Automating Representation Choice for AI Tools
rep2rep2This three-year project follows on from the previous 18-month feasibility study. The previous study was called 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. How can we build machines that will adapt to us?
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 works 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, how we can model this ability on machines, and thus how to engineer systems that will select effective representation to enhance people's problem solving and learning.
This is a grand challenge, because it must marry human with machine capabilities. Our previous EPSRC funded feasibility project brought together an interdisciplinary team to combine expertise in computer science on automated reasoning with diagrammatic representations (Jamnik, Cambridge) with expertise in cognitive science on human problem solving and learning with representations (Cheng, Sussex). This interdisciplinary approach has been critical to the success of the feasibility project.
We previously showed 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 build on and generalise this hypothesis and demonstrate its utility by building a mathematics tutor that intelligently picks good representations according to the skill level of different learners. We have the following goals:
Develop representation selection theory based on the formalisation of formal and cognitive properties.
Develop a cognitive theory to assess the efficacy of alternative representations and methods for selecting representations suited to the competencies of individual users.
Devise computational algorithms (software) that mechanise the right choice of representation based on the theoretical foundations.
Develop and test the algorithms on a range of domains to demonstrate the scalability and generality of the approach.
Build an AI tutoring system that implements automated and personalised representation choice based on the user's level of expertise and experience.
Empirically evaluate the capability of the tutoring system to select beneficial representations for supporting problem solving.
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.