Data-driven Preference Learning Methods for Multiple Criteria Sorting with Temporal Criteria

The advent of predictive methodologies has catalyzed the emergence of data-driven decision support across various domains. However, developing models capable of effectively handling input time series data presents an enduring challenge. This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria. We first formulate a convex quadratic programming model characterized by fixed time discount factors, operating within a regularization framework. Additionally, we propose an ensemble learning algorithm designed to consolidate the outputs of multiple, potentially weaker, optimizers, a process executed efficiently through parallel computation. To enhance scalability and accommodate learnable time discount factors, we introduce a novel monotonic Recurrent Neural Network (mRNN). It is designed to capture the evolving dynamics of preferences over time while upholding critical properties inherent to MCS problems, including criteria monotonicity, preference independence, and the natural ordering of classes. The proposed mRNN can describe the preference dynamics by depicting marginal value functions and personalized time discount factors along with time, effectively amalgamating the interpretability of traditional MCS methods with the predictive potential offered by deep preference learning models. Comprehensive assessments of the proposed models are conducted, encompassing synthetic data scenarios and a real-case study centered on classifying valuable users within a mobile gaming app based on their historical in-app behavioral sequences. Empirical findings underscore the notable performance improvements achieved by the proposed models when compared to a spectrum of baseline methods, spanning machine learning, deep learning, and conventional multiple criteria sorting approaches

Construction and refinement of preference ordered decision classes

Preference learning methods are commonly used in multicriteria analysis. The working principle of these methods is similar to classical machine learning techniques. A common issue to both machine learning and preference learning methods is the difficulty of the definition of decision classes and the assignment of objects to these classes, especially for large datasets. This paper proposes two procedures permitting to automatize the construction of decision classes. It also proposes two simple refinement procedures, that rely on the 80-20 principle, permitting to map the output of the construction procedures into a manageable set of decision classes. The proposed construction procedures rely on the most elementary preference relation, namely dominance relation, which avoids the need for additional information or distance/(di)similarity functions, as with most of existing clustering methods. Furthermore, the simplicity of the 80-20 principle on which the refinement procedures are based, make them very adequate to large datasets. Proposed procedures are illustrated and validated using real-world datasets

A study on affect model validity : nominal vs ordinal labels

The question of representing emotion computationally remains largely unanswered: popular approaches require annotators to assign a magnitude (or a class) of some emotional dimension, while an alternative is to focus on the relationship between two or more options. Recent evidence in affective computing suggests that following a methodology of ordinal annotations and processing leads to better reliability and validity of the model. This paper compares the generality of classification methods versus preference learning methods in predicting the levels of arousal in two widely used affective datasets. Findings of this initial study further validate the hypothesis that approaching affect labels as ordinal data and building models via preference learning yields models of better validity.peer-reviewe

Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division

Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division. On the other end, we ask them to report their levels of competence, autonomy, relatedness and presence using the Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods based on support vector machines to infer the mapping between gameplay and the reported four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the best obtained models reach accuracies of near certainty, from 92% up to 94% on unseen players.Comment: Version accepted for IEEE Conference on Games, 201

Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores

Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of large amount of interactive feedback. This paper presents a new method that uses scores provided by humans, instead of pairwise preferences, to improve the feedback efficiency of interactive reinforcement learning. Our key insight is that scores can yield significantly more data than pairwise preferences. Specifically, we require a teacher to interactively score the full trajectories of an agent to train a behavioral policy in a sparse reward environment. To avoid unstable scores given by human negatively impact the training process, we propose an adaptive learning scheme. This enables the learning paradigm to be insensitive to imperfect or unreliable scores. We extensively evaluate our method on robotic locomotion and manipulation tasks. The results show that the proposed method can efficiently learn near-optimal policies by adaptive learning from scores, while requiring less feedback compared to pairwise preference learning methods. The source codes are publicly available at https://github.com/SSKKai/Interactive-Scoring-IRL.Comment: Accepted by IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

بررسی رابطه‌ی سبک های تفکر و شیوه های یادگیری دانشجویان دانشگاه علوم پزشکی شیراز ،1390

زمینه و هدف: یادگیری از مهم‌ترین زمینه های مطرح در علوم روانشناسی و تعاریف آن از دشوارترین مفاهیم است. پژوهش حاضر در پی بررسی رابطه‌ی سبک های تفکر و شیوه های یادگیری ترجیحی دانشجویان دانشکده‌ی مدیریت و اطلاع رسانی دانشگاه علوم پزشکی شیراز مورد بررسی انجام گرفت. روش بررسی: پژوهش حاضر از نوع توصیفی-تحلیلی که به‌صورت مقطعی در سال 1390انجام گرفت . جامعه‌ی پژوهش دانشجویان سال اول و دوم دانشکده‌ی مدیریت و اطلاع رسانی دانشگاه علوم پزشکی شیراز بود . حجم نمونه با روش سرشماری 113 نفر تعیین شد. داده ها با استفاده از دو پرسشنامه‌ی شیوه های یادگیری استاندارد وارک و پرسشنامه‌ی 32 گویه از پرسشنامه جی رابرت استرنبرگ به منظور تشخیص نوع تفکر دانشجویان استفاده گردید. داده ها با استفاده از آمارهای توصیفی ، فراوانی مطلق ،میانگین وآزمون همبستگی پیرسون و تی دو دامنه تحلیل شد . یافته ها: بین سبک های تفکر و شیوه های یادگیری ترجیحی دانشجویان رابطه‌ی مستقیم وجود داشت.یافته ها نشان داد که بین شیوه های یادگیری و سبک های یادگیری در برخی ابعاد دارای تفاوت معناداری بود (05/0p≤).سبک قضاوتگر با شیوه‌ی خواندنی و نوشتنی رابطه‌ی معنادار داشت (05/0p≤) و با سایر شیوه های یادگیری رابطه‌ی مستقیم داشت. سبک های قانون گذار-اجرایی ، اجرایی-قانون گذار و کلی نگر-جزیی نگر با تمامی شیوه ها رابطه‌ی مستقیم داشت .یافته ها حاکی از وجود رابطه‌ی معکوس بین سبک جزیی نگر-کلی نگر با شیوه‌ی دیداری وجود داشت و سبک محافظه کارآزاد اندیش تنها با شیوه‌ی دیداری رابطه‌ی مستقیم داشت. نتیجه گیری: به‌طورکلی بین سبک های تفکر و شیوه های یادگیری ترجیحی در بسیاری از ابعاد رابطه‌ی مستقیم وجود داشت . به منظور بالا بردن سطح علمی و کیفیت آموزشی باید اساتید برنامه ریزی های آموزشی خود را به سمت استفاده از پیشنهادات و انتقادات دانشجویان سوق دهند. این مطالعه و مطالعات مشابه می‌تواند به‌عنوان یک نقطه عطف در تحول شیوه های تدریس در سیستم آموزشی حاضر باشد