MultiDisciplinary Optimization (MDO) problems represent one of the hardest and broadest domains of continuous optimization, often too complex to be tackled by classical optimization methods. We propose an original approach for taking into account this complexity using a self-adaptive multi-agent system where each elements of the problem become an agent in charge of a small part of the problem.

Betty’s Brain is a teachable agent system in the domain of river ecosystems that combines learning by teaching and self-regulation strategies to promote deep learning and understanding. Scaffolds in the form of hypertext resources, a Mentor agent, and a set of quiz questions help novice students learn and self-assess their own knowledge. The computational architecture is implemented as a multi-...

Modern educational technology is playing a very important role in information engineering discipline in ordinary universities and colleges. The using of modern educational technology can be classified into instinctive stage and rational stage. In the instinctive stage, users have been aware of the importance of those modern assisted teaching methods and make full application already. In rationa...

We propose a novel approach to self-regenerating systems which require continuous operation, such as security surveillance. For that aim we introduce HADES, a self-regenerating cooperative multi-agent system with local monitoring. When agents of HADES find local failures they repair them. However, in extreme cases repair may not be possible and irregular aggressive agents will multiply. These i...

Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that each agent reaches its goal and do not collide. In recent years, variants MAPF have risen in a wide range real-world applications as warehouse management autonomous vehicles. Optimizing common objectives, minimizing sum-of-costs or makespan, computationally intractable, but state-of-the-art algorithms a...

York University evaluated a prepcessing approach for this year’s enterprise document search task. With different parsing tools, we create two data sets. Based on each data set, we generate two official runs. Their results demonstrate that the removal of raw data in preprocessing stage has a negative impact on the retrieval performance.