Open AccessDECISION MAKING IN DYNAMIC ENVIRONMENTS AN APPLICATION OF MACHINE LEARNING TO THE ANALYTICAL HIERARCHY PROCESS
Author(s) -
Rahim Jassemi-Zargani,
Caelum Kamps
Publication year - 2021
Publication title -
international journal of the analytic hierarchy process
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 3
ISSN - 1936-6744
DOI - 10.13033/ijahp.v13i1.766
Subject(s) - computer science , analytic hierarchy process , interdependence , adaptability , process (computing) , hierarchy , dynamic decision making , reinforcement learning , machine learning , artificial intelligence , operations research , data mining , ecology , political science , law , operating system , economics , engineering , market economy , biology
The purpose of this work is to propose a method of algorithmic decision making that builds on the Analytical Hierarchy Process by applying reinforcement learning. Decision making in dynamic environments requires adaptability as new information becomes available. The Analytical Hierarchy Process (AHP) provides a method for comparative decision making but is insufficient to handle information that becomes available over time. Using the opinions of one or many subject matter experts and the AHP, the relative importance of evidence can be quantified. However, the ability to explicitly measure the interdependencies is more challenging. The interdependency between the different evidence can be exploited to improve the model accuracy, particularly when information is missing or uncertain. To establish this ability within a decision-making tool, the AHP method can be optimized through a stochastic gradient descent algorithm. To illustrate the effectiveness of the proposed method, an experiment was conducted on air target threat classification in time series developing scenarios.