Knowledge Representation & Reasoning

Knowledge Representation and Reasoning (KRR) is one of the fundamental requirements of Artificial Intelligence. Every intelligent system needs to represent its knowledge and understand the meaning of it. The system must also be able to apply this knowledge to new situations, acquire knowledge from its interactions with the world and infer new knowledge from its existing knowledge, when required. Representing knowledge is not about storing information in a database or spreadsheet—rather, it involves formalising the meaning of the knowledge. Imagine your computer has KRR capabilities and knows what the data in your spreadsheets mean. Then your computer would be able to automatically create new formulas for linking cells together whenever you have a new query.

At ANU, we specialise in representing and reasoning about spatial and temporal knowledge. We are interested in collaborations with other areas such as Computer Vision, Planning or Machine Learning to study different applications of spatial and temporal KRR, such as sensor networks, navigation or video games. One of our long-term research projects aims to develop methods for representing knowledge of physics, predicting consequences of physical actions (most of which are spatial and temporal) and selecting physical actions that have no undesired consequences. We are organising the Angry Birds AI Competition to develop and test these capabilities in a controlled and simplified environment.

Explore our available student research projects below and if you’d like to discuss opportunities for collaboration or funding, please email us.

Academic staff

Professor Jochen Renz »



Xiaoyu Ge

Mr Gary Ge »

PhD candidate

Mr Matthew Stephenson »

PhD Student

Peng Zhang

Mr Peng Zhang »

PhD student


Mr Mikael Boors »

Occupational Trainee

Updated:  8 September 2015/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing