A Cognitive Systems Framework for Creative Problem Solving

This thesis provides a theoretical framework for a wide variety of types of cognitively-inspired creative problem solving. The framework (CreaCogs) is formalized and its various creative processes detailed. The framework is put to the test in a few computational implementations: a solver to the Remote Associates Test - comRAT-C, its adaptation to the visual domain - comRAT-V, and an object replacement and object composition system in a household domain - OROC. The performance and process of these implementations are then (i) compared to human answers and performance in creativity tests or (ii) assessed with the same toolkit that would be used to assess human answers. A set of practical insight problems with objects are given to human participants in a think aloud protocol, which is then encoded and compared to the framework. The experiments and data analysis show that the framework is successful in computationally modeling creative problem solving across a wide variety of tasks

A Visual Remote Associates Test and Its Validation

The Remote Associates Test (RAT) is a widely used test for measuring creativity, specifically the ability to make associations. The Remote Associates Test normally takes a linguistic form: given three words, the participant is asked to come up with a fourth word associated with all three of them. While visual creativity tests do exist, no creativity test to date can be given in both a visual and linguistic form. Such a test would allow the study of differences between various modalities, in the context of the same creative process. In this paper, a visual version of the well-known Remote Associates Test is constructed. This visual RAT is validated in relation to its linguistic counterpart

An overview of the Remote Associates Test in different languages

The Remote Associates Test (RAT, CRA) is a classical creativity test used to measure creativity as a function of associative ability. The RAT has been administered in different languages. Nonetheless, because of how embedded in the language the test is, only a few items are directly translatable, and most of the time the RAT is created anew in each language. This process of manual (and in two cases computational) creation of RAT items is guided by the researchers’ understanding of the task. However, are the RAT items in different languages comparable? In this paper, different RAT stimuli datasets are analyzed qualitatively and quantitatively. Significant differences are observed between certain datasets in terms of solver performance. The potential sources of these differences are discussed, together with what this means for creativity psychometrics and computational vs. manual creation of stimuli

Creativity as an information-based process

Abstract: Creativity, mostly ignored in Western philosophy due to its supposed mysteriousness, has recently become a respected research topic in psychology, neuroscience, and artificial intelligence. We discuss how in science the approach has mainly been to describe creativity as an information-based process, coherently with a computational view of the human mind started with the cognitive revolution. This view has produced progressively convincing models of creativity, up to current artificial neural network systems, vaguely inspired by biological neural processing, but already competing with human creativity in several fields. These successes suggest that creativity might not be an exclusively human function, but actually a way of functioning of any natural or artificial system implementing the creative process. We conclude by acknowledging that the information-based view of creativity has tremendous explanatory and generative power, but we propose a thought experiment to start discussing how it actually leaves out the experiential side of being creative.Keywords:  Creative Cognition; Cognitive Neuroscience; Computational Creativity; Generative Algorithms; Cognitive Science La creatività come processo basato sull’informazioneRiassunto: La creatività, spesso ignorata dalla filosofia occidentale per la sua presunta oscurità, in tempi recenti è diventata un rispettabile oggetto di ricerca per la psicologia, la neuroscienza e l’intelligenza artificiale. Vogliamo illustrare il modo in cui lo sguardo scientifico sia rivolto prevalentemente a considerare la creatività come processo information-based, coerentemente con la prospettiva computazionale sulla mente umana aperta dalla rivoluzione cognitiva. Questa prospettiva ha prodotto modelli della creatività sempre più convincenti, fino agli attuali sistemi di reti neurali artificiali, vagamente inspirati al processamento biologico neurale, ma già competitivi rispetto alla creatività umana in molti ambiti. Questi successi suggeriscono che la creatività possa non essere una funzione esclusivamente umana ma in effetti un modo di funzionare di un sistema naturale o artificiale capace di implementare il processo creativo. In conclusione, pur riconoscendo come il considerare la creatività come processo information-based possieda grande potere esplicativo e generativo, proporremo un esperimento mentale per aprire una discussione sul come questa prospettiva non copra in effetti il lato esperienziale dell’essere creativo.Parole chiave: Cognizione creativa; Neuroscienza cognitiva; Creatività computazionale; Algoritmi generativi; Scienza cognitivaAbstract: Creativity, mostly ignored in Western philosophy due to its supposed mysteriousness, has recently become a respected research topic in psychology, neuroscience, and artificial intelligence. We discuss how in science the approach has mainly been to describe creativity as an information-based process, coherently with a computational view of the human mind started with the cognitive revolution. This view has produced progressively convincing models of creativity, up to current artificial neural network systems, vaguely inspired by biological neural processing, but already competing with human creativity in several fields. These successes suggest that creativity might not be an exclusively human function, but actually a way of functioning of any natural or artificial system implementing the creative process. We conclude by acknowledging that the information-based view of creativity has tremendous explanatory and generative power, but we propose a thought experiment to start discussing how it actually leaves out the experiential side of being creative.Keywords:  Creative Cognition; Cognitive Neuroscience; Computational Creativity; Generative Algorithms; Cognitive Science La creatività come processo basato sull’informazioneRiassunto: La creatività, spesso ignorata dalla filosofia occidentale per la sua presunta oscurità, in tempi recenti è diventata un rispettabile oggetto di ricerca per la psicologia, la neuroscienza e l’intelligenza artificiale. Vogliamo illustrare il modo in cui lo sguardo scientifico sia rivolto prevalentemente a considerare la creatività come processo information-based, coerentemente con la prospettiva computazionale sulla mente umana aperta dalla rivoluzione cognitiva. Questa prospettiva ha prodotto modelli della creatività sempre più convincenti, fino agli attuali sistemi di reti neurali artificiali, vagamente inspirati al processamento biologico neurale, ma già competitivi rispetto alla creatività umana in molti ambiti. Questi successi suggeriscono che la creatività possa non essere una funzione esclusivamente umana ma in effetti un modo di funzionare di un sistema naturale o artificiale capace di implementare il processo creativo. In conclusione, pur riconoscendo come il considerare la creatività come processo information-based possieda grande potere esplicativo e generativo, proporremo un esperimento mentale per aprire una discussione sul come questa prospettiva non copra in effetti il lato esperienziale dell’essere creativo.Parole chiave: Cognizione creativa; Neuroscienza cognitiva; Creatività computazionale; Algoritmi generativi; Scienza cognitiv

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc