The complexity of Markov decision processes
Mathematics of Operations Research
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Exploiting gradient information in numerical multi--objective evolutionary optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
SIAM Journal on Optimization
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Combining gradient techniques for numerical multi-objective evolutionary optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Local search for multiobjective function optimization: pareto descent method
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Point-Based Value Iteration for Continuous POMDPs
The Journal of Machine Learning Research
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Learning all optimal policies with multiple criteria
Proceedings of the 25th international conference on Machine learning
The Journal of Machine Learning Research
Stochastic local search for POMDP controllers
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Solving POMDPs by searching the space of finite policies
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Multi-reward policies for medical applications: anthrax attacks and smart wheelchairs
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A survey of multi-objective sequential decision-making
Journal of Artificial Intelligence Research
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Plans and decisions in many real-world scenarios are made under uncertainty and to satisfy multiple, possibly conflicting, objectives. In this work, we contribute the multi-reward partially-observable Markov decision process (MR-POMDP) as a general modelling framework. To solve MR-POMDPs, we present two hybrid (memetic) multi-objective evolutionary algorithms that generate non-dominated sets of policies (in the form of stochastic finite state controllers). Performance comparisons between the methods on multi-objective problems in robotics (with 2, 3 and 5 objectives), web-advertising (with 3, 4 and 5 objectives) and infectious disease control (with 3 objectives), revealed that memetic variants outperformed their original counterparts. We anticipate that the MR-POMDP along with multi-objective evolutionary solvers will prove useful in a variety of theoretical and real-world applications.