Towards Representation of Collective Memory: A Holonic Multi-Agent System

Collective memory helps societies access and learn from the knowledge that they have gained through experience in the past, and make better informed decisions at present. When collective memory is explicit and accessible, it facilitates knowledge transfer within the society, helps reach decisions faster, and contributes to preparation for risks. However, access to collective memory can be a challenge because it is often not explicit. Moreover, collective memory changes and evolves constantly, in part due to the passing of time, and partly because of innovations, societal transitions, and environmental change. These complexities give rise to questions such as: How can we represent the collective memory and its evolution? How can we find and retrieve specific information in the collective memory? What can we learn about the changes of the collective memory? To shed light to theses questions, this project uses Artificial Intelligence approaches.

Faculty Supervisor:

Liliana Perez

Student:

Partner:

Ciblexpert

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program: