Consider a decentralised machine learning problem in which two rival online marketing companies aim to share their data in order to train a recommendation algorithm to maximise its accuracy level, as none of them has sufficiently large amount of data to do so. The more data they decide to share, the better improvement in the performance of the algorithm they can get. However, as they are also rivals, sharing too many data with each other will also induce a cost (e.g., losing sensitive data and market to the rival). On the other hand, without cooperation, they would not be able to achieve high recommendation efficiency, and thus, would not be able to increase their profit. As such, each company has to find the best trade-off between hiding data and training the machine learning algorithm together.
Another example comes from the domain of smart grids. Consider a set of households who together would like to form a coalition and trade for good energy deals in the market. In order to maximise their own utility, as well as the social welfare, these households are encouraged to share as much information about their personal preferences with each other as possible. However, this raises a number of privacy issues. Given this, the main objective here is to identify to what extent the homeowners should lower their privacy demands in order to achieve good social welfare (i.e., good collective deals).
The abovementioned examples belong to a new and emerging class of optimisation problems called Coopetition (for cooperation in competition), rooted in Game Theory and Bi-Level Optimisation. With the rapid improvements in Data Science, Decentralised Machine Learning (predicted to be the future of Machine Learning), and large-scale ubiquitous intelligent systems (e.g., Internet of Things, smart grids, and smart cities), Coopetition is becoming emergent. However, as it is a very new research field, it still lacks rigorous mathematical models that can capture the complex interconnection between cooperation and competition. As such, during my PhD studies, I would like to investigate the problem of Coopetition and its application to artificial intelligence in more detail. In particular, I aim to achieve the following goals:
1. Theoretical foundations: Investigate the mathematical foundations of Coopetition, and propose a thorough theoretical analysis of different models (year 1).
2. Algorithmic Contribution: Develop scalable and efficient algorithms to calculate different solution concepts of Coopetition (year 2).
3. Application: Apply my theoretical and algorithmic findings to concrete domains of Artificial Intelligence and Machine Learning (year 3).