Efficient Utility Function Learning for Multi-Objective Parameter Optimization with Prior Knowledge

by   Farha A. Khan, et al.

The current state-of-the-art in multi-objective optimization assumes either a given utility function, learns a utility function interactively or tries to determine the complete Pareto front, requiring a post elicitation of the preferred result. However, result elicitation in real world problems is often based on implicit and explicit expert knowledge, making it difficult to define a utility function, whereas interactive learning or post elicitation requires repeated and expensive expert involvement. To mitigate this, we learn a utility function offline, using expert knowledge by means of preference learning. In contrast to other works, we do not only use (pairwise) result preferences, but also coarse information about the utility function space. This enables us to improve the utility function estimate, especially when using very few results. Additionally, we model the occurring uncertainties in the utility function learning task and propagate them through the whole optimization chain. Our method to learn a utility function eliminates the need of repeated expert involvement while still leading to high-quality results. We show the sample efficiency and quality gains of the proposed method in 4 domains, especially in cases where the surrogate utility function is not able to exactly capture the true expert utility function. We also show that to obtain good results, it is important to consider the induced uncertainties and analyze the effect of biased samples, which is a common problem in real world domains.


page 1

page 2

page 3

page 4


Multi-objective Influence Diagrams

We describe multi-objective influence diagrams, based on a set of p obje...

Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making

In many real-world scenarios, the utility of a user is derived from the ...

Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints

We consider the problem of constrained multi-objective blackbox optimiza...

Identifying Coordination in a Cognitive Radar Network – A Multi-Objective Inverse Reinforcement Learning Approach

Consider a target being tracked by a cognitive radar network. If the tar...

Preference Communication in Multi-Objective Normal-Form Games

We study the problem of multiple agents learning concurrently in a multi...

Realistic utility functions prove difficult for state-of-the-art interactive multiobjective optimization algorithms

Improvements to the design of interactive Evolutionary Multiobjective Al...

Racing Multi-Objective Selection Probabilities

In the context of Noisy Multi-Objective Optimization, dealing with uncer...

Please sign up or login with your details

Forgot password? Click here to reset