Sequential decision-making problems with multiple objectives arise naturally in practice and
pose unique challenges for research in decision-theoretic planning and learning, which has …

Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

Multi-objective optimization (MO) is a fast-developing field in computational intelligence
research. Giving decision makers more options to choose from using some post-analysis …

Decision making is the process of selecting a possible course of action from all the available
alternatives. In almost all such problems the multiplicity of criteria for judging the alternatives …

Multilevel decision-making techniques aim to deal with decentralized management
problems that feature interactive decision entities distributed throughout a multiple level …

This paper investigates the use of policy gradient techniques to approximate the Pareto
frontier in Multi-Objective Markov Decision Processes (MOMDPs). Despite the popularity of …

Many kinds of practical problems such as engineering design, industrial m-agement and?
nancial investment have multiple objectives con? icting with eachother. Thoseproblemscanbeformulatedasmultiobj …

Many real-world decision problems have multiple objectives. For example, when choosing a
medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize …

In the predict-then-optimize framework, the objective is to train a predictive model, mapping
from environment features to parameters of an optimization problem, which maximizes …

We present an integrated prediction-optimization (PredOpt) framework to efficiently solve
sequential decision-making problems by predicting the values of binary decision variables …