Well Scheduling in Oil and Gas Industry
(Graham Lange, Fuhua Lin, Xiaokun Zhang, Junye Wang, Eugene Kook)
In today's intensive competitive environment in the oil and gas industry, it is important to increase efficiency in production through resource management and well scheduling (resource scheduling, for short). However, resource scheduling has been a challenge for oil and gas companies for many years because it is influenced by a number of factors including environment, regulations, stakeholders, finances, the market, and approaches to determine and plan daily schedulable tasks. Currently, resource management and well scheduling are done manually by rule of thumb, with spreadsheets or some commercial tools. This project will develop an innovative Market-Based and Agent-Based approach that is needed to assist onsite decision makers to efficiently schedule daily operational tasks around well-site jobs according to the context and operational features of well planning and rig planning, as well as help them predict cost and/or time needed for these schedules.
(William Chen, AJ Armstrong, Oscar Lin)
COD is a MAS-based decision support system for course-offering determination. We studied using student preference elicitation, preference reasoning, voting-based preference aggregation, group decision making, agent negotiation techniques of MAS to solve planning, and scheduling issues in program planning and course-offering determination for educational programs.
Jason in Wonderland
Integrating a Multi-Agent System with a Virtual World
(Grant McClure, Oscar Lin)
We have successfully implemented a prototype focusing on using intelligent software agents (MAS) (Jason) to control non-player characters (NPCs) in Open Wonderland (http://openwonderland.org/), a 3D virtual world. The project was funded by NSERC discovery grant and CFI of Canada. The contribution of this work lies in the area of integrating these systems together and coming up with a solution that allows users to create more interesting NPCs and scenarios in Open Wonderland. We show that MAS is the right approach to realizing more believable immersive virtual worlds for e-learning, e-health, etc. and the work that We have done is a step towards realizing that. The demos can be viewed from YouTube (Part 1):
and Part 2 and Part 3.
Game-based Adaptive Testing --- QuizMASter
(Team: Daniel Hamacher, Steeve Laberge, Keway So, Oscar Lin)
QuizMASter is an educational game being developed by a group of researchers at Athabasca University. It helps students perform adaptive testing and collaborative learning through friendly competition. Conceptually, QuizMASter is designed to be similar to a TV game show, where a small group of contestants compete by answering questions presented by the game show host. The game follows simple rules, whereby a contestant gets a point for answering the question correctly. The contestant with the highest points wins. However, QuizMASter is different from a TV game show, in that, every participant gets his or her separate perspective view and experience of the game on three different levels:
The short video demonstrates version 1 of QuizMASter is here.
We are enhancing the intelligence capabilities and animations (such as lip-synchronization) of the game.