Multi-agent Modelling for Policy Monitoring in Smart Buildings

Summary

A typical adult spends roughly 80% of a day indoors, whether it is at work or at home. Irrespective of the location, there are policies that are monitored to determine adherence to them. For example, a policy at home may be to reduce electricity consumption by 15%. Another different policy, this time at work, for an adult may be to take at least 2K steps while a company policy at an aggregate level may be to reduce energy consumption by 10%. Another policy may be that at least 75% of office workers need to engage with recycling. Overall, in one given location, there may be several and possibly conflicting policies imposed on the system.

Such policies are difficult to monitor due to environmental or device constraints. For example, a device may be running in low-power mode, making tracking step counts more challenging. Another challenge is the added privacy policy that needs to be respected. In this project, we assume that users (or agents) are self-interested (i.e., rational) agents and will need to be incentivised to help implement a policy. Some of the problems that will be addressed are (i) how are agents incentivised and/or punished to ensure that policies are satisfied and (ii) how are policies monitored in the system in the presence of constraints.
Applicants need to have strong programming skills in C/C++, Python or Java as well as mathematical skills for modelling.

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