Authors:
Pratyusha Kalluri
1
and
Pablo Gervás
2
Affiliations:
1
MIT, United States
;
2
Universidad Complutense de Madrid, Spain
Keyword(s):
Social Perception, Social Cognition, Knowledge Representation, Bayesian Inference.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bayesian Networks
;
Enterprise Information Systems
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Soft Computing
;
Symbolic Systems
Abstract:
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 so
cial scenarios, and we identify potential
applications to automated narrative analysis, AI narrative generation, and assistive technology.
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