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

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain

The abilities to form and abstract concepts is key to human intelligence, but such abilities remain lacking in state-of-the-art AI systems. There has been substantial research on conceptual abstraction in AI, particularly using idealized domains such as Raven's Progressive Matrices and Bongard problems, but even when AI systems succeed on such problems, the systems are rarely evaluated in depth to see if they have actually grasped the concepts they are meant to capture. In this paper we describe an in-depth evaluation benchmark for the Abstraction and Reasoning Corpus (ARC), a collection of few-shot abstraction and analogy problems developed by Chollet [2019]. In particular, we describe ConceptARC, a new, publicly available benchmark in the ARC domain that systematically assesses abstraction and generalization abilities on a number of basic spatial and semantic concepts. ConceptARC differs from the original ARC dataset in that it is specifically organized around "concept groups" -- sets of problems that focus on specific concepts and that are vary in complexity and level of abstraction. We report results on testing humans on this benchmark as well as three machine solvers: the top two programs from a 2021 ARC competition and OpenAI's GPT-4. Our results show that humans substantially outperform the machine solvers on this benchmark, showing abilities to abstract and generalize concepts that are not yet captured by AI systems. We believe that this benchmark will spur improvements in the development of AI systems for conceptual abstraction and in the effective evaluation of such systems

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise

Many modern machine learning approaches require vast amounts of training data to learn new concepts; conversely, human learning often requires few examples--sometimes only one--from which the learner can abstract structural concepts. We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. The agent extracts spatial relations from a sparse set of noisy examples of block-based structures, and trains convolutional and sequential models of those relation sets. To create novel examples of similar structures, the agent begins placing blocks on a virtual table, uses a CNN to predict the most similar complete example structure after each placement, an LSTM to predict the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. We verify that the agent learned the concept by observing its virtual block-building activities, wherein it ranks each potential subsequent action toward building its learned concept. We empirically assess this approach with human participants' ratings of the block structures. Initial results and qualitative evaluations of structures generated by the trained agent show where it has generalized concepts from the training data, which heuristics perform best within the search space, and how we might improve learning and execution

Editorial: Welcome to the Journal of STEM Arts, Crafts, and Constructions

The Journal of STEM Arts, Crafts, and Constructions is a scholarly journal that seeks to engage professionals, including preK-12 teachers, in a conversation about the benefits of arts integration; the ways that the STEM subjects can be integrated with the arts to produce effective teaching (STEAM Education); and how the Next Generation Science Standards (NGSS), can be effectively implemented with integrated arts, crafts, or constructions. Manuscripts, including guest editorials, are blind peer-reviewed by usually two reviewers and an associate editor or by three reviewers. This editorial explains the Journal’s origin in a faculty professional learning community. The Journal has a national reach with plans for two issues each year. The editorial discusses what the Journal is looking for in manuscript submissions, how the Journal may be of use to readers, and highlights of the articles in this issue. Finally, the editor explains the 5E’s learning cycle lesson model, which is an effective format for inquiry lessons to readers who may be interested in incorporating this format into lessons and future manuscripts

Categorical Change: Exploring the Effects of Concept Drift in Human Perceptual Category Learning

Categorization is an essential survival skill that we engage in daily. A multitude of behavioral and neuropsychological evidence support the existence of multiple learning systems involved in category learning. COmpetition between Verbal and Implicit Systems (COVIS) theory provides a neuropsychological basis for the existence of an explicit and implicit learning system involved in the learning of category rules. COVIS provides a convincing account of asymptotic performance in human category learning. However, COVIS – and virtually all current theories of category learning – focus solely on categories and decision environments that remain stationary over time. However, our environment is dynamic, and we often need to adapt our decision making to account for environmental or categorical changes. Machine learning addresses this significant challenge through what is termed concept drift. Concept drift occurs any time a data distribution changes over time. This dissertation draws from two key characteristics of concept drift in machine learning known to impact the performance of learning models, and in-so-doing provides the first systematic exploration of concept drift (i.e., categorical change) in human perceptual category learning. Four experiments, each including one key change parameter (category base-rates, payoffs, or category structure [RB/II]), investigated the effect of rate of change (abrupt, gradual) and awareness of change (foretold or not) on decision criterion adaptation. Critically, Experiments 3 and 4 evaluated differences in categorical adaptation within explicit and implicit category learning tasks to determine if rate and awareness of change moderated any learning system differences. The results of these experiments inform current category learning theory and provide information for machine learning models of decision support in non-stationary environments