https://github.com/joaomlneto/ia_scheduling
Project assignment for the Artificial Intelligence course @ IST 2012
https://github.com/joaomlneto/ia_scheduling
Last synced: 3 months ago
JSON representation
Project assignment for the Artificial Intelligence course @ IST 2012
- Host: GitHub
- URL: https://github.com/joaomlneto/ia_scheduling
- Owner: joaomlneto
- Created: 2013-06-05T21:25:53.000Z (over 12 years ago)
- Default Branch: master
- Last Pushed: 2013-06-05T21:58:21.000Z (over 12 years ago)
- Last Synced: 2025-01-05T19:11:56.126Z (9 months ago)
- Size: 320 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
ia_scheduling
=============
Project assignment for the Artificial Intelligence course @ IST 2012
Simplified Scheduling problem in Common LISP.
Problem:
You must satisfy all tasks for a given list of tasks.
There are several alternatives to satisfy a task - at least one must be satisfied.
Each alternative is a set of subtasks.
Different subtasks cannot be done at the same time.
Subtasks (activities) have a duration of 1 hour and are defined by:
- Day, a positive integer
- Hour, an integer between 0 and 23 (inclusive)
- ID, an identifier.
Tasks with the same identifier are assumed to be the same (if you do one of them, you do not need to do it again).
Alternatives are a set of subtasks.
Tasks are a set of alternatives.
Formulations:
- Formulation A: the most efficient. Has a tree structure.
- Formulation B: Has a graph structure (repeats states).
Heuristics (for Formulation A):
- A1: dumb "return 0" heuristic. (admissible and consistent)
- A2: dumb greedy heuristic.
- A3: not so dumb greedy heuristic - choose task with alternative that imposes fewer subtasks (removing duplicates).
- A4: improvement over A3 - considers all the task's alternatives
- A5: improvement over A4 - considers all tasks (admissible and consistent)
- A6: improvement over A5 - also detects paths leading to dead-ends (saves some processing time)
Search algorithms (tree and graph versions):
- PPP: Depth First Search
- PPPL: Limited Depth First Search
- PPPI: Iterative Deepening First Search
- PLP: Breadth First Search
- PCU: Uniform Cost Search
- PA*: A* Search
- RBFS: Recursive Best-First Search
- CSP: Constraint Satisfaction Problem:
- Minimum Degree Heuristic
- Most Restricted Value Heuristic
- TODO: Minimum Remaining Values Heuristic
- TODO: MAC (Maintaining Arc Consistency) algorithm