How to (re)represent it?

Lead Research Organisation: University of Cambridge
Department Name: Computer Laboratory


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 \textit{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.

Planned Impact

"How to (re)represent it?" is an ambitious project where we want to find out how humans choose and also change representation during problem solving. We want to develop engines that will give machines the same capability. Such machines will accrue many of the benefits that humans obtain from changing representation, including: more effective communication with a human by selecting a representation that is well-suited to their level of familiarity with the target topic; better understanding of a human by identifying and adopting the representation that the human favours; and greater flexibility in adapting to the needs of the user. As such, the project will have economic, societal and knowledge impact.

The impact of this work will be wide as AI systems are becoming ubiquitous. When interacting and exchanging information with humans, developers of such products need to represent information in human understandable ways. We will devise techniques that will help developers of AI systems to build products that choose representations appropriate for their users -- and thus have potential economic benefit to them.

Making machines more human accessible will also benefit the society at large, in particular, as it will potentially bring technical tools to those that are less technically versed.

We have already outlined how our work will contribute to the expansion of knowledge in Academic Beneficiaries. The techniques we will develop are novel and will provide a scientific advance in all areas (in academia and in industry) that must represent information for solving problems, reasoning, etc.

Whilst we exposed education as our initial concrete application target, our contributions will be general and could apply across different domains. They will lead to better understanding of representations and their relation to human expertise and preferences. As intelligent tools make part of our every day life, we will all benefit from having more human-like systems that adapt to us, rather than us adapting to them.

To achieve maximum impact, we will follow a comprehensive dissemination strategy. We will publish our results in all relevant communities (e.g., artificial intelligence, cognitive science, computer science, automated reasoning, diagrams, knowledge representation, human-computer interaction, information visualisation) to achieve wide dissemination. We will demonstrate our work by organising a workshop and preparing tutoring material aimed at academic as well as more specific industrial communities. This will also provide a community building opportunity for people interested in representations and more generally, human-like computing. We will continue to participate at outreach activities to raise the awareness of the importance of human-like computing. We will create a web repository to enable free and public access to our papers, corpus of problems and their solutions, software and tutorials. Finally, we have enlisted an advisory board of experts spanning all areas relevant to this project, and coming from academia and industry - their advice will help us stay focused on relevant problems, influence diverse communities that they lead, and transfer our technology widely.


10 25 50