@inproceedings{LinCzarnuchMalhotraetal., author = {Lin, Luyuan and Czarnuch, Stephen and Malhotra, Aarti and Yu, Lifei and Schr{\"o}der, Tobias and Hoey, Jesse}, title = {Affectively Aligned Cognitive Assistance Using Bayesian Affect Control Theory}, series = {Ambient Assisted Living and Daily Activities}, booktitle = {Ambient Assisted Living and Daily Activities}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-13105-4}, issn = {1611-3349}, doi = {10.1007/978-3-319-13105-4_41}, pages = {279 -- 287}, abstract = {This paper describes a novel emotionally intelligent cognitive assistant to engage and help older adults with Alzheimer's disease (AD) to complete activities of daily living (ADL) more independently. Our new system combines two research streams. First, the development of cognitive assistants with artificially intelligent controllers using partially observable Markov decision processes (POMDPs). Second, a model of the dynamics of emotion and identity called Affect Control Theory that arises from the sociological literature on culturally shared sentiments. We present background material on both of these research streams, and then demonstrate a prototype assistive technology that combines the two. We discuss the affective reasoning, the probabilistic and decision-theoretic reasoning, the computer-vision based activity monitoring, the embodied prompting, and we show results in proof-of-concept tests.}, subject = {Dichte }, language = {en} } @article{HoeySchroederAlhothali, author = {Hoey, Jesse and Schr{\"o}der, Tobias and Alhothali, Areej}, title = {Affect control processes}, series = {Artificial Intelligence}, volume = {230}, journal = {Artificial Intelligence}, publisher = {Elseviert}, address = {Amsterdam}, issn = {0004-3702}, doi = {10.1016/j.artint.2015.09.004}, pages = {134 -- 172}, abstract = {This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have culturally shared fundamental affective sentiments about identities, behaviours, and objects, and they act so that the transient affective sentiments created during interactions confirm the fundamental sentiments. Humans seek and create situations that confirm or are consistent with, and avoid and suppress situations that disconfirm or are inconsistent with, their culturally shared affective sentiments. This "affect control principle" has been shown to be a powerful predictor of human behaviour. In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents. The new model, called BayesAct, can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices. This allows the model to generate affectively intelligent interactions with people by learning about their identity, predicting their behaviours using the affect control principle, and taking actions that are simultaneously goal-directed and affect-sensitive. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for artificially intelligent systems that interact with humans in two different settings: an exam practice assistant (tutor) and an assistive device for persons with a cognitive disability.}, subject = {Emotionales Verhalten}, language = {en} }