Preference Models for Creative Artifacts and Systems

Abstract Although there is vigorous debate around definitions of creativity, there is general consensus that creativity i) has multiple facets, and ii) inherently involves a subjective value judgment by an evaluator. In this paper, we present evaluation of creative artifacts and computational creativity systems through a multiattribute preference modeling lens. Specifically, we introduce the use of multiattribute value functions for creativity evaluation and argue that there are significant benefits to explicitly representing creativity judgments as subjective preferences using formal mathematical models. Various implications are illustrated with the help of examples from and inspired by the creativity literature

Event Prediction using Case-Based Reasoning over Knowledge Graphs

Applying link prediction (LP) methods over knowledge graphs (KG) for tasks such as causal event prediction presents an exciting opportunity. However, typical LP models are ill-suited for this task as they are incapable of performing inductive link prediction for new, unseen event entities and they require retraining as knowledge is added or changed in the underlying KG. We introduce a case-based reasoning model, EvCBR, to predict properties about new consequent events based on similar cause-effect events present in the KG. EvCBR uses statistical measures to identify similar events and performs path-based predictions, requiring no training step. To generalize our methods beyond the domain of event prediction, we frame our task as a 2-hop LP task, where the first hop is a causal relation connecting a cause event to a new effect event and the second hop is a property about the new event which we wish to predict. The effectiveness of our method is demonstrated using a novel dataset of newsworthy events with causal relations curated from Wikidata, where EvCBR outperforms baselines including translational-distance-based, GNN-based, and rule-based LP models.Comment: published at WWW '23: Proceedings of the ACM Web Conference 2023. Code base: https://github.com/solashirai/WWW-EvCB

How can economic schemes curtail the increasing sex ratio at birth in China?

Fertility decline, driven by the one-child policy, and son preference have contributed to an alarming difference in the number of live male and female births in China. We present a quantitative model where people choose to sex-select because they perceive that married sons are more valuable than married daughters. Due to the predominant patrilocal kinship system in China, daughters-in-law provide valuable emotional and financial support, enhancing the perceived present value of married sons. We argue that inter-generational transfer data will help ascertain the extent to which economic schemes (such as pension plans for families with no sons) can curtail the increasing sex ratio at birth.sex ratio at birth, sex-selection, sex-selective potency, son preference, value of child

A Multi-Channel Neural Graphical Event Model with Negative Evidence

Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using some underlying parametric form to capture historical dependencies, or on non-parametric models that focus primarily on tasks such as prediction. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. We use a novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval. We evaluate our method against state-of-the-art baselines on model fitting tasks as gauged by log-likelihood. Through experiments on both synthetic and real-world datasets, we find that our proposed approach outperforms existing baselines on most of the datasets studied.Comment: AAAI 202