Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at https://github.com/lmzintgraf/gp_pref_elici

Quality Assessment of MORL Algorithms: A Utility-Based Approach

Sequential decision-making problems with multiple objectives occur often in practice. In such settings, the utility of a policy depends on how the user values different trade-offs between the objectives. Such valuations can be expressed by a so-called scalarisation function. However, the exact scalarisation function can be unknown when the agents should learn or plan. Therefore, instead of a single solution, the agents aim to produce a solution set that contains an optimal solution for all possible scalarisations. Because it is often not possible to produce an exact solution set, many algorithms have been proposed that produce approximate solution sets instead. We argue that when comparing these algorithms we should do so on the basis of user utility, and on a wide range of problems. In practice however, comparison of the quality of these algorithms have typically been done with only a few limited benchmarks and metrics that do not directly express the utility for the user. In this paper, we propose two metrics that express either the expected utility, or the maximal utility loss with respect to the optimal solution set. Furthermore, we propose a generalised benchmark in order to compare algorithms more reliably

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems

Interactive multi-objective reinforcement learning in multi-armed bandits for any utility function

In interactive multi-objective reinforcement learning (MORL), an agent has to simultaneously learn about the environment and the preferences of the user, in order to quickly zoom in on those decisions that are likely to be preferred by the user. In this paper we study interactive MORL in the context of multi-objective multi-armed bandits. Contrary to earlier approaches to interactive MORL, we do not make stringent assumptions about the utility functions of the user, but allow for non-linear preferences. We propose a new approach called Gaussian-process Utility Thompson Sampling (GUTS), which employs non-parametric Bayesian learning to allow any type of utility function, exploits monotonicity information, and limits the number of queries posed to the user by ensuring that questions are statistically significant. We show empirically that, in contrast to earlier methods, GUTS can learn non-linear preferences, and that the regret and number of queries posed to the user are highly sub-linear in the number of arm pulls

Interactive multi-objective reinforcement learning in multi-armed bandits for any utility function

In interactive multi-objective reinforcement learning (MORL), an agent has to simultaneously learn about the environment and the preferences of the user, in order to quickly zoom in on those decisions that are likely to be preferred by the user. In this paper we study interactive MORL in the context of multi-objective multi-armed bandits. Contrary to earlier approaches to interactive MORL, we do not make stringent assumptions about the utility functions of the user, but allow for non-linear preferences. We propose a new approach called Gaussian-process Utility Thompson Sampling (GUTS), which employs non-parametric Bayesian learning to allow any type of utility function, exploits monotonicity information, and limits the number of queries posed to the user by ensuring that questions are statistically significant. We show empirically that, in contrast to earlier methods, GUTS can learn non-linear preferences, and that the regret and number of queries posed to the user are highly sub-linear in the number of arm pulls

Visualizing Deep Neural Network Decisions

No abstract available

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class. It overcomes several shortcoming of previous methods and provides great additional insight into the decision making process of classifiers. Making neural network decisions interpretable through visualization is important both to improve models and to accelerate the adoption of black-box classifiers in application areas such as medicine. We illustrate the method in experiments on natural images (ImageNet data), as well as medical images (MRI brain scans)

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input. When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class. It overcomes several shortcoming of previous methods and provides great additional insight into the decision making process of classifiers. Making neural network decisions interpretable through visualization is important both to improve models and to accelerate the adoption of black-box classifiers in application areas such as medicine. We illustrate the method in experiments on natural images (ImageNet data), as well as medical images (MRI brain scans)