Module Details

Module Code: COMP9064
Title: AI for Sustainability
Long Title: AI for Sustainability
NFQ Level: Expert
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 1 programme(s)
Module Description: Computing and artificial intelligence can play an important role in addressing critical sustainability challenges faced by present and future generations. The goal of this module is to introduce students to a range of sustainability challenges and to computational methods in Artificial Intelligence (AI) that deal with such sustainability challenges.

In this module the students will be able to identify real-life applications in which the use of the planet's resources can be minimized. In addition, students will learn how to solve such sustainability problems via computational models and techniques from the field of AI.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Identify a wide range of real-life sustainability problems from various application areas.
LO2 Identify the main features of sustainability problems, including their resources to minimize.
LO3 Model real-life sustainability problems as graphs and as optimization models.
LO4 Apply graph theory algorithms to solve the real-life sustainability problems modelled.
LO5 Apply dynamic programming techniques in order to solve a range of sustainability problems.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Introduction to Computational Sustainability
Introduction to real-life sustainability problems. Real-life applications where the minimization of resources impact the sustainability of our planet. Modelling computational sustainability problems as optimization problems. Explanation of the features of computational sustainability problems. Introducing the concept of best solution according to certain criterion/criteria such as minimising costs, energy, resources, etc.
Graphs Algorithms for Sustainability Problems
Graph main concepts (sub-graph, path, cycle, connection) and categories (directed, weighted). Modelling real-life sustainability problems as graphs. Graph algorithms: topological sorting, connectivity, minimum spanning tree (Prim's algorithm and Kruskal’s algorithm), shortest path (Dijkstra's algorithm), network flow. Travelling salesman problem.
Dynamic Programming Algorithms for Sustainability Problems
Decreasing recursive design (top-down approach) vs. increasing iterative design (bottom-up approach). Applications: Graphs, resource allocation problems, cutting problems, etc.
Applications to Real-life Sustainability Problems:
Analysis, modelling and solving of real-life sustainability problems. For instance, the forestry harvesting problem can be modelled as a cutting problem and solved with dynamic programming; the network design problem can be modelled as a graph and be solved with minimum spanning trees; finding the shortest path from a source to certain destination can be modelled as graph and can be solved with specific algorithms for graphs, etc.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 50
Timing Week 8 Learning Outcomes 1,2,3,4
Assessment Description
For a given case study of a sustainability problem, the student would be expected to
model it as a graph and solve it by implementing specific algorithms for graphs.
Assessment Type Project % of Total Mark 50
Timing Sem End Learning Outcomes 1,2,3,5
Assessment Description
For a given case study of a sustainability problem, the student would be expected to model it and solve it by implementing dynamic programming algorithms.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Presentation of theory. Every Week 2.00 2
Lab Contact Lab supporting lectures. Every Week 2.00 2
Independent Learning Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Presentation of theory. Every Week 2.00 2
Lab Contact Lab supporting lectures. Every Week 2.00 2
Independent Learning Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • William Kocay, Donald L. Kreher. (2016), Graphs, Algorithms, and Optimization, Second Edition. [ISBN: 9781482251166].
  • Art Lew, Holger Mauch. (2006), Dynamic Programming: A Computational Tool, Springer, [ISBN: 3540370137].
Supplementary Book Resources
  • Jörg Lässig, Kristian Kersting, Katharina Morik. (2016), Computational Sustainability, Springer, [ISBN: 978-3-319-318].
Recommended Article/Paper Resources
  • P Kilkki, U Väisänen. Determination of the optimum cutting policy for the forest stand by means of dynamic programming..
  • A Arbelaez, D Mehta, B O'Sullivan, L Quesada. A Constraint-Based Local Search for Edge Disjoint Rooted Distance-Constrained Minimum Spanning Tree Problem..
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KARIN_9 Master of Science in Artificial Intelligence 1 Elective