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preference learning on an OSGi Based Home Gateway

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2009年第3期55卷 1322-1329页

作者： Hasan, Md Kamrul Ngoc, Kim Anh Pham Lee, Young-Koo Lee, Sungyoung Kyung Hee Univ Dept Comp Engn Ubiquitous Comp Lab Seoul South Korea

The goal of ubiquitous computing is to create intelligent environment. To make the environment adapt rationally according to the desire of users, the system should he able to guess users' interest, by learning users' preferences. Users' preferences are sometimes conflicting and needs to he resolved. When many users are involved in a ubiquitous environment, the decisions of one user can be affected by the desires of others. This makes learning and prediction of user preferences difficult. In this paper we prove that learning and prediction of user preference is NP-Hard. So, we propose Bayesian RN-Metanetwork a multilevel Bayesian network to model user preference and priority. This is a semi optimal online learning approach. By using game theory we prove that the method we use will certainly converge after a while. We also provide implementation details of the metanetwork on an OSGi based home gateway.

关键词： preference learning OSGi Home Gateway Bayesian RN-Metanetwork

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preference learning for Cognitive Modeling: A Case Study on Entertainment preferences

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2009年第6期39卷 1165-1175页

作者： Yannakakis, Georgios N. Maragoudakis, Manolis Hallam, John IT Univ Copenhagen DK-2300 Copenhagen Denmark Univ Aegean Dept Informat & Commun Syst Engn Karlovassi 83200 Greece Univ So Denmark Maersk Mc Kinney Moller Inst DK-5230 Odense Denmark

learning from preferences, which provide means for expressing a subject's desires, constitutes an important topic in machine learning research. This paper presents a comparative study of four alternative instance preference learning algorithms (both linear and nonlinear). The case study investigated is to learn to predict the expressed entertainment preferences of children when playing physical games built on their personalized playing features (entertainment modeling). Two of the approaches are derived from the literature-the large-margin algorithm (LMA) and preference learning with Gaussian processes-while the remaining two are custom-designed approaches for the problem under investigation: meta-LMA and neuroevolution. preference learning techniques are combined with feature set selection methods permitting the construction of effective preference models, given suitable individual playing features. The underlying preference model that best reflects children preferences is obtained through neuroevolution: 82.22% of cross-validation accuracy in predicting reported entertainment in the main set of game survey experimentation. The model is able to correctly match expressed preferences in 66.66% of cases on previously unseen data (p-value = 0.0136) of a second physical activity control experiment. Results indicate the benefit of the use of neuroevolution and sequential forward selection for the investigated complex case study of cognitive modeling in physical games.

关键词： Augmented-reality games Bayesian learning (BL) entertainment modeling large-margin classifiers neuroevolution preference learning

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preference learning for Move Prediction and Evaluation Function Approximation in Othello

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IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES 2014年第3期6卷 300-313页

作者： Runarsson, Thomas Philip Lucas, Simon M. Univ Iceland Sch Engn & Nat Sci IS-101 Reykjavik Iceland Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

This paper investigates the use of preference learning as an approach to move prediction and evaluation function approximation, using the game of Othello as a test domain. Using the same sets of features, we compare our approach with least squares temporal difference learning, direct classification, and with the Bradley-Terry model, fitted using minorization-maximization (MM). The results show that the exact way in which preference learning is applied is critical to achieving high performance. Best results were obtained using a combination of board inversion and pair-wise preference learning. This combination significantly outperformed the others under test, both in terms of move prediction accuracy, and in the level of play achieved when using the learned evaluation function as a move selector during game play.

关键词： Computational and artificial intelligence n-tuple preference learning temporal difference learning Othello

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preference learning in consecutive experimental auctions

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AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS 2000年第4期82卷 1016-1021页

作者： Shogren, JF List, JA Hayes, DJ Univ Wyoming Dept Econ & Finance Laramie WY 82071 USA Univ Cent Florida Dept Econ Orlando FL 32765 USA Iowa State Univ Sci & Technol Dept Econ Ames IA 50011 USA

This paper explores the origins of the strikingly high price premia paid for new food products in lab valuation exercises Our experimental design distinguishes between two explanations of this phenomenon: novelty of the experimental experience versus the novelty of the good, i.e., preference learning-bids reflect a person's desire to learn how an unfamiliar good fits into their preference set. Subjects bid in four consecutive experimental auctions for three goods that vary in familiarity, candy bars mangos and irradiated meat. Our results suggest that preference learning is the main source of the high premia, and that novelty of the experimental experience does not in itself artificially inflate valuations.

关键词： experimental auctions preference learning price premia

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preference learning Using the Choquet Integral: The Case of Multipartite Ranking

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2012年第6期20卷 1102-1113页

作者： Tehrani, Ali Fallah Cheng, Weiwei Huellermeier, Eyke Univ Marburg Dept Math & Comp Sci D-35032 Marburg Germany

We propose a novel method for preference learning or, more specifically, learning to rank, where the task is to learn a ranking model that takes a subset of alternatives as input and produces a ranking of these alternatives as output. Just like in the case of conventional classifier learning, training information is provided in the form of a set of labeled instances, with labels or, say, preference degrees taken from an ordered categorical scale. This setting is known as multipartite ranking in the literature. Our approach is based on the idea of using the (discrete) Choquet integral as an underlying model for representing ranking functions. Being an established aggregation function in fields such as multiple criteria decision making and information fusion, the Choquet integral offers a number of interesting properties that make it attractive from a machine learning perspective, too. The learning problem itself comes down to properly specifying the fuzzy measure on which the Choquet integral is defined. This problem is formalized as a margin maximization problem and solved by means of a cutting plane algorithm. The performance of our method is tested on a number of benchmark datasets.

