Evaluating Human-Agent Interaction in the Wild Alper Alan1 , Enrico Costanza1 , Sarvapali D. Ramchurn1 , Joel Fischer2 , Tom Rodden2 , and Nicholas R. Jennings1 1
Agents, Interaction and Complexity Group, University of Southampton, Southampton, UK [ata1g11, ec, sdr, nrj]@ecs.soton.ac.uk 2 Mixed Reality Lab, University of Nottingham, Nottingham, UK [jef, tar]@cs.nott.ac.uk
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.
1
Introduction
The increasing availability and miniaturisation of low-cost sensing, actuation and computational devices is likely to result in the wide adoption of sensor-based ”smart” applications and systems that leverage large amounts of data. Artificial Intelligence (AI) researchers need to face the challenge of designing interactive systems to help people take advantage of this data in a domestic context, often with the ambition to help us more efficiently utilise the limited resources of our planet. Recently, a small number of studies have investigated systems that automatically respond to sensed environmental changes such as ”smart” thermostats that take into account our whereabouts and learn our preferences [6, 20]. We are interested in the potential of domestic interactive technology to operate autonomously, and in users’ inclination, or resistance, to deliberately transfer control to such systems. On the one hand, autonomous operation may be desirable, or even essential, if we are to harness the capabilities offered by increasing amounts of data. On the other hand, autonomous operation may not be the best choice due to noise and biases in real world data, the limited size of training datasets, and the discrepancies between computationally feasible models and complex real-life systems, that result in the operation of these ”smart” autonomous systems being, at times, sub-optimal or, in the worst case, detrimental. For example, a recent work examining the real-world uptake of a smart thermostat highlighted how such errors are likely to cause users’ frustration and may lead them to abandon the technology [20]. It is therefore crucial that researchers and designers understand how to best design autonomous systems and interaction techniques that make the system status and operation clearly readable, and that allow its users to easily shift between autonomous and manual operation, a notion known in the Multi-Agent Systems community as ”adjustable autonomy” [14] or ”flexible autonomy” [8]. In particular, our focus is on how interactive autonomous computing systems may mediate user interaction with future energy infrastructure: the ”smart” grid. The choice of such a setting is driven by two main factors. First, energy as an application is important in itself for its societal and economic implications. Second, energy systems provide an opportunity to study rich interactions with prototypes of future autonomous systems. Hence, in this paper, we present a field evaluation of an agent-based home energy management application, which monitors household energy consumption, as well as available energy pricing schemes, and calculates the best one, and (optionally) automatically switches to it. The field trial, which lasted for six weeks and involved a diverse group of 12 participants, is reported through an analysis of automatic interaction logs and qualitative analysis of post-trial semi-structured interviews, aimed at uncovering long-term orientations towards smart systems in the wild, by taking AI beyond lab simulations.
1.1
Flexible Autonomy
Autonomous agents (software agents, robots or other systems) have long been the object of study of AI. Although definitions of agents can vary depending on the domain, they are commonly referred to as intelligent systems that perform a number of operations on behalf of human users [19]. Today, systems based on human-agent collectives, where humans and agents establish a range of collaborative relationships and share responsibility for accomplishing a specific goal, are continually growing [8]. In systems where humans rely on autonomous systems to complete complex tasks, the relationship between humans and agents is typically defined by the level of autonomy attributed to the agents [14]. Traditional approaches to defining this relationship assume that humans are somehow more knowledgeable about the task and therefore more apt at defining when the agent should autonomously act and when it should request manual operation. This is typically referred to as ”adjustable autonomy”. However, in many cases, the points at which guidance needs to be requested may not be well-defined. Moreover, humans may instead be guided by agents to complement tasks undertaken by the agents. Thus the locus of control may change between humans and agents at any point in time. Such interactions put humans and agents on the same level and are, hence, defined as realisations of flexible autonomy. The concept of ”flexible autonomy” indicates that the responsibility and authority of both humans and agents can be configured at any time depending on the current situation to optimise the overall performance. To study flexible autonomy and people’s interactions with it in their everyday lives, we use electricity tariff switching as an example application scenario, which is described in the following. 1.2
Electricity Tariff Switching
An energy tariff is a pricing scheme by which consumers are charged for the energy they use. Today, there are 34 energy providers that the consumer in the UK faces when she needs to pick an electricity tariff. The energy providers offer various tariff structures, where the most common ones are fixed tariffs and tiered tariffs. The fixed tariffs usually include two guaranteed rates: a daily standing charge that is the price for the energy service such as distribution and maintenance, and a unit rate that is the price per unit of energy. The tiered tariffs mostly have multiple tiers where each tier is charged with different rate. For example, ’Economy 7’ is one of the well-known tiered tariffs, which offers a cheaper rate for a seven-hour period during the night, where consumers are required to have a special meter installed to be able to use such tiered tariffs. Given that there are many tariff options available in the energy market, finding the best energy tariff may be a daunting task for most customers. Indeed, in the UK, most customers (70%) are on the wrong tariff and tend to stick to the same tariff without spending much time searching for a better option. 3 Agent-based solutions can help people to react to these different tariffs to get actual benefit out of them. 1.3
