Support agents that help users in their daily lives need to take into account not only the user’s characteristics, but also the social situation of the user. Existing work on including social context uses some type of situation cue as an input to information processing techniques in order to assess the expected behavior of the user. However, research shows that it is important to also determine the meaning of a situation, a step which we refer to as social situation comprehension. We propose using psychological characteristics of situations, which have been proposed in social science for ascribing meaning to situations, as the basis for social situation comprehension. Using data from user studies, we evaluate this proposal from two perspectives. First, from a technical perspective, we show that psychological characteristics of situations can be used as input to predict the priority of social situations, and that psychological characteristics of situations can be predicted from the features of a social situation. Second, we investigate the role of the comprehension step in human–machine meaning making. We show that psychological characteristics can be successfully used as a basis for explanations given to users about the decisions of an agenda management personal assistant agent.

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Artificial agents that support people in their daily activities (e.g., virtual coaches and personal assistants) are increasingly prevalent. Since many daily activities are social in nature, support agents should understand a user's social situation to offer comprehensive support. However, there are no systematic approaches for developing support agents that are social situation aware. We identify key requirements for a support agent to be social situation aware and propose steps to realize those requirements. These steps are presented through a conceptual architecture centered on two key ideas: 1) conceptualizing social situation awareness as an instantiation of "general" situation awareness, and 2) using situation taxonomies for such instantiation. This enables support agents to represent a user's social situation, comprehend its meaning, and assess its impact on the user's behavior. We discuss empirical results supporting the effectiveness of the proposed approach and illustrate how the architecture can be used in support agents through two use cases.

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The use of support agents that help people in their daily lives is steadily growing. While there have been continuous developments in integrating and modelling internal aspects of the user in these support agents, research shows that people's behavior is also shaped by their environment. While there have been attempts at integrating elements of the physical environment such as location, support agents generally lack the ability to take into account the effect of the user's social situation on their behavior. This is important since the majority of our daily life situations have a social nature. This thesis proposes a social situation awareness framework for allowing support agents to take into account the user's social situation in order to offer more comprehensive support. The framework is inspired by existing work on situation awareness from research in human factors and computer science, instantiated with concepts from social sciences. This thesis demonstrates how to integrate social situation awareness components in support agents. The studies presented in the thesis provide insight into the concepts and techniques that are needed for social situation awareness, and how they can be used in practice through a hypothetical case study involving a socially aware agenda management agent. This contribution serves as a blueprint for designers of support agents, and provides a basis towards more comprehensive support for users.@en

Personal values represent what people find important in their lives, and are key drivers of human behavior. For this reason, support agents should provide help that is aligned with the personal values of the users. To do this, the support agent not only should know the value preferences of the user, but also how different situations in the user's life affect these personal values. We represent situations using their psychological characteristics, and we build predictive models that given the psychological characteristics of a situation, predict whether the situation promotes, demotes or does not affect a personal value. In this work, we focus on predictions for the value ‘enjoyment of life', and use different machine learning classifiers, all of them performing better than chance when training on data from multiple people. The best predictive model is a multi-layer perceptron classifier, which achieves an accuracy of 72%. Further, we hypothesize that the accuracy of such models would drop when tested on individual data sets. The data supports our hypothesis, and the accuracy of the best performing model drops by at least 11% when tested on individual data. To tackle this, we propose an active learning procedure to build personalized prediction models having the user in the loop. Results show that this approach outperforms the previously built model while using only 30% of the training data. Our findings suggest that how situations affect personal values can have subjective interpretations, but we can account for those subjective interpretations by involving the user when building a prediction model.

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Personal assistant agents have been developed to help people in their daily lives with tasks such as agenda management. In order to provide better support, they should not only model the user’s internal aspects, but also their social situation. Current research on social context tackles this by modelling the social aspects of a situation from an objective perspective. In our approach, we model these social aspects of the situation from the user’s subjective perspective. We do so by using concepts from social science, and in turn apply machine learning techniques to predict the priority that the user would assign to these situations. Furthermore, we show that using these techniques allows agents to determine which features influenced these predictions. Results based on a crowd-sourcing user study suggest that our proposed model would enable personal assistant agents to differentiate between situations with high and low priority. We believe this to be a first step towards agents that better understand the user’s social situation, and adapt their support accordingly.

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Support agents are investigated more and more as a way of assisting people in carrying out daily tasks. Support agents should be flexible in adapting their support to what their user needs. Research suggests that the situation someone is in affects their behaviour, however its effect has not been incorporated in the decision making of support agents. Modelling the characteristics of situations explicitly and studying their effect on internal perceptions of the user, such as their personal values, would enable support agents to provide more personalized support. We propose a method which groups situations according to their psychological characteristics, and in turn determines which personal values of the user would be promoted or demoted in each group of situations. To do this, we conduct a user study to gather data from participants about situations that they encounter in their daily lives. Results show that the created groups of situations significantly promote or demote certain personal values. This approach can allow support agents to help the user in a way which is in line with their personal values. @en

Behaviour support agents need to be aware of the social environment of the user in order to be able to provide comprehensive support. However, this is a feature that is currently lacking in existing systems. To tackle it, first of all we explore literature from social sciences in order to find which elements of the social environment need to be represented. We structure this knowledge as a two-level ontology that models social situations. We formalize the elements that are needed to model social situations, which consist of different types of meetings between two people. We conduct an experiment to evaluate the lower level of the ontology using feedback from the subjects, and to test whether we can use the data to reason about the priority of different situations. Subjects found our proposed features of social relationships to be understandable and representative. Furthermore, we show these features can be combined in a decision tree to predict the priority of social situations.

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