Knowledge Engineering Group
Knowledge Engineering Group
- I lead the Knowledge Engineering Group (KEG).
- We conduct research within the field of Artificial Intelligence (AI).
- AI is concerned with programs that provide solutions (or approximations to solutions) to problems which are 'difficult' to solve by traditional methods. The difficulty stems from the presence in the problem of disorder, uncertainty, lack of precision or inherent intractability.
- In particular, the Group is interested in knowledge-based systems for intelligent retrieval and decision-support.
- We are best known for our work in Case-Based Reasoning (CBR) and Recommender Systems.
- But we have also made contributions to areas such as natural language processing, machine learning and ant algorithms.
Research questions in Case-Based Reasoning and Recommender Systems
- Case-Based Reasoning (CBR) is an approach to problem solving which can be successful in domains where "similar problems have similar solutions". In such domains, new problems can be solved by transferring and adapting solutions that were used to solve similar problems in the past.
- Recommender systems help us to decide which goods, services or information to consume based on what they infer about our preferences.
- Our research addresses questions such as:
- In a CBR system how do we retrieve the best cases, and in a recommender system how do we select what to recommend?
- How can these systems learn from experience, and how well do they learn?
- How can these system maintain their knowledge, especially if it is changing over time?
- How can these systems explain their actions and conclusions and advise their users?
- How can these systems reason with unstructured information (e.g. text), as well as structured information?
- How can we integrate multiple reasoning engines (e.g. case-based with rule-based, case-bases with databases, case-based with constraint processing, case-based with genetic algorithms & genetic programming, ant algorithms with constraint processing, collaborative filtering with content-based filtering, etc.)?
Example application areas
- We have applied our research to problems in several domains.
- In spam filtering we face challenges such as handling unstructured information, and using learning to cope with concept drift (i.e. the fact that spam is a moving target).
- To recommend configurable products such as travel itineraries and high-end consumer electronics requires the ability to reason not only with the user's preferences but also with constraints and policies that determine how well different sub-components 'go together'.
- In a waste exchange system we seek to pair those who produce waste with those who can reuse it, with the challenge that neither party can be persuaded to describe the waste they have or want with sufficient precision.
- In predicting the product yield of the trees in a forest we have to estimate tree stem diameter along the length of each tree from laser scanner data that is noisy and gappy.
Case Based Reasoning; CBR; Knowledge Engineering Group; KEG; Artificial Intelligence; AI;