Alper T. Alan

Negotiation as an Interaction Mechanism for Deciding App Permissions

On the Android platform, apps make use of personal data as part of their business model, trading location, contacts, photos and more for app use. Few people are particularly aware of the permission settings or make changes to them. We hypothesize that both the difficulty in checking permission settings for all apps on a device, along with the lack of flexibility in deciding what happens to ones data, makes the perceived cost to protect ones privacy too high. In this paper, we present the preliminary results of a study that explores what happens when permission settings are more discretional at install time. We present the results of a pilot experiment, in which we ask users to negotiate which data they are happy to share, and we show that this results in higher user satisfaction than the typical take-it-or-leave-it setting. Our preliminary findings suggest negotiating consent is a powerful interaction mechanism that engages users and can enable them to strike a balance between privacy and pricing concerns.

Managing energy tariffs with agents: a field study of a future smart energy system at home

Interactive autonomous systems are likely to be more involved in future energy systems to assist human users. Given this, we prototyped a future scenario in which householders are assisted in switching electricity tariffs by an agent-based interactive system. The system uses real-time electricity monitoring to instantiate a scenario where participants may have to make, or delegate to their agent (in a variety ways), tariff switching decisions given uncertainty about their own consumption. We carried out a field trial with 12 households for 6 weeks in order to study the notion of autonomy. The results show nuanced ways in which monitoring system performance and taking control is balanced in everyday practice. Our field study provides promising directions for future use of smart systems that help householders manage their energy.

Meaningful Consent Study 2 Dataset

Data supporting: Baarslag, Tim et al. (2017) An Automated Negotiation Agent for Permission Management. In, AAMAS 2017: 16th International Conference on Autonomous Agents and Multiagent Systems.Additional Scenario file added 02/08/2017

Smart Thermostat Dataset

Smart energy systems are becoming increasingly ubiquitous, and consequently transforming our lives. Domestic energy use is one of the most talked-about domains where people may greatly benefit from these systems. Given this, it is important to understand interactions with smart energy systems within peoples everyday lives. To this end, we developed and deployed the first heating system that allows its users to control their home heating with real-time prices. In particular, we implemented three different designs of our heating system, and evaluated them with 30 UK households in a four-week in the wild study.This dataset contains anonymous quantitative data from the deployment of these three designs.

Towards a Smart Home Framework

We present our Smart Home Framework (SHF) which simplifies the modelling, prototyping and simulation of smart infrastructure (i.e., smart home and smart communities). It provides the buildings blocks (e.g., home appliances) that can be extended and assembled together to build a smart infrastructure model to which appropriate AI techniques can be applied. This approach enables rapid modelling where new research initiatives can build on existing work.