Using Psychological Characteristics of Situations for Social Situation Comprehension in Support Agents

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.

[1]  John A. Edwards,et al.  The structure of perceived qualities of situations , 2005 .

[2]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[3]  Been Kim,et al.  Towards A Rigorous Science of Interpretable Machine Learning , 2017, 1702.08608.

[4]  Sang Eun Woo,et al.  CAPTION-ing the Situation: A Lexically-Derived Taxonomy of Psychological Situation Characteristics , 2017, Journal of personality and social psychology.

[5]  Tim Miller,et al.  Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..

[6]  Qian Yang,et al.  Designing Theory-Driven User-Centric Explainable AI , 2019, CHI.

[7]  Michael Chromik,et al.  A Taxonomy for Human Subject Evaluation of Black-Box Explanations in XAI , 2020, ExSS-ATEC@IUI.

[8]  Jasper van der Waa,et al.  Evaluating XAI: A comparison of rule-based and example-based explanations , 2021, Artif. Intell..

[9]  K. Lewin.  Field Theory and Experiment in Social Psychology: Concepts and Methods , 1939, American Journal of Sociology.

[10]  Predicting the Priority of Social Situations for Personal Assistant Agents , 2020, PRIMA.

[11]  Bryan T. Kelly,et al.  Empirical Asset Pricing Via Machine Learning , 2018, The Review of Financial Studies.

[12]  Catholijn M. Jonker,et al.  Towards Social Situation Awareness in Support Agents , 2021, ArXiv.

[13]  Nicolas A. Brown,et al.  Measuring the Evolutionarily Important Goals of Situations , 2015, Evolutionary psychology : an international journal of evolutionary approaches to psychology and behavior.

[14]  Daniel A. Wilkenfeld,et al.  Goals Affect the Perceived Quality of Explanations , 2015, CogSci.

[15]  Andreas Reckwitz.  Toward a Theory of Social Practices , 2002 .

[16]  Gary Klein,et al.  Metrics for Explainable AI: Challenges and Prospects , 2018, ArXiv.

[17]  Catholijn M. Jonker,et al.  Grouping Situations Based on their Psychological Characteristics Gives Insight into Personal Values , 2020, MRC@ECAI.

[18]  Hui Guo,et al.  Arnor: Modeling Social Intelligence via Norms to Engineer Privacy-Aware Personal Agents , 2017, AAMAS.

[19]  Fabiola H. Gerpott,et al.  How Do People Think About Interdependence? A Multidimensional Model of Subjective Outcome Interdependence , 2017, Journal of personality and social psychology.

[20]  Michael Winikoff,et al.  No Pizza for You: Value-based Plan Selection in BDI Agents , 2017, IJCAI.

[21]  Michel C. A. Klein,et al.  A Personalized Support Agent for Depressed Patients: Forecasting Patient Behavior Using a Mood and Coping Model , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[22]  Matthias Ziegler,et al.  The Situational Eight DIAMONDS: a taxonomy of major dimensions of situation characteristics. , 2014, Journal of personality and social psychology.

[23]  Frank Dignum,et al.  Contextualized Planning Using Social Practices , 2014, COIN@AAMAS/PRICAI.

[24]  Nava Tintarev,et al.  Evaluating the effectiveness of explanations for recommender systems , 2012, User Modeling and User-Adapted Interaction.

[25]  Catholijn M. Jonker,et al.  Who's That? - Social Situation Awareness for Behaviour Support Agents - A Feasibility Study , 2019, EMAS@AAMAS.

[26]  Veton Kepuska,et al.  Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home) , 2018, 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC).

[27]  K. Fitzpatrick,et al.  Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial , 2017, JMIR mental health.

[28]  Milind Tambe,et al.  Electric Elves: What Went Wrong and Why , 2006, AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications.

[29]  Victor R. Lesser,et al.  Creating Socially Adaptive Electronic Partners: Interaction, Reasoning and Ethical Challenges , 2015, AAMAS.

[30]  Russell Beale,et al.  Digital Behaviour Change Interventions to Break and Form Habits , 2018, ACM Trans. Comput. Hum. Interact..

[31]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.