TY - CHAP
T1 - Measures of Interpretability
AU - Sreedharan, Sarath
AU - Kulkarni, Anagha
AU - Kambhampati, Subbarao
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - This chapter will act as the introduction to the technical discussions in the book. We will start by establishing some of the basic notations that we will use, including the definitions of deterministic goal-directed planning problems, incomplete planning models, sensor models, etc. With the basic notations in place, we will then focus on establishing the three main interpretability measures in human-aware planning; namely, Explicability, Legibility, and Predictability. We will revisit two of these measures (i.e., explicability and legibility) and discuss methods to boost these measures throughout the later chapters.
AB - This chapter will act as the introduction to the technical discussions in the book. We will start by establishing some of the basic notations that we will use, including the definitions of deterministic goal-directed planning problems, incomplete planning models, sensor models, etc. With the basic notations in place, we will then focus on establishing the three main interpretability measures in human-aware planning; namely, Explicability, Legibility, and Predictability. We will revisit two of these measures (i.e., explicability and legibility) and discuss methods to boost these measures throughout the later chapters.
UR - https://www.scopus.com/pages/publications/85139499375
UR - https://www.scopus.com/pages/publications/85139499375#tab=citedBy
U2 - 10.1007/978-3-031-03767-2_2
DO - 10.1007/978-3-031-03767-2_2
M3 - Chapter
AN - SCOPUS:85139499375
T3 - Synthesis Lectures on Artificial Intelligence and Machine Learning
SP - 15
EP - 26
BT - Synthesis Lectures on Artificial Intelligence and Machine Learning
PB - Springer Nature
ER -