TY - JOUR
T1 - Learning Qualitative Differential Equation models
T2 - a survey of algorithms and applications
AU - Pang, Wei
AU - Coghill, George M.
PY - 2010/3
Y1 - 2010/3
N2 - Over the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been applied to a wide range of fields, including physics, biology and medical science. In this paper, we first identify the scope of this review by distinguishing QML from other QML systems, and then review all the noteworthy QML systems within this scope. The applications of QML in several application domains are also introduced briefly. Finally, the future directions of QML are explored from different perspectives.
AB - Over the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been applied to a wide range of fields, including physics, biology and medical science. In this paper, we first identify the scope of this review by distinguishing QML from other QML systems, and then review all the noteworthy QML systems within this scope. The applications of QML in several application domains are also introduced briefly. Finally, the future directions of QML are explored from different perspectives.
UR - https://www.scopus.com/pages/publications/77952388180
U2 - 10.1017/S0269888909990348
DO - 10.1017/S0269888909990348
M3 - Article
SN - 0269-8889
VL - 25
SP - 69
EP - 107
JO - Knowledge Engineering Review
JF - Knowledge Engineering Review
IS - 1
ER -