Previous Work and Overview of New Results
For agent-based systems to become practical and embraced in reality, many research challenges have to be addressed. In [1] an initial iteration of tariff-switching application was described and a set of design guidelines derived from a short field study were presented. This paper complements that work: we focus on long-term interactions of automated electricity tariff switching through removing an emphasis on renewable energy purposefully. In this paper, we make three new contributions. First, we propose a principled way of applying flexible autonomy for the development of agent-based applications. Second, we introduce a new application and UI design that allows users to determine their interaction and autonomy preferences flexibly. In particular, the new UI enables users to decide how frequent the agent will be interacting with them in addition to what level and way they will be interacting with the agent. Our third and main contribution is a long-term field study which was conducted to learn whether users’ preferences for autonomy and interaction vary over time in connection with their trust towards an autonomous agent. Our findings, from automatically recorded user interactions and semi-structured interviews, demonstrate that none of our participants disabled or even reduced the frequency of the updates they received from the agent once every day for the entire six weeks. Furthermore, our results show that all users preferred control over the agent and at least some of them gave away more control over time, which provides the first promising results that flexible autonomy might be effective in developing autonomous agent-based systems that are perceived more useful and trustworthy. 3
http://www.bbc.co.uk/news/uk-22745110.
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2
Background
In AI, a number of agent-based solutions have recently been introduced to assist humans managing residential energy use with real-time electricity prices [18, 11]. However, these approaches have only been tested in simulation, assuming that users have predictable energy consumption profiles and users will trust the agent to make the right decisions when energy prices are not completely predictable. Closer to our work, researchers proposed an agent-based recommender system without automatic tariff-switching [10], and only performed a usability test [5]. To date, there have been few studies in which agent-based systems are evaluated with actual end-users through real-world deployments. [14, 16, 9] presented a seminal work on adjustable autonomy where a number of personal assistant agents are deployed to alleviate the burden on humans in an office environment. [2] tested their intelligent alarm triage system with real network operators through a user study. [4] explored an agent-based system with a field study. In this study, software agents assisted users by charging their home batteries in lower priced hours. [3] deployed energy-aware washing machines that provide users suggestions on when best to do their laundry based on the amount of green energy generated locally, and tested various intervention techniques. Differently from these work, our work goes one step further, and investigates the dynamics around human-agent interactions with flexible autonomy in long term. In particular, we aim to shed light on the challenge of designing autonomous agent-based systems that are able to establish a long-term relationship with their humans users by sustaining their usefulness and credibility.
3
Managing Tariffs with TariffAgent
TariffAgent is developed as a combination of a web application and off-the-shelf sensors. It is inspired by the scenario depicted in a recent work [12], where autonomous agents embedded in households have the ability to switch the energy providers based on their offered rates and the user’s consumption routines. In our scenario, we consider a daily electricity tariff-switching problem so as to be able to create a realistic field study (as depicted in Section 4). Therefore, we only concerned with the daily energy usage of a home for selecting the best tariff. In what follows, we elaborate on how this daily consumption will be billed by different tariffs and hence define the challenge of choosing between these tariffs based on the predictions of it. 3.1
Daily Tariff Switching
Let the energy consumption of a home on day d be denoted as cd ∈ <+ kWh, where d ∈ {0, · · · , D}, and D ∈ Z+ . Then, let the set of tariffs provided by suppliers in the energy market be denoted as t1 , ..., ts ∈ T and for each tariff there exists a function F : T × < → < that takes the predicted energy consumption for the next day cd+1 of the home and returns the predicted cost for that tariff. For example, given a standard tariff from typical UK retailers where a customer is charged a fixed rate r1 and a standing charge s1 , function F would return cd+1 × r1 + s1 . To create a more challenging decision environment for users of the application (and therefore incentivize them to delegate their tariff decisions to an agent), we assume there exists eight suppliers, each with their own tariffs similar to real-world tariffs. In particular, each tariff represents the best value for a particular consumption range so that it is not easy to decide which tariff is the cheapest as it may change every day unless the user is able to accurately predict her own consumption. 3.2
Software Agent
In our scenario, planning which tariff to change to and when to change is a well-suited task for a software agent since it is required to continuously monitor the changing consumption to predict the best tariff. Energy consumption is monitored by the agent through off-the-shelf home energy monitoring devices (AlertMe4 ). These monitoring devices measure the total consumption of the household through a current clamp, and make the data available through an HTTP API. As was shown in a previous study, applying complex ma-chine learning techniques to predict day-ahead usage accurately is a challenging problem [17]. Here we also do not aim to test the user’s trust in the accuracy of predicted 4
http://www.alertme.co.uk.
