A ONE-OF-A-KIND GUIDE TO THE BEST PRACTICES IN DECISION ANALYSIS Decision
analysis provides powerful tools for addressing complex decisions that involve uncertainty …

Three-way decisions provide a trisecting-and-acting framework for complex problem solving.
For a cost-sensitive decision-making problem under multiple levels of granularity, sequential …

Chang [C.-T. Chang, Multi-choice goal programming, Omega, The Inter. J. Manage. Sci. 35
(2007) 389–396] has recently proposed a new method namely multi-choice goal …

Many real-world problems involve the optimization of multiple, possibly conflicting
objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard …

Expensive multi-objective optimization problems can be found in many real-world
applications, where their objective function evaluations involve expensive computations or …

Machine learning is inherently a multiobjective task. Traditionally, however, either only one
of the objectives is adopted as the cost function or multiple objectives are aggregated to a …

Tasks in multi-task learning often correlate, conflict, or even compete with each other. As a
result, a single solution that is optimal for all tasks rarely exists. Recent papers introduced …

We provide a problem oriented review of multiple criteria decision research. We focus on
classifying multiple criteria decision making problems, and discussing how decision makers …

Complexity and variety of modern multiobjective optimisation problems result in the
emergence of numerous search techniques, from traditional mathematical programming to …

This paper is about learning a continuous approximation of the Pareto frontier in Multi-
Objective Markov Decision Problems (MOMDPs). We propose a policy-based approach that …