The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu

Building Student’s Mathematical Connection Ability in Abstract Algebra: The Combination of Analogy-Contruction-Abstraction Stages

The objective of the study was to describe the effect of six types of mathematical connections (representation connections, structural connections, procedural connections, implication connections, generalization connections, and hierarchy connections) on abstract algebraic materials through four stages, i.e., abstraction, analogy-abstraction, construction-analogy, and construction. The study employed qualitative descriptive approaches, including tests, questionnaires, and interviews. The subjects of the study were chosen based on the responses to a questionnaire regarding the employed stages. Then, two subjects who could converse and were willing to be interviewed were chosen from each stage. Data collection techniques were conducted through four stages, i.e., 1) identifying the stages used; 2) identifying the ability of six types of student mathematical connections through predictive indicators; 3) describing the capabilities of the six types of connections through interviews; and 4) conducting source triangulation and method triangulation. The results indicated that the subjects who utilized the construction stage tended to be able to construct six types of mathematical connection links in a set, as well as standard and non-standard binary operations. The subjects who utilized the construction-analogy stage likely to be able to build three forms of representation connections, structural connections, and procedural connections in a set of standard binary operations. In characterizing the symbol of a set element and the binary operation of the standard form inside the closed property of the standard form, the subjects who used the analogy-abstraction stage have the same tendency as subjects who use the abstraction-construction stage

Computer modeling of human decision making

Models of human decision making are reviewed. Models which treat just the cognitive aspects of human behavior are included as well as models which include motivation. Both models which have associated computer programs, and those that do not, are considered. Since flow diagrams, that assist in constructing computer simulation of such models, were not generally available, such diagrams were constructed and are presented. The result provides a rich source of information, which can aid in construction of more realistic future simulations of human decision making

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

Constructivism, epistemology and information processing

The author analyzes the main models of artificial intelligence which deal with the transition from one stage to another, a central problem in development. He describes the contributions of rule-based systems and connectionist systems to an explanation of this transition. He considers that Artificial Intelligence models, in spite of their limitations, establish fruitful points of contact with the constructivist position.El autor analiza los principales modelos de inteligencia artificial que dan cuenta del paso de la transición de un estudio a otro, problema central del desarrollo. Describe y señala las aportaciones de los sistemas basados en reglas así como de los sistemas conexionistas para explicar dicha transición. Considera que los modelos de inteligencia artificial, a pesar de sus limitaciones, permiten establecer puntos de contacto muy fructiferos con la posición constructivista

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Integrating Analogical Similarity and Error-Driven Learning For Relational Concept Acquisition

How can people and machines learn new relational concepts, such as the concept of &lsquo;fork&rsquo; in chess, or the grammar of sentences? The approach taken in this work is to develop a theoretical and computational framework for how such concepts are learned and applied. The framework integrates established principles of cognition (analogy and error-driven learning) and explores their computational power and empirical validity. The first chapter of this thesis presents computational models and simulation results in the domain of two-player adversarial games. These models demonstrate how a synthesis of analogy and reinforcement learning (RL) provides a framework for constructing abstract relational concepts and evaluating their usefulness. The second chapter describes experiments with humans that qualitatively test the model predictions. These experiments demonstrate how reward feedback and frequency affect the reinforcement of relational concepts. The third chapter explores how the framework can be extended to model grammar learning in language processing. This model extension demonstrates the viability of tensor-based representations (HRRs) as the interface between an analogical meaning system and a recurrent neural network (RNN) sequencing system in the domain of sentence production. Together, these models offer solutions to the now three-decade challenge of unifying the flexibility and fluidity of deep learning with the expressive power of compositional representations.</p