期刊
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
卷 12, 期 2, 页码 306-317出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2019.2905211
关键词
Computational modeling; Probabilistic logic; Programming; Object oriented modeling; Cognition; Psychology; Affective computing; Affective computing; artificial intelligence; emotion theory; modeling human emotion
资金
- A*STAR Human-Centric Artificial Intelligence Programme (SERC SSF Project) [A1718g0048]
- Singapore MOE AcRF Tier 1 [251RES1709]
- NIH [1R01MH112560-01]
- DARPA [FA8750-14-2-0009]
Affective Computing is a fast-growing field that proposes a probabilistic programming approach to translate psychological theories of emotion into computational models. Probabilistic programming languages offer flexibility, modularity, integration with deep learning libraries, and ease of adoption, providing a standardized platform for theory-building and experimentation.
Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据