A Cognitive Systems Framework for Creative Problem Solving

Universität Bremen FB 3 Informatik Doctor of Natural Sciences A Cognitive Systems Framework for Creative Problem Solving by Ana-Maria Olteţeanu Creativity, problem solving and insight have been studied through different methods by cognitive science, artificial intelligence and the new field of computational creativity. This thesis proposes to close this interdisciplinary gap by addressing the topic of creative problem solving by cognitive systems. A cognitive theoretical framework (CreaCogs) for types of knowledge organization which can support creative processes is proposed and formalized, as to account for a wide set of creative processes in a unified manner. Processes which use cognitive principles of similarity, association, structured representation, re-representation and restructuring are supported by the type of knowledge organization proposed in CreaCogs. Creative search and substitution mechanisms, together with creative construction mechanisms are described in CreaCogs, in the context of various creativity tasks. Different methods have generally been used in the literature for the evaluation of human versus the evaluation of computational creativity. In the context of this framework, the various prototype systems implemented are evaluated either in comparison to normative data from humans performing creativity tests, or analyzed with the same tools as human answers would be analyzed across these tasks. Various prototype systems are implemented to showcase the use of the CreaCogs framework. One such system is comRAT, which computationally solves a human creativity test the Remote Associates Test. This comRAT-C system uses language data from the Corpus of Contemporary American English which is organized and searched with associative convergence principles posited in the CreaCogs framework. comRAT-C is evaluated using normative data from human participants solving the compound Remote Associates Test. The performance of comRAT-C can be successfully compared to human solver counterparts: the system gives both plausible and correct answers, and its ability to find an answer correlates with human difficulty when solving the task. As no creative test which can be given in different modalities exists up to date, the formalization of the Remote Associates Test made here is used to develop a visual variant for the test. Human participants are subsequently given this task to solve; they also provide visual associates for the cognitive knowledge acquisition of comRAT-V, a system which solves the visual variant of the Remote Associates Test successfully. Early data analysis shows that the correlation between the system’s ability to solve these queries and human performance holds in the visual variant as well, showing promise for future cross-modal explorations of the Remote Associates test from both the computational and empirical perspective. Another prototype cognitive system for object replacement and object composition (OROC) showcases another side of CreaCogs principles. Cognitive knowledge retrieval and encoding are discussed in an object domain, and multi-feature similarity is explored. Various results of object replacement and object composition queries are described, together with the types of queries which can be answered by OROC using a small set of CreaCogs principles. OROC’s object replacement abilities are then used to answer a human creativity test the Alternative Uses Test. OROC’s performance is evaluated with the same tools as human participants, including an evaluation of the Novelty, Likability and Usability of its alternative uses made by human participants. Think aloud data and human answers to the Alternative Uses test show that OROC matches many of the cognitive processes used by humans in such a task. A set of classical and new practical object insight problems are described and given to human participants in a think aloud protocol. The processes used by human participants in such problems are encoded and shown to correspond to processes posited in the CreaCogs framework. This work prepares the future modeling and computational solving of such problems, and strategies for creating more of them as to further explore practical insight in object affordance domains. Together, these experiments and data analyses show that the creative processes and framework proposed here can account for a description of creative problem solving across a variety of tasks. Furthermore, the prototype systems implemented here show promise in generating creative tests of their own, which could be further used in the empirical exploration of creative processes. This work generally argues for adapting the definition of problem solving to allow for the processes required for creative problem solving. Classical problem solving is shown to be a subset of problem solving initiated after processes of interpretation and representation of the problem have already been deployed. The approach taken here, of seeking comparability of artificial cognitive systems and their products to human solving of creativity tests is shown to be productive. This approach opens computational creative systems to improvement via cognitive influence, and turns such systems into useful adaptable tools for studying the human creative process from a computational perspective. Both the goals of artificial intelligence and cognitive psychology are thus served by this type of approach, in the interdisciplinary spirit of cognitive science.