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Table 1. Autonomy Levels. Autonomy Levels What human does What agent does Human-guided Changes tariff Provides suggestion Semi-autonomous Monitors changes Changes tariff Fully autonomous Does not monitor Changes tariff
consumption (since this is likely to be low when using state-of-the-art algorithms in any case) and instead focus on trust in the agent that may make mistakes at times. Thus, the software agent uses a very simple prediction algorithm to predict the day-ahead consumption on day d that simply uses the previous day’s consumption as a prediction for the next day’s consumption (i.e., cˆd+1 = cd−1 ). For all tariff switching suggestions and those autonomously enacted, the agent calculates the cheapest tariff for the day-ahead based on the next day’s predicted consumption and the cost function defined in Section 3.1. 3.3
Applying Flexible Autonomy
To enable users to flexibly specify their relationship with our agent, we provide a number of autonomy levels for various interaction modalities that go beyond the simple notion of moving between human-controlled and fully autonomous tariff switching. These autonomy levels allow users to dynamically arrange the authority and responsibility of their own and the agent. In addition, with these levels they can decide how the human-agent interactions will be held. Table 1 below shows the three different autonomy levels designed for this study. Human-guided: If the agent detects that the current tariff is different from the one predicted to be the best for the next day, it sends an SMS suggesting a tariff change. Users can accept the suggestion by replying ”Yes” via SMS. Semi-autonomous: The agent automatically switches to the predicted best tariff and informs users of the change via SMS. If the users are not happy with the change they can go to the website and manually change the tariff there. This level is semi-autonomous in that it automatically switches tariffs, but it allows users to easily regain control. Fully autonomous: The agent automatically switches to the predicted best tariff but does not inform the user of the change. This level is fully autonomous in that it completely offloads users of the burden of tariff switching. In the next section, we present the interaction modalities that allow users to communicate with the agent. 3.4
Human-Agent Interactions
Interactions between users and the agent are supplied through two mediums: they can interact with the agent either through SMS, or through a web site that represents the main ”face” of the system. The site includes two pages: Home and Details, which are described in what follows. The Home page, illustrated in Figure 1, comprises four components: tariff, setting, reports and budget. Through the Tariff component users can see the current tariff and manually select the tariff for the next day. The current and the next day’s tariffs are displayed on the top. The next day’s tariff is highlighted in green if it is the same as the one the agent suggested, otherwise in orange, to emphasise the difference. In the middle of the component, the predicted values for the user’s consumption on the next day are shown. Below the predictions, the eight tariffs are listed, from the one predicted to be the cheapest to the most expensive. The suggested tariff (the cheapest) is marked as such through a text label. Users can select a tariff through a button that is placed next to each tariff. The predicted amount of energy consumption for the next day can be modified through radio buttons. Changes in the consumption prediction are immediately reflected in the estimated costs for each tariff. The manually selected prediction can be confirmed, or ”told”, to the agent by clicking a button. By so doing, users can understand how the agent uses their predicted consumption to make a better choice on their behalf and therefore inspire confidence in the system. Setting is the second component of the Home page. It allows users to select one of the three autonomy levels described in the previous section. Because the delegation of autonomy is central to our study, we included this setting on the Home page, to make it easy for participants to adjust the level of autonomy during the trial. 4
Fig. 1. Home Page
The third component, Reports, enables users to decide how often to receive an SMS report, where the option every day is initially selected by default. The other options are: every 3 days, every 5 days and every week. The last component on the home page is the Budget, which displays how much was spent and how much remains of the budget allocated at the beginning of the study (see the next section for more details). It also provides a link to the other web page of the system, described in the following.
