|499||Coffee/Office Analytics||2-3 (3)||❌|
|499||LOCM Planning Model Learning||2-3 (1)||🔵|
|499||Planning.Domains API||2-3 (2)||🔵|
|499||Nurse and Physician Scheduling||2-3||✔️|
|499||Computational Art Installation||3 (3)||❌|
|500||Planning Graph Factorization||1||❌|
|500||Dialogue Agent Believability||1||❌|
Note: The 🔵 symbol means some students have already signed up (# indicated in brackets), but spots remain.
We won’t be able to fill every project position. This is a rough measure of how much capacity Prof. Muise and the MuLab has remaining for capstone projects:
To apply for any of the projects, please email Prof. Muise with the following details:
Your name and a bit of background about yourself.
The project name and expression of your interest in the area.
Your Queen’s transcript.
(if available) A CV/Resume
(if available) A link to any software/projects you’ve worked on (e.g., GitHub profile).
Over the coming months, we will reach out to interested students and possibly interview if there is high demand for the project. If necessary, the interview process will involve a small coding exercise as well as meeting with Prof. Muise and/or Mu Lab members.
A short summary of each project is below.
As a collaboration between the Mu Lab and the Machine Intelligence & Biocomputing (MIB) Lab, we have embarked on a research exploration to optimize several aspects of the office experience. This project explores the challenges and opportunities presented by the IoT elements in the MuLab. These include:
(1) Improving the analytics acquisition (website to acquire coffee/tea consumption data).
(2) Running machine learning algorithms to predict the coffee/tea selection given energy profiles (from smart plugs).
(3) Predicting the consumption levels and timing based on timing factors.
More sub-project ideas may be added over time.
LOCM Planning Model Learning
api.planning.domains aims to be the most complete compendium of benchmark problems for the area of AI known as Automated Planning. It currently houses thousands of the existing problems that have been created over the last several decades, and this capstone project aims to dramatically expand its coverage to (1) several other forms of planning; (2) several of the newer benchmarks that have been released since the service was created; and (3) a range of custom features that could be used for machine-learning settings in the field.
The skills that will be used as part of the project include (a) general understanding of automated planning (e.g., what would be acquired from CISC 352); (b) web development (e.g., node.js, flask, etc); and (c) large-scale compute experiments (e.g., using ansible). Please note that none of these are requirements. Rather, these are skills useful to have and will otherwise be a part of the learning process for this project.
The team will be working with Prof Muise and several other researchers internationally to expand on the api.planning.domains service, and the work will impact hundreds of researchers globally that use the service on a regular basis.
Nurse and Physician Scheduling
An optimized schedule can make all the difference between a hospital wing that runs smoothly or a hospital wing that is unbalanced. This project aims to explore the problem of scheduling nurses and physicians on one or more hospital floors, and do so using state-of-the-art constraint-based AI optimization techniques. The team will work with a company that specializes in nurse/physician scheduling to generate realistic synthetic data and model complex real-world constraints. The primary software to be explored is MiniZinc, but previous knowledge of the language is not required. Ideal candidates will be those with CISC 352 experience (performing well in the CSP assignment) and those that have done a course project in CISC 204.
Computational Art Installation
This project will involve working with some number of members from the MuLab to visualize, artistically, some of the ongoing research. The final artefact will be an installation in Goodwin 627 (the MuLab), and this open-ended project is aimed at students who are in COCA and interested in the field of computational art.
Planning Graph Factorization
In viewing state transition systems as graphs, we are able to ask questions concerning their representation. Does the formulation hide structure that can be exploited to solve the problem? Can we extract any properties or missing information from the formulation? Given some representation of a planning problem, can we determine an equivalent representation that is more amenable to existing techniques? Such questions suggest a novel area of research focused on the representation and reformulation of planning problems and these are the core problems being to be addressed in this project.
Dialogue Agent Believability
This project is aimed at understanding conversations between humans and structured dialogue agents. Specifically, it involves understanding how likely different dialogue paths are and identifying unlikely outcomes in dialogue agent interactions. This understanding could allow us to identify edge cases in a dialogue tree and whether they are covered or not by the agent. This would allow improving existing dialogue agents, which would improve the bandwidth of human computer interaction.