This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Interaction between a user and a product like a home-appliance is sometimes a mutual relationship. A product not only provides service to a user, but also requires maintenance by the user to keep it working smoothly. If the user stops paying attention to maintenance, the product will no longer be used. To keep a sustainable relationship between a user and a product, the author proposes making a product an agent with anthropomorphic representation and interaction supported by the Anti-max Prisoner’s Dilemma game.

Keywords: Human-agent interaction, human interface, genetic programming.

Citation: Hirotaka Osawa. Sustainable Relationship with Product by Implementing Intentional Interaction.

Download: http://bit.ly/1Snueep

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called TariffAgent, to study notions of flexible autonomy in the context of tariff switching. TariffAgent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonomy.

Keywords: Human-Agent Interaction, Autonomous Agents, Flexible Autonomy, Energy, Smart Grid.

Citation: Alper Alan, Enrico Costanza, Joel Fisher, Sarvapali Ramchurn, Tom Rodden and Nicholas Jennings. A Field Study of Human-Agent Interaction for Electricity Tariff Switching.

Download: http://bit.ly/1MBoStF

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: With the improvement in agent technology and agent capabilities we foresee increasing use of agents in social contexts and, in particular, in human-agent team applications. To be effective in such team contexts, agents need to understand and adapt to the expectation of human team members. This paper presents our study on how behavioral strategies of agents affect the humans’ trust in those agents and the concomitant performance expectations that follow in virtual team environments. We have developed a virtual teamwork problem that involves repeated interaction between a human and several agent types over multiple episodes. The domain involves transcribing spoken words, and was chosen so that no specialized knowledge beyond language expertise is required of the human participants. The problem requires humans and agents to independently choose subset of tasks to complete without consulting with the partner and utility obtained is a function of the payment for task, if completed, minus its efforts. We implemented several agents types, which vary in how much of the teamwork they perform over different interactions in an episode. Experiments were conducted with subjects recruited from the MTurk. We collected both teamwork performance data as well as surveys to gauge participants’ trust in their agent partners. We trained a regression model on collected game data to identify distinct behavioral traits. By integrating the prediction model of player’s task choice, a learning agent is constructed and shown to significantly improve both social welfare, by reducing redundant work without sacrificing task completion rate, as well as agent and human utilities.

Keywords: Human-agent interaction, teamwork, trust, adaptation.

Citation: Feyza Hafizoglu and Sandip Sen. Evaluating Trust Levels in Human-agent Teamwork in Virtual Environments.

Download: http://bit.ly/1S8ceCA

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Interactive agent-based systems are becoming increasingly ubiquitous in our everyday lives, helping people in many domains including healthcare, transportation, and energy. As such, there is a need to investigate how humans and agents interact with each other to maximize the benefit that such systems can offer. In this paper, we present a field study that lasted for six weeks, in which 12 different households were required to interact on a daily basis with an agent-based system in order to manage their electricity pricing scheme. The main goal of this study was to explore long term interactions between human users and an agent to understand how people’s trust towards an agent may change over time, and consequently affect their autonomy and interaction preferences. Our results suggest that flexible autonomy shows promise for sustaining users’ trust and engagement with an agent, despite its occasional mistakes.

Keywords: Human-Agent Interaction, Autonomous Agents, Flexible Autonomy, Energy.

Citation: Alper Alan, Enrico Costanza, Sarvapali Ramchurn, Joel Fischer, Tom Rodden and Nicholas Jennings. Evaluating Human-Agent Interaction in the Wild.

Download: http://bit.ly/1S8bZri