Fig. 2. Details View
The Details Page provides historical information about the operation of TariffAgent, with the aim of allowing users to evaluate their performance, and make the system transparent. In particular, for each past day the predicted and actual values for energy consumption are shown, together with the suggested and actual tariff selection, the budget, the cost and the savings or loss incurred. To facilitate the understanding of the information displayed, table cells are colourcoded. Tariffs are displayed in green/red depending on whether the selection was optimal or suboptimal. Consumption 5
predictions are shown in green when they turned out to be accurate (within 10%) and the resulting tariff suggestion is optimal. They are shown in red when they are inaccurate (outside 10%) compared to the realised values and the resulting tariff suggestion is suboptimal. They are shown in orange if the predictions are off (outside 10%), but the resulting tariff suggestion was optimal. Agent Interacting with Human User The agent can send three different types of notifications via SMS: reports, suggestions and confirmations. Reports provide information on how much energy was consumed, how much the cost was, which tariff was selected, and how much was saved or lost (compared to the optimal or the worst tariff). Reports were sent to all users regardless of their setting or tariff. An example report is: ”Hello, yesterday your tariff was TariffA, your consumption was 4.4 kWh and it cost you 0.69 pound. You saved 1.30 pound with Tariff-A.” The system sends suggestions to users who are on setting S1, when their tariff for the next day is predicted not to be optimal, for example: ”Hello, your tariff needs to be changed from Tariff-A to Tariff-B for tomorrow. If you confirm the change please reply as ’Yes’. ” Saving assumptions are only presented in the web UI for brevity, rather than in the SMS. Confirmation messages are sent only to users on setting S2 to inform them of an automatic tariff switch, such as: ”Hello, I switched your tariff from Tariff-A to Tariff-B for tomorrow.”
Fig. 3. SMS Dialogue with Semi-Autonomy
4
Evaluation in the Wild
Our focus is to study how people interact with an autonomous system, such as TariffAgent, which can influence people’s financial situation and possibly daily routines. In order to obtain meaningful results from such a study, it is crucial to offer a high degree of ecological validity for a long period of time (also to reduce novelty effects). There-fore a real world deployment, in the form of a field trial, was designed and carried out to evaluate how people experienced the system by using it as part of their everyday life. 4.1
Participants
We recruited 12 participants (6 female, 6 male) to cover a range of lifestyles, as detailed in Table 2 with pseudonyms, through word of mouth and snowball sampling. The only requirement to take part in the trial was to have a broad-band Internet connection and a mobile phone. 4.2
Method
The trial involved participants installing the meters in their own homes and using the system for a period of 6 weeks (42 days). The system used the real electricity consumption data collected from the participants’ homes to calculate their 6
Table 2. Participantsâ&#x20AC;&#x2122; Details. Participant Adam Arthur Chloe Dionisia Evelyn Gerard Gonca Hiroko Lucy Peter Stewart Tamer
Gender Male Male Female Female Female Male Female Female Female Male Male Male
Age 67 66 48 25 66 26 23 37 25 33 33 30
Occupation Priest Retired Community Mgr PhD Stu. (Chemistry) Retired Media Production Mgr MSc Stu. (Social Policy) Housewife Estate Mgr Unemployed Software Consultant PhD Stu. (Chemistry)
daily energy cost, based on the energy consumption and the selected tariff (as detailed in the previous section). It was particularly emphasized that the system does not affect their actual energy tariffs and bills. To motivate participants to engage with the system and experience the use of an autonomous system, we provided monetary incentives based on their performance according to the following setup. At the beginning of the study, all participants were allocated an online budget of 80, and their daily consumption cost was reduced from this budget over the period of the trial. At the end of the study, participants received payments (in the form of a gift voucher) according to the amount left on their budget. By so doing, we not only aimed to encourage participants to engage with the system, but also make saving have a real and tangible impact. This idea of using monetary incentives to mimic energy pricing was partly inspired from an earlier study around energy pricing [15], where participants were rewarded according to their study performance. 4.3
Data Analysis
Data was collected through automatic recording of interaction logs and individual semi-structured exit interviews. The interview focused on peopleâ&#x20AC;&#x2122;s use, adoption and under-standing of the system, and lasted between 20 and 30 minutes. To analyse the qualitative data collected through the interviews we adopted an approach inspired by grounded theory, a qualitative analysis method characterized by an absence of predefined assumption [13]. The interviews were documented through audio recording (later fully transcribed); analysis started by categorizing the material at the sentence level through open codes grouped in broader categories, such as perception, orientation and responsibility
5
Results
Most participants used the web interface through desktop computers, only 3 accessed the website from smart phones (34 hits from iPhone and 4 hits from Android). The Home page was loaded more frequently, with 420 page loads over the course of the study, while the Details page was loaded overall 184 times, still accounting for approximately 30% of the total page views (see Figure 4 for more details). The figures are based on the auto-recorded user interactions. Figure 4 shows the change in the total number of users opted for each type of autonomy level over the 42 days. Figure 5 shows the overall page loads of home and details page during the 6 weeks. Figure 6 shows the acceptance rate of the agentâ&#x20AC;&#x2122;s suggestions with percentages. The default autonomy level at the beginning of the trial, for all participants, was human-guided: the system would send SMS suggestions about tariff switching but it would not automatically switch. This default option was chosen because it is the one that requires users the most interaction, so we wanted to see whether they would change to a less demanding one by time. Four of the participants modified the autonomy level to semi-autonomous option, where the system automatically changes to the predicted best tariff and informs the user of the change via SMS. The remaining eight users kept using the default autonomy level. No one selected full autonomy (where the system changes the tariff 7
Fig. 4. Page Loads
Fig. 5. Autonomy Preferences
Fig. 6. Trust in Suggestions
without informing the user). We report in Figure 5 for how the number of users keeping different autonomy levels varied over the course of the study. In terms of tariff selection, the system’s predictions of the optimal tariff were correct on average 65% of the time (SD:12.3%); for reference users’ actual selections throughout the study (a combination of automatic and manual) corresponded to the optimal tariff 61% of the time (SD:15.4%). Overall 10 participants received SMS tariff switching suggestions from the agent (while on the human-guided). The eight participants accepted them by replying ”Yes” via SMS at least once, the other two participants never accepted any tariff switching suggestion but changed their tariff manually on the website at least once. In total 139 SMS suggestions were sent from the system to the users, with a 38% acceptance rate. Additionally, 59 times users changed their tariffs manually from the website and in 49 cases they accepted the agent’s suggestions. All users except one took advantage of the web UI to provide manual estimates of their electricity consumption prediction for the following day at least once. In total this explicit input was provided 110 times during the study, and resulted in 85 times correct tariff selections. The one person who did not use the input feature turned out to have a very regular consumption profile, within a range of 2.1 kWh throughout the entire duration of the study. The figures above demonstrate that the interactions of users were drastically dropped after the first week, yet maintained at considerable level by each user till the end of the study. Everyone either accessed the web interface or replied to SMS suggestions with some regularity that is on average at least once every 2.5 days (SD: 1.3 days). More interestingly, none of the users changed the frequency of reports from daily to a less frequent option, namely opted for the agent interacting with them every day. In the interviews, most participants appeared to hold an understanding that mirrors quite closely the actual design of the agent. For example, Gerard eloquently described, ”It looks at what usage you think you are gonna be using in the next day, which is some thing you either tell it or it just guesses itself, and then it looks at various tariffs based on the standing costs and the per unit costs, and works out which one is going to be the cheapest.”, or more concisely Hiroko told us, ”It decides the tariff and recommends me.” All participants commented that they perceived the system as helping them save money, and most of them stated that they trust the system’s tariff decisions. When asked about experienced mistakes in tariff suggestions or selections, they mostly considered it to be their own responsibility. For instance, Chloe said, ”It requires individual responsibility to make the system work, it sort of gives responsibility back to the energy user.” In an even more drastic way Gerard stated: ”I think it was always my fault. So I just felt annoyed with myself and I started trusting the machine more than I trusted myself, I was like oh the machine knows what it is doing, just leave it alone.”
6
Discussion and Conclusion
In this paper, we present a field trial that exposed 12 participants to a prototyped future energy scenario, where an autonomous system helps in energy cost management. Our results suggest that participants appear to understand and appreciate the autonomous nature of TariffAgent, which proactively sends users suggestions about switching energy tariffs. In particular, this is demonstrated by the general and prolonged engagement that most participants had in switching tariffs and in providing input to the system to improve its consumption forecast performance, and hence the 8
quality of the suggestions. Indeed all participants accepted tariff suggestions, and all except one provided consumption pre- dictions input at least once. Additionally, none of the participants reported to find the SMS tariff switching prompts bothersome, supporting the results of recent studies [1, 3]. Overall four participants delegated more responsibility to the system to automatically switch their tariff. Thus we believe that it is important to offer autonomy delegation as an option, as some users took advantage of it. In other words, the decision of balancing control and autonomy should be left to users where appropriate to cater for individual differences in what levels of system autonomy people are comfortable with. In so doing, users will may continue to make use of relevant parts of the system autonomy, rather than taking over full control. While existing trends in mixedinitiative systems put the onus of requesting human control or input on the system [7, 14], we suggest that systems involving humans and agents should enable users to enact control or to adjust autonomy by default. Moreover, we propose that such systems should also enable users to provide input to agents at any point in time to improve their operation. In turn, this makes legibility of system state an essential requirement for mixed-initiative systems. As future work, we aim to apply flexible autonomy to applications in other domains so as to explore interactions among multiple humans and agents.
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