From Somewhere to Nowhere and Back Again: Emplaced abstraction in science communication

Science and environmental communication often relies on place-based narrative elements to explore relationships between particularity and abstraction. By combining Hayakawa’s abstraction ladder with Sack\u27s relational geographic framework, a useful tool emerges for identifying narrative dimensions for creating compelling place-based nonfiction. This tool may be particularly useful in science communication teaching and learning. Hayakawa’s ladder of abstraction extends from particularity low on the ladder to higher-order abstractions up top. Sack\u27s relational geographic framework explores the role of place in creating knowledge, stretching from a focal point of emplaced ontological forces – materiality, meaning, and social relations – through increasingly abstract knowledge and value dimensions

Object-Oriented Dynamics Learning through Multi-Level Abstraction

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial Intelligence (AAAI), 202

Analogy Mining for Specific Design Needs

Finding analogical inspirations in distant domains is a powerful way of solving problems. However, as the number of inspirations that could be matched and the dimensions on which that matching could occur grow, it becomes challenging for designers to find inspirations relevant to their needs. Furthermore, designers are often interested in exploring specific aspects of a product-- for example, one designer might be interested in improving the brewing capability of an outdoor coffee maker, while another might wish to optimize for portability. In this paper we introduce a novel system for targeting analogical search for specific needs. Specifically, we contribute a novel analogical search engine for expressing and abstracting specific design needs that returns more distant yet relevant inspirations than alternate approaches