settings

Krunalscorp/Finding_Optimized_Graphpath

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

13 Commits
 
 
 
 

Repository files navigation

Optimizing the Campus Route

1. PEAS and Task Environment of the Agent

PEAS (Performance measure, Environment, Actuators, Sensors)

  • Performance measure:
    Minimize the total walking distance and time taken by Rohan to complete all the tasks. This can be measured by calculating the total distance travelled or the total time taken.

  • Environment:
    The university campus, including locations such as Admission Office, Hostel Office, Hostel, Campus Canteen, Department, Library, and campus exit. It also includes the walking distances between each pair of locations.

  • Actuators:
    Rohan, the student, who can move between locations on the campus.

  • Sensors:
    Rohan's ability to perceive his surroundings, including the locations, walking distances, and his current energy level. Rohan can also use a campus map to navigate.


Task Environment

  • Fully observable:
    The environment is fully observable, as Rohan has access to the campus map and can see the locations.

  • Deterministic:
    The environment is deterministic, as the walking distances between locations are fixed and known.

  • Sequential:
    The task is sequential as Rohan must complete all tasks, but not necessarily in a fixed order (e.g., registration before hostel office procedures). If the order was fixed, it would change the problem significantly.

  • Static:
    The environment is static, as the locations and walking distances do not change during the task.

  • Discrete:
    The task is discrete, as Rohan can only move between specific predefined locations.


2. Heuristic and Fitness Functions for the Algorithms

A* Algorithm

  • Heuristic function (h):
    Estimates the walking distance from the current location to the campus exit. This can be calculated using Euclidean or Manhattan distance.
    Example: If current location is Hostel Office, heuristic might estimate 500 meters to campus exit.

  • Cost function (g):
    Calculates the actual walking distance from the Admission Office to the current location.
    Example: If current location is Hostel Office, cost function might calculate 200 meters from Admission Office to Hostel Office.

  • Fitness function (f):
    Combines cost and heuristic to guide the search:
    [ f = g + h ]
    Example: For Hostel Office, total estimated cost might be (200 + 500 = 700) meters.


Random Restart Hill Climbing Algorithm

  • Fitness function:
    Calculates the total walking distance for the entire route. It is the sum of distances between consecutive locations.
    Example: For the route Admission Office -> Hostel Office -> Hostel -> Campus Canteen -> Department -> Library -> Campus Exit, total distance might be 1500 meters.

  • Heuristic function:
    Not explicitly used in Hill Climbing, but can be used to generate or guide the initial solution.
    Example: The heuristic might help generate a starting solution close to optimal.


Note:
The heuristic function for A* should be admissible (never overestimate the true cost) and consistent (estimated cost is always less than or equal to the actual cost).

About

Optimizing Campus Route — An AI agent-based solution to minimize walking distance and time for completing tasks across university campus locations using search algorithms like A* and Random Restart Hill Climbing.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published