Toward Social Situation Awareness in Support Agents

Kola, Ilir; Murukannaiah, Pradeep K.; Jonker, Catholijn M.; Riemsdijk, M. Birna van

doi:10.1109/mis.2022.3163625

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Such an approach would on one hand benefit from having the user in the loop, and on the other hand would reduce the amount of interruptions that the user gets from the support agent. Towards a practical application, more research is also needed on how to perceive user situations and how to automatically assess the psychological characteristics of situations [13,33]. Next, it would be important to replicate our results for other personal values.…”

Section: Future Workmentioning

confidence: 99%

Does Personalization Help? Predicting How Social Situations Affect Personal Values

Kola

Isufaj

Jonker

2022

HHAI2022: Augmenting Human Intellect

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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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Section: Future Workmentioning

confidence: 99%

Does Personalization Help? Predicting How Social Situations Affect Personal Values

Kola

Isufaj

Jonker

2022

HHAI2022: Augmenting Human Intellect

Self Cite

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Using psychological characteristics of situations for social situation comprehension in support agents

Kola¹,

Jonker²,

Riemsdijk³

2023

Auton Agent Multi-Agent Syst

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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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Situation-Aware Sensor-Based Wearable Computing Systems: A Reference Architecture-Driven Review

2022

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Toward Social Situation Awareness in Support Agents

Cited by 3 publications

References 27 publications

Does Personalization Help? Predicting How Social Situations Affect Personal Values

Does Personalization Help? Predicting How Social Situations Affect Personal Values

Using psychological characteristics of situations for social situation comprehension in support agents

Situation-Aware Sensor-Based Wearable Computing Systems: A Reference Architecture-Driven Review

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