How to (Re)represent it?


AI engines are ubiquitous in our lives: we talk to our mobile phones, ask directions from our sat-navs, and learn new facts and skills with our digital personal assistants. But most of the time, we need to learn how these systems work first: we have to adapt to them as they are not aware of our level of experience, expertise and preferences. AI engines are fast and can deal with a deluge of data much better than us, but they do so in machine-oriented ways, which are often inaccessible and unintelligible to humans.

The aim of this project is to identify and study how humans represent information that they want to work with and from which they will obtain new knowledge. Humans have the capability to choose the representation that is just right for them to enable them to solve a new problem, and moreover, if the representation needs to be changed, they can spot this and change it. Unlike humans, machines in general have fixed representations and do not have the understanding of the user. For example, sat-nav systems will only give directions with elementary spatial commands or route planning functions, whereas humans give directions in many forms, for instance in terms of landmarks or other geographic features that are based on shared knowledge.

We want to model in computational systems this inherently human ability to choose or change appropriate representations, and make machines do the same. We want to find out what are the cognitive processes that humans use to select representations, what criteria they use to choose them, and how we can model this ability on machines. Our hypothesis is that when humans choose a representation of a problem, they use cognitive and formal properties of the problem and its representation to make their choice. In this project, we will test this hypothesis by achieving the following goals:

  1. Collect a corpus of problems and candidate representations to study and categorise their cognitive and formal properties.
  2. Devise coding schemes and conduct cognitive studies to identify cognitive and formal properties that people use in choosing representations. Develop cognitive theories based on these experiments.
  3. Design and implement computational algorithms that allow users to choose alternative representations. Build a ranking and recommendation system based on the taxonomy from cognitive studies to suggest appropriate representation given a particular problem and user.
  4. Evaluate the utility of the system and generalise the approach to other domains outside of mathematics. Investigate how to apply our cognitive and computational models in education in the form of AI tutors that are adaptable to users.

Our work is novel in that it will address the problem of appropriate representation choice. Moreover, we will build novel cognitive theories and computational models that will allow AI systems to operate in more human-like ways and adapt to the requirements of the problem and the needs of the user. Thus, the potential impact will span numerous domains where systems interact with humans to represent information and use it for extracting new knowledge.