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Multi-Objective Decision Making

Morgan & Claypool Publishers

Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize efficacy of the treatment while minimizing side effects. These objectives typically conflict, e.g., we can increase the efficacy of the treatment, but cause more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. ISBN 9781627059602, 129 pages.


Online Article Ranking as a Constrained, Dynamic, Multi-Objective Optimization Problem

AAAI Conferences

The content ranking problem in a social news website is typically a function that maximizes a scalar metric like dwell-time. However, in most real-world applications we are interested in more than one metric — for instance, simultaneously maximizing click-through rate, monetization metrics, and dwell-time — while also satisfying the constraints from traffic requirements promised to different publishers. The solution needs to be an online algorithm since the data arrives serially. Additionally, the objective function and the constraints can dynamically change. In this paper, we formulate this problem as a constrained, dynamic, multi-objective optimization problem. We propose a novel framework that extends a successful genetic optimization algorithm, NSGA-II, to solve our ranking problem. We evaluate optimization performance using the Hypervolume metric. We demonstrate the application of our framework on a real-world article ranking problem from the Yahoo! News page. We observe that our proposed solution makes considerable improvements in both time and performance over a brute-force baseline technique that is currently in production.


The Real-Time Strategy Game Multi-Objective Build Order Problem

AAAI Conferences

In this paper we examine the build order problem in real-time strategy (RTS) games in which the objective is to optimize execution of a strategy by scheduling actions with respect to a set of subgoals. We model the build order problem as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA). A three dimensional solution space is presented providing a depiction of a Pareto front for the build order MOP. Results of the online strategic planning tool are provided which demonstrate that our planner out-performs an expert scripted player. This is demonstrated for an AI agent in the Spring Engine Balanced Annihilation RTS game.


Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems

arXiv.org Artificial Intelligence

Whether it is in the field of production, logistics, in medicine or biology; everywhere the global optimal solution or the set of global optimal solutions is sought. However, most real-world problems are of nonlinear nature and naturally multimodal which poses severe problems to global optimization. Multimodality, the existence of multiple (local) optima, is regarded as one of the biggest challenges for continuous single-objective problems [23]. A lot of algorithms get stuck searching for the global optimum or are requiring many function evaluations to escape local optima. One of the most popular strategies for dealing with multimodal problems are population-based methods like evolutionary algorithms due to their global search abilities [2]. In this paper we will examine another approach of coping with local traps, namely multiobjectivization. By transforming a single-objective into a multi-objective problem, we aim at exploiting the properties of multi-objective landscapes. So far, the characteristics of single-objective optimization problems have often been directly transferred to the multiobjective domain.


Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Parallel Versions

arXiv.org Machine Learning

Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes to maximize the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed.