Socially assistive robots provide physical and mental assistance for humans via cognitive human-machine interactions. These robots should sustain long-term engaging interactions with humans in a similar way humans interact with each other. According to the theory of mind, in their interactions humans develop cognitive models of each other in order to estimate their unobservable state-of-mind, predict their behavior, and act accordingly. Based on the theory of mind, we propose mathematical cognitive models of humans, which enable machines to understand cognitive procedures of humans in general and as distinct individuals. In particular, a network representation that is formulated based on a proposed extended version of fuzzy cognitive maps is introduced. The resulting models are identified and validated using (1) computer-based simulations designed according to a general data set of human's intuitive reasoning and literature and (2) real-life personalised experiments with 15 human participants. The results of the experiments show that the proposed cognitive models can excellently be personalised to different participants and precisely estimate and predict their current and future state-of-mind and expected behaviors.
翻译:社会辅助机器人通过认知人的机器互动,为人类提供身心帮助。这些机器人应该以人类相互互动的类似方式保持与人类的长期接触互动。根据思想理论,人类在其互动中发展彼此的认知模型,以便估计其不可观察的状态,预测其行为并据此采取行动。根据思想理论,我们提出了人类的数学认知模型,使机器能够理解人类一般的认知程序以及作为独特个人的认知程序。特别是,根据拟扩大版的模糊认知图制作的网络代表方式被引入。所产生的模型被确定和验证,使用(1) 计算机模拟,根据一套人类直观推理和文学的一般数据设计,(2) 与15名人类参与者进行真实生命个人化的实验。实验结果显示,拟议的认知模型可以很好地针对不同的参与者个人,精确地估计和预测其当前和今后的状况和预期的行为。