TY - CHAP
T1 - Conclusion
AU - Sreedharan, Sarath
AU - Kulkarni, Anagha
AU - Kambhampati, Subbarao
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - This book presents a concise introduction to recent research on human-aware decision-making, particularly ones focused on the generation of behavior that a human would find explainable or deceptive. Human-aware AI or HAAI techniques are characterized by the acknowledgment that for automated agents to successfully interact with humans, they need to explicitly take into account the human’s expectations about the agent. In particular, we look at how for the robot to successfully work with humans, it needs to not only take into account its model MR, which encodes the robot’s beliefs about the task and their capabilities but also take into account the human’s expectation of the robot model $$M:h^R$$ which captures what the human believes the task to be and what the robot is capable of. It is the model $$M:h^R$$ that determines what the human would expect the robot to do and as such if the robot expects to adhere to or influence the human expectations, it needs to take into account this model. Additionally, this book also introduces three classes of interpretability measures, which capture certain desirable properties of robot behavior. Specifically, we introduce the measures Explicability, Legibility, and Predictability.
AB - This book presents a concise introduction to recent research on human-aware decision-making, particularly ones focused on the generation of behavior that a human would find explainable or deceptive. Human-aware AI or HAAI techniques are characterized by the acknowledgment that for automated agents to successfully interact with humans, they need to explicitly take into account the human’s expectations about the agent. In particular, we look at how for the robot to successfully work with humans, it needs to not only take into account its model MR, which encodes the robot’s beliefs about the task and their capabilities but also take into account the human’s expectation of the robot model $$M:h^R$$ which captures what the human believes the task to be and what the robot is capable of. It is the model $$M:h^R$$ that determines what the human would expect the robot to do and as such if the robot expects to adhere to or influence the human expectations, it needs to take into account this model. Additionally, this book also introduces three classes of interpretability measures, which capture certain desirable properties of robot behavior. Specifically, we introduce the measures Explicability, Legibility, and Predictability.
UR - http://www.scopus.com/inward/record.url?scp=85139495705&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139495705&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-03767-2_11
DO - 10.1007/978-3-031-03767-2_11
M3 - Chapter
AN - SCOPUS:85139495705
T3 - Synthesis Lectures on Artificial Intelligence and Machine Learning
SP - 147
EP - 150
BT - Synthesis Lectures on Artificial Intelligence and Machine Learning
PB - Springer Nature
ER -