شاخص های غیر شفاهی از تمایلات بدخواهانه: اجزاء عاطفی برای استفهام آموزش واقعیت مجازی

Models of affective behavior are critical for the development of training systems that are designed to exercise social interactions. Potential applications include various security-oriented operations such as police interrogation, airport security, border crossings, and military peacekeeping. Aside from speech, humans also communicate through vocalizations and inflections, as well as with body language. Such nonverbal communication can convey affect such as anger or nervousness that is important in identifying deception. In this research, a trainee is asked to perform checkpoint duty and question drivers of vehicles about their identity and reasons for entering a secured area. Most of the encounters are routine and innocuous, but occasionally a scenario unfolds that requires additional interrogation and rapid decision-making the part of the trainee. These special scenarios require the individual to draw upon his/her knowledge of social interactions in order to make the proper decisions and act appropriately. Virtual environments that address this form of training are few. Accordingly, the present paper describes an ongoing program of research designed to generate affective states for intelligent agents, create affective component behaviors to convey cues for anger, nervousness, and deception, and provide a complex interrogative training environment to exercise judgment-based decision-making.