학술논문

Prescriptive Inductive Operations on Probabilities Serving to Decision-Making Agents
Document Type
Periodical
Author
Source
IEEE Transactions on Systems, Man, and Cybernetics: Systems IEEE Trans. Syst. Man Cybern, Syst. Systems, Man, and Cybernetics: Systems, IEEE Transactions on. 52(4):2110-2120 Apr, 2022
Subject
Signal Processing and Analysis
Robotics and Control Systems
Power, Energy and Industry Applications
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Tools
Task analysis
Uncertainty
Merging
Cognition
Probabilistic logic
Entropy
Approximation
dynamic decision making (DM)
extension
merging
relative entropy (RE)
uncertainty
Language
ISSN
2168-2216
2168-2232
Abstract
Approximation, extension, and merging of probability distributions support inductive reasoning. They serve to modeling, knowledge, and preference elicitation as well as to a soft cooperation within various decision-making (DM) scenarios. The theory dubbed as the fully probabilistic design of DM strategies unifies the design of these operations on distributions. The unification decreases the danger of their improper choice and use. Still there is an uncertainty how the gained tools should be wielded. This article diminishes it by spelling out conditions ruling their exploitation. This article serves as an updated description of these tools, provides examples of their use, and guides their tailoring to diverse scenarios.