Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur

A constructive theory of automated ideation

In this thesis we explore the field of automated artefact generation in computational creativity with the aim of proposing methods of generation of ideas with cultural value. We focus on two kinds of ideas: fictional concepts and socially embedded concepts. For fictional concepts, we introduce a novel method based on the non-existence-conjectures made by the HR automated theory formation system. We further introduce the notion of typicality of an example with respect to a concept into HR. This leads to methods for ordering fictional concepts with respect to three measurements: novelty, vagueness and stimulation. We ran an experiment to produce thousands of definitions of fictional animals and then compared the software's evaluations of the non-fictional concepts with those obtained through a survey consulting sixty people. The results showed that two of the three measurements have a correlation with human notions.For socially embedded concepts, we apply a typicality-based classification method, the Rational Model of Classification (RMC), to a set of data obtained from Twitter. The aim being the creation of a set of concepts that naturally associate to an initial topic. We applied the RMC to four sets of tweets, each corresponding to one of four initial topics. The result was a set of clusters per topic, each cluster having a definition consisting of a set of words that appeared recurrently in the tweets. A survey was used to ask people to guess the topic given a set of definitions and to rate the artistic relevance of these definitions. The results showed both high association percentage and high relevance scores. A second survey was used to compare the rankings on the social impact of each of the definitions. The results obtained show a weak positive correlation between the two rankings. Our experiments show that it is possible to automatically generate ideas with the purpose of using them for artefact generation. This is an important step for the automation of computational creativity because most of the available artefact generation systems do not explicitly undertake idea generation. Moreover, our experiments introduce new ways of using the notion of typicality and show how these uses can be integrated in both the generation and evaluation of ideas.Open Acces

Situational interest in physical education: A function of learning task design

Situational interest is the appealing effect of unique characteristics students recognize in a learning task during interaction with the task. It occurs when a learning task gives the learner a sense of novelty and challenge, demands high attention and exploration intention, and generates instant enjoyment during the person-task interaction. In this study, a repeated measure research design was used to examine the effects of task design on situational interest and the extent to which the effects were mediated by gender, grade, personal interest, and skill levels. Middle school students (N = 242) evaluated situational interest of four learning tasks with different cognitive and physical demands after having experienced the tasks in their physical education classes. Analyzed data showed that cognitive demand of a learning task played a critical role in generating situational interest. Grade levels, gender, and personal interest mediated the effects of task design on situational interest. But these mediation effects seemed rather limited. Physical skill levels had little influence on the effects of task design on situational interest. The findings seem to suggest that to enhance interestingness of a physical activity task, an option for physical educators may be to increase cognitive demand rather than reduce physical demand

XSnippets : exploring semi-structured data via snippets

Users are usually not familiar with the content and structure of the data when they explore the data source. However, to improve the exploration usability, they need some primary hints about the data source. These hints should represent the overall picture of the data source and include the trending issues that can be extracted from the query log. In this paper, we propose a two-phase interactive exploratory search framework for the clueless users that exploits the snippets for conducting the search on the XML data. In the first phase, we present the primary snippets that are generated from the keywords of the query log to start the exploration. To retrieve the primary snippets, we develop an A* search-based technique on the keyword space of the query log. To improve the performance of computations, we store the primary snippet computations in an index data structure to reuse it for the next steps. In the second phase, we exploit the co-occurring content of the snippets to generate more specific snippets with the user interaction. To expedite the performance, we design two pruning techniques called inter-snippet and intra-snippet pruning to stop unnecessary computations. Finally, we discuss a termination condition that checks the cardinality of the snippets to stop the interactive phase and present the final Top-l snippets to the user. Our experiments on real datasets verify the effectiveness and efficiency of the proposed framework. © 2019 Elsevier B.V

Feeling the landscape: six psychological studies into landscape experience

In de zes studies van deze dissertatie zijn een aantal zowel praktische als theoretische vraagstukken met betrekking tot de beleving van landschap onderzocht. Landschapsbeleving wordt gedefinieerd als een dynamisch proces, als het resultaat van interacties tussen cultureel en biologisch bepaalde, algemene determinanten van de ervaring. In de studies wordt een aantal verschillende psychologische theoriën getoetst, en samen tonen deze het belang aan van psychologisch onderzoek naar landschapsbeleving. Het is de toepassing van methodologiën en theoretische perspectieven uit de psychologie, die het mogelijk heeft gemaakt tot de inzichten te komen over de interactie tussen mens en landschap, die het resultaat zijn van deze studie

Text mining with exploitation of user\u27s background knowledge : discovering novel association rules from text

The goal of text mining is to find interesting and non-trivial patterns or knowledge from unstructured documents. Both objective and subjective measures have been proposed in the literature to evaluate the interestingness of discovered patterns. However, objective measures alone are insufficient because such measures do not consider knowledge and interests of the users. Subjective measures require explicit input of user expectations which is difficult or even impossible to obtain in text mining environments. This study proposes a user-oriented text-mining framework and applies it to the problem of discovering novel association rules from documents. The developed system, uMining, consists of two major components: a background knowledge developer and a novel association rules miner. The background knowledge developer learns a user\u27s background knowledge by extracting keywords from documents already known to the user (background documents) and developing a concept hierarchy to organize popular keywords. The novel association rule miner discovers association rules among noun phrases extracted from relevant documents (target documents) and compares the rules with the background knowledge to predict the rule novelty to the particular user (useroriented novelty). The user-oriented novelty measure is defined as the semantic distance between the antecedent and the consequent of a rule in the background knowledge. It consists of two components: occurrence distance and connection distance. The former considers the co-occurrences of two keywords in the background documents: the more the shorter the distance. The latter considers the common connections of with others in the concept hierarchy. It is defined as the length of the connecting the two keywords in the concept hierarchy: the longer the path, distance. The user-oriented novelty measure is evaluated from two perspectives: novelty prediction accuracy and usefulness indication power. The results show that the useroriented novelty measure outperforms the WordNet novelty measure and the compared objective measures in term of predicting novel rules and identifying useful rules

Machine learning stochastic design models.

Due to the fluid nature of the early stages of the design process, it is difficult to obtain deterministic product design evaluations. This is primarily due to the flexibility of the design at this stage, namely that there can be multiple interpretations of a single design concept. However, it is important for designers to understand how these design concepts are likely to fulfil the original specification, thus enabling the designer to select or bias towards solutions with favourable outcomes. One approach is to create a stochastic model of the design domain. This paper tackles the issues of using a product database to induce a Bayesian model that represents the relationships between the design parameters and characteristics. A greedy learning algorithm is presented and illustrated using a simple case study