关键词： Attribute interactions Choquet integral classification monotonicity preference learning

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preference learning and multiple criteria decision aiding: differences, commonalities, and synergies-part II

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4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 2024年第3期22卷 313-349页

作者： Huellermeier, Eyke Slowinski, Roman Ludwig Maximilians Univ Munchen Inst Informat Munich Germany Munich Ctr Machine Learning Munich Germany Poznan Univ Tech Inst Comp Sci Poznan Poland Polish Acad Sci Syst Res Inst Warsaw Poland

This article elaborates on the connection between multiple criteria decision aiding (MCDA) and preference learning (PL), two research fields with different roots and developed in different communities. It complements the first part of the paper, in which we started with a review of MCDA. In this part, a similar review will be given for PL, followed by a systematic comparison of both methodologies, as well as an overview of existing work on combining PL and MCDA. Our main goal is to stimulate further research at the junction of these two methodologies.

关键词： preference learning preference modelling Multiple criteria decision aiding Multiple criteria decision making Machine learning

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preference learning Labels by Anchoring on Consecutive Annotations 24

Preference Learning Labels by Anchoring on Consecutive Annot...

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Interspeech Conference

作者： Naini, Abinay Reddy Salman, Ali N. Busso, Carlos Univ Texas Dallas Multimodal Signal Proc MSP Lab Dept Elect & Comp Engn Richardson TX 75080 USA

An important task in human-computer interaction is to rank speech samples according to their expressive content. A preference learning framework is appropriate for obtaining an emotional rank for a set of speech samples. However, obtaining reliable labels for training a preference learning framework is a challenging task. Most existing databases provide sentence-level absolute attribute scores annotated by multiple raters, which have to be transformed to obtain preference labels. Previous studies have shown that evaluators anchor their absolute assessments on previously annotated samples. Hence, this study proposes a novel formulation for obtaining preference learning labels by only considering annotation trends assigned by a rater to consecutive samples within an evaluation session. The experiments show that the use of the proposed anchor-based ordinal labels leads to significantly better performance than models trained using existing alternative labels.

关键词： Speech emotion recognition preference learning

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A reinforcement learning approach for thermostat setpoint preference learning

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Building Simulation 2024年第1期17卷 131-146页

作者： Hussein Elehwany Mohamed Ouf Burak Gunay Nunzio Cotrufo Jean-Simon Venne Department of Environmental Engineering Carleton UniversityOttawaCanada Departmentof Building Civiland Environmental EngineeringConcordia UniversityMontrealCanada BrainBox Al Inc. MontrealCanada

Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy *** OcC has been used in a wide range of building applications,an OcC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints *** recent studies used reinforcement learning(RL)as an agent for OcC to optimize energy use and occupant *** studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat *** paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat *** algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python APl of *** simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration *** substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity.

关键词： reinforcement learning preference learning occupant-centric controls smart thermostats off-policy learning

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preference learning Based Decision Map Algebra: Specification and Implementation 19th

Preference Learning Based Decision Map Algebra: Specificatio...

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19th Annual UK Workshop on Computational Intelligence (UKCI)

作者： Abubahia, Ahmed M. Chakhar, Salem Cocea, Mihaela Univ Portsmouth Fac Technol Sch Comp Portsmouth Hants England Univ Portsmouth Portsmouth Business Sch Portsmouth Hants England Univ Portsmouth Ctr Operat Res & Logist Portsmouth Hants England

ISBN: (纸本)9783030299330;9783030299323

Decision Map Algebra (DMA) is a generic and context independent algebra, especially devoted to spatial multicriteria modelling. The algebra defines a set of operations which formalises spatial multi-criteria modelling and analysis. The main concept in DMA is decision map, which is a planar subdivision of the study area represented as a set of non-overlapping polygonal spatial units that are assigned, using a multicriteria classification model, into an ordered set of classes. Different methods can be used in the multicriteria classification step. In this paper, the multicriteria classification step relies on the Dominance-based Rough Set Approach (DRSA), which is a preference learning method that extends the classical rough set theory to multicriteria classification. The paper first introduces a preference learning based approach to decision map construction. Then it proposes a formal specification of DMA. Finally, it briefly presents an object oriented implementation of DMA.

关键词： Decision Map Algebra preference learning Dominance-based Rough Set Approach Spatial modelling Multicriteria modelling

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preference learning from interval pairwise data. A distance-based approach

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8th International Conference on Intelligent Data Engineering and Automated learning

作者： Dopazo, Esther Ruiz-Tagle, Mauricio Robles, Juan Tech Univ Madrid Fac Informat Campus MontegancedpCP28660 Madrid Spain Unive Austral de Chile Facu Cienc Engn Valdivia Chile

ISBN: (纸本)9783540772255

preference learning has recently received a lot of attention within the machine learning field, concretely learning by pairwise comparisons is a well-established technique in this field. We focus on the problem of learning the overall preference weights of a set of alternatives from the (possibly conflicting) uncertain and imprecise information liven by a group of experts into the form of interval pairwise comparison matrices. Because of the complexity of real world problems, incomplete information or knowledge and different patterns of the experts, interval data provide a flexible framework to account uncertainty and imprecision. In this context, we propose a two-stage method in a distance-based framework, where the impact of the data certainty degree is captured. First, it is obtained the group preference matrix that best reflects imprecise information given by the experts. Then, the crisp preference weights and the associated ranking of the alternatives are derived from the obtained group matrix. The proposed methodology is made operational by using an Interval Goal Programming formulation.

关键词： preference learning pairwise comparison matrices interval data distance methods interval goal programming

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