# Contextual Emotion Appraisal Based on a Sentential Cognitive System for Robots
## 1 Introduction  
As the technology of human-robot interaction (HRI) matures, the idea of robots being companions of humans in their daily lives is becoming popular. Emotional exchange with robots facilitates a human's empathy, engagement, and collaboration. An affective robot involves understanding human emotion and expressing believable emotion to humans, where the latter need to define the robot's emotional states and their expressions. Studies have focused on how emotions should be expressed on a robot's face [1,2] and in its voice [3], and how emotions should be appraised based on recognized information or stimulation [4,5]. One of the most significant studies on robots simulating emotion involves MIT's social robot Kismet [3]. Its cognitive architecture was influenced by [PERSON]'s perceptual approach to emotion [6], which included critical emotional constructs, such as arousal (strong versus weak) and valence (positive versus negative) associated with the robot's internal drive (e.g., social needs, fatigue). [PERSON] can express affect through face, voice, and posture based on an emotional internal representation form by the affective appraisal of external stimuli, such as threatening motion or praising a human's speech.  
Emotion is intertwined with context. Specifically, emotion is associated with an object or an individual at a given time. How contexts from past to present are induced to appraise a robot's emotional state is important. A significant amount of research has been conducted in cognitive psychology on this subject. [PERSON] et al. claimed that emotion is determined by the interpretation of the context that induces emotions as reactions regarding interest toward an event or a subject. They categorized the emotional appraisal process based on 22 contexts [7].  
Attempts have been made to apply psychological emotional models to robots. A study appraised emotions by defining a list of objects and actions and analyzed their relationship [4]. Another study appraised emotions using an explanatory process for recognized information [8]. However, it is difficult to consider these studies as full contextual emotion appraisals because they appraise emotions based on objects or events at points in time, rather than in a context extending from past to present.  
In this paper, an emotion appraisal system based on the cognitive context of robots is proposed. For computational modeling, a simplified Ortony, Clore, and Collins (OCC) model is adopted, and that is applied to a sentential cognitive system (SCS) to combine emotional valence with cognitive information. The state of human emotions decreases over time to neutral, and some emotions are transferred to different emotions. In this paper, the change in intensity of emotional valence and the transition of emotion are modeled and formulated. For contextual emotion appraisal, the targets of emotion include objects, agents, and events. The robot evaluates the emotion of an agent dealing with objects, and the behavior of the agent becomes an event, which is a target of emotion. An implementation of the proposed approach is conducted with scenarios where a robot interacts with humans manipulating objects on a table.  
The contributions of this paper are as follows: (1) a contextual emotion appraisal model abstracting and simplifying the OCC model is implemented in a SCS of a cognitive robot. (2) The suggested emotion appraisal model provides the principal contextual characteristics by formulating the emotional valence related among the targets of objects, agents, and events and introducing emotional transition according to the events interrelated with the targets.  
This paper is organized as follows. In Section 2, related work on emotion appraisal studies is described. Section 3 details the contextual emotion appraisal model. Section 4 depicts a SCS based contextual emotion appraisal system. Section 5 provides the implementation of the proposed approach to a service robot, and Section 6 depicts conclusions and future work.