Qualitative Geometric Reasoning

Are students' explanations of motion generated by an underlying structure? We address this question by exploring striking parallels between intuitive explanations and those offered by medieval scholastics. Using the historical record, it is possible to reconstructan inferential structure that generates medieval explanations. W e posit a parallel structure for intuitive explanations

Physical Object Representation and Generalization: A Survey of Natural Language Processing Programs

This paper surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding. Five programs serve as case studies for guiding the course of the paper. Within the framework of describing each of these programs, seven other programs, ideas and theories that are relevant to the program in focus are presented. Our current work which integrates representation and generalization is also discussed

Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge

Differences in the categorization of physics problems by novices and experts

This study investigates categorization of physics problems. Expectations were that novices use surface structures (explicitly-stated features in the text of physics problems) and experts use deep structures (physics principles that determine and control solutions to physics problems) in the formation of representations;The perspective is obtained from information-processing theory: The representations are viewed as organized knowledge structures within short-term memory, constructed by problem solvers, that describe the environment. Problems are solved by operations on such descriptions. The knowledge within long-term memory used in the formation of a problem representation is accessed when a problem solver categorizes a problem. Choosing a problem category, i.e., the categorization process occurring in short-term memory, allows for the inference of structures that exist in the domain-dependent knowledge base in long-term memory;One of four sets of physics problems was sorted and one physics problem was solved by each of 94 novices (first-year physics students), five intermediates (students who had completed an advanced undergraduate physics course) and 20 experts (professors);Cluster analysis shows that (a) experts categorize according to deep structures and novices use surface features and deep structures in the categorization process and (b) the categorization by novices is less consistent than the categorization by experts. Differences in expert-like behavior in the sorting and solving tasks were found to exist among the novices. The analysis of variance, on the alpha = .05 level, does not show these differences to be related to the ACT science score, the final grade in Physics 221, and the high school class rank. However, expert-like behavior correlates with the final grade in Physics 221 at the significance level of 0.0338;The inclusion of greater numbers of subjects than is customary in this kind of research contributes toward a greater degree of generalization. The use of dendograms and the variable that measures the degree of expert-like behavior allow for better reproducibility

Reasoning-Driven Question-Answering For Natural Language Understanding

Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems. In the final part, we present the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field

Case studies of cycles in developing a physics lesson.

Children\u27s reasoning and learning about levers and simple machines were investigated in this study. The study included several cycles of design, test and clinical interview tutoring sessions and the two final cycles are presented here. The methodology combined the use of qualitative clinical interviewing data and quantitative summative data: quantitative evaluations provided an overview of the lessons\u27 effects, while qualitative, formative lesson evