Affinity-based Interpretation of Triangle Social Scenarios

Pratyusha Kalluri, Pablo Gervás


Computational interpretation of social scenarios is a critical step towards more human-like artificial intelligence. We present a model that interprets social scenarios by deducing the affinities of the constituent relationships. First, our model deploys Bayesian inference with an action affinity lexicon to infer probabilistic affinity relations characterizing the scenario. Subsequently, our model is able to use the inferred affinity relations to choose the most probable statement from multiple plausible statements about the scenario. We evaluate our approach on 80 Triangle-COPA multiple-choice problems that test interpretation of social scenarios. Our approach correctly answers the majority (59) of the 80 questions (73.75%), including questions about behaviors, emotions, social conventions, and complex constructs. Our model maintains interpretive power while using knowledge captured in the lightweight action affinity lexicon. Our model is a promising approach to interpretation of social scenarios, and we identify potential applications to automated narrative analysis, AI narrative generation, and assistive technology.


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Paper Citation

in Harvard Style

Kalluri P. and Gervás P. (2017). Affinity-based Interpretation of Triangle Social Scenarios . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 640-647. DOI: 10.5220/0006205506400647

in Bibtex Style

author={Pratyusha Kalluri and Pablo Gervás},
title={Affinity-based Interpretation of Triangle Social Scenarios},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Affinity-based Interpretation of Triangle Social Scenarios
SN - 978-989-758-220-2
AU - Kalluri P.
AU - Gervás P.
PY - 2017
SP - 640
EP - 647
DO - 10.5220/0006205506400647