Magazine article AI Magazine

Reports of the Workshops of the 32nd AAAI Conference on Artificial Intelligence

Magazine article AI Magazine

Reports of the Workshops of the 32nd AAAI Conference on Artificial Intelligence

Article excerpt

The AAAI-18 workshop program offered 15 workshops covering a wide range of topics in AI. The workshops were held February 2-3, 2018, at the Hilton New Orleans Riverside in New Orleans, Louisiana. This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the Artificial Intelligence for Cyber Security workshop; the AI for Imperfect-Information Games; the Declarative Learning Based Programming workshop; the Engineering Dependable and Secure Machine Learning Systems workshop; the Health Intelligence workshop; the Knowledge Extraction from Games workshop; the Plan, Activity, and Intent Recognition workshop; the Planning and Inference workshop; the Preference Handling workshop; the Reasoning and Learning for Human-Machine Dialogues workshop; and the the AI Enhanced Internet of Things Data Processing for Intelligent Applications workshop.

Affective Content Analysis

The Affective Content Analysis workshop was conducted as an interdisciplinary platform to stimulate cross-disciplinary discussions on affect in content and to more deeply involve the AI and ML community in the open problems in affective content analysis, with a special focus on affect in language and text. Affective content analysis in this context refers to the interdisciplinary research space of computational linguistics, psycholinguistics, consumer psychology, and human-computer interaction (HCI) with respect to the various forms of online communication. The number of workshops and conferences related to affective computing has been growing, which points to the importance of the research problem, as well as the timeliness of this workshop for the AI community.

Affective computing has traditionally focused on modeling human reactions using multimodal sensor data, but not text. Sentiment and emotion analysis, on the other hand, has been applied to both text and multimodal datasets, but this research has been limited to quantifying well-defined human reactions. Affect analysis (that is, techniques and applications to understand the experience of an emotion) in the context of language and text is an up-and-coming research space. Work on affect analysis in language and text spans many research communities: computational linguistics, consumer psychology, HCI, marketing science, and cognitive science. Computational linguists study how language evokes, as well as expresses, emotion. Consumer psychology examines human affect by drawing upon grounded psychological theories of human behavior. The HCI community studies human responses as a part of user experience evaluation. Computational models for consumer psychology theories present a huge opportunity to guide the construction of intelligent systems that understand human reactions, and tools from linguistics and machine learning can provide attractive methods to fulfill those opportunities. Models of affect have recently been adapted for social media platforms, enabling new approaches to understanding user's opinions, intentions, and expressions.

The workshop focused on the analysis of emotions, sentiments, and attitudes in textual, visual, and multimodal content for applications in psychology, consumer behavior, language understanding, and computer vision. Besides original research presentations and posters, the workshop also hosted a range of keynote speakers who highlighted the state of the art in affective computing in a range of fields.

James Pennebaker from the University of Texas Austin provided evidence from a series of studies about how affect and emotion can be mined from the words used by people in everyday life. Dipankar Chakravarti from Virginia Tech discussed some of the challenges involved in affective analysis of text for consumer behavior, especially noting the differences between the experience and the expression of affect. …

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