Preference Learning: An Introduction

Fürnkranz, Johannes; Hüllermeier, Eyke

doi:10.1007/978-3-642-14125-6_1

Cited by 106 publications

(99 citation statements)

References 37 publications

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“…Available methods include artificial neural networks, Bayesian networks, decision trees, support vector machines and standard linear regression. Alternatively, if experience is annotated in a ranked format, standard supervised-learning techniques are inapplicable, as the problem becomes one of preference learning [7]. Neuro-evolutionary preference learning [29] and rank-based support vector machines [12], along with simpler methods such as [23] linear discriminant analysis [28], are some of the available approaches for learning preferences.…”

Section: Modelling Approachesmentioning

confidence: 99%

The experience-driven perspective

Shaker

Togelius

Yannakakis

2016

Procedural Content Generation in Games

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Ultimately, content is generated for the player. But so far, our algorithms have not taken specific players into account. Creating computational models of a player's behaviour, preferences, or skills is called player modelling. With a model of the player, we can create algorithms that create content specifically tailored to that player. The experience-driven perspective on procedural content generation provides a framework for content generation based on player modelling; one of the most important ways of doing this is to use a player model in the evaluation function for search-based PCG. This chapter discusses different ways of collecting and encoding data about the player, primarily player experience, and ways of modelling this data. It also gives examples of different ways in which such models can be used.

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Section: Modelling Approachesmentioning

confidence: 99%

The experience-driven perspective

Shaker

Togelius

Yannakakis

2016

Procedural Content Generation in Games

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“…Modeling the preference or overall style of a designer falls under the category of preference learning [12], and requires extensive information on a designer's choices, rankings or ratings among alternatives. Such adaptive models of taste have been trained based on a user's choice of one artifact over others [6] or from a user's rankings of artifacts in order of preference [13].…”

Section: B Designer Modelingmentioning

confidence: 99%

Designer modeling for Sentient Sketchbook

Liapis

Yannakakis

Togelius

2014

2014 IEEE Conference on Computational Intelligence and Games

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Abstract-This paper documents the challenges in creating a computer-aided level design tool which incorporates computergenerated suggestions which appeal to the human user. Several steps are suggested in order to make the suggestions more appropriate to a specific user's overall style, current focus, and end-goals. Designer style is modeled via choice-based interactive evolution which adapts the impact of different dimensions of quality based on the designer's choice of certain suggestions over others. Modeling process is carried out similarly to style, but adapting to the current focus of the designer's actions. Goals are modeled by estimating the visual patterns of the designer's final artifact and changing the parameters of the algorithm to enforce such patterns on generated suggestions.

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“…On the other hand, a growing interest in ranking problems has recently emerged in fields related to information retrieval, Internet-related applications, or bio-informatics (see, e.g, Fürnkranz and Hüllermeier 2011;Liu 2011). Indeed, ranking is at the core of document retrieval, collaborative filtering, or computation advertising.…”

Section: Introductionmentioning

confidence: 99%

Robust ordinal regression in preference learning and ranking

et al. 2013

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Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking.

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Preference Learning: An Introduction

Cited by 106 publications

References 37 publications

The experience-driven perspective

The experience-driven perspective

Designer modeling for Sentient Sketchbook

Robust ordinal regression in preference learning and ranking

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