Grant: Discovery Grants Program - Individual
Carleton University
$48,000.00
Automated Reasoning and Adaptation for Assisted Experience Design
FY 2024-25
Summary
Many of our daily experiences are designed by one or more people who have particular goals in mind. These designed experiences could include visiting a museum or city park, following a fitness plan, playing a sport or a video game, and more. Sometimes designers aim to meet people's needs, like recovering from an injury, relaxing, or learning a new set of skills.
A valuable experience is one that does what it was designed to in a reasonable amount of time, and without any unwanted side-effects. For example, a valuable training experience might fully train a new employee during their first week, without making them want to quit. While many practicing designers work to make our experiences more valuable (that is, better or faster at doing what they were designed to do), their time is limited, and the number of ways that an experience could be made better is enormous.
The goal of this research is to study how practicing experience designers might work together with Artificial Intelligence (AI) systems to make more valuable experiences. One current example of an AI-based system is the writing software Grammarly, which supports a writer as they co-design a text, helping to create a more valuable reading experience. We want to understand how other kinds of experience design, besides writing, can be supported by AI systems. To gain this understanding, we will work with different kinds of practicing designers (like museum curators or game designers) to learn about their design processes and translate them into a machine-understandable form. We will also ask these designers about how their current processes might be improved, and for the most impactful (but still solveable) problems, we will build and test AI systems that help solve them. We will also consider the challenge of managing privacy, since people should generally have control over how and when their personal information is used to build or modify their experiences. We want our AI systems to support designers in making just decisions about how personal data will be used.
For Canadians, this work is important because many kinds of common experiences could be improved: imagine having more engaging fitness plans, more effective training programs, or more exciting games. For researchers of AI systems, this work represents a key step toward more general AI systems, since reasoning about experiences is an important part of functioning in the world. Finally, this work will show an important way to make progress in AI research: every step forward will be made toward a goal of benefiting society, with tangible improvements to how experiences are designed.
Program
Discovery Grants Program - Individual
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2020-06502
Name nom
Thue, David
Department département
School of Information Technology
Organization id
23
Institution établissement
Carleton University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2020
Award amount
48000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Automated Reasoning and Adaptation for Assisted Experience Design
Keywords
human/ai collaboration; experience management; user modelling and adaptation; game design; creativity support tools
Application summary
Many of our daily experiences are designed by one or more people who have particular goals in mind. These designed experiences could include visiting a museum or city park, following a fitness plan, playing a sport or a video game, and more. Sometimes designers aim to meet people's needs, like recovering from an injury, relaxing, or learning a new set of skills.
A valuable experience is one that does what it was designed to in a reasonable amount of time, and without any unwanted side-effects. For example, a valuable training experience might fully train a new employee during their first week, without making them want to quit. While many practicing designers work to make our experiences more valuable (that is, better or faster at doing what they were designed to do), their time is limited, and the number of ways that an experience could be made better is enormous.
The goal of this research is to study how practicing experience designers might work together with Artificial Intelligence (AI) systems to make more valuable experiences. One current example of an AI-based system is the writing software Grammarly, which supports a writer as they co-design a text, helping to create a more valuable reading experience. We want to understand how other kinds of experience design, besides writing, can be supported by AI systems. To gain this understanding, we will work with different kinds of practicing designers (like museum curators or game designers) to learn about their design processes and translate them into a machine-understandable form. We will also ask these designers about how their current processes might be improved, and for the most impactful (but still solveable) problems, we will build and test AI systems that help solve them. We will also consider the challenge of managing privacy, since people should generally have control over how and when their personal information is used to build or modify their experiences. We want our AI systems to support designers in making just decisions about how personal data will be used.
For Canadians, this work is important because many kinds of common experiences could be improved: imagine having more engaging fitness plans, more effective training programs, or more exciting games. For researchers of AI systems, this work represents a key step toward more general AI systems, since reasoning about experiences is an important part of functioning in the world. Finally, this work will show an important way to make progress in AI research: every step forward will be made toward a goal of benefiting society, with tangible improvements to how experiences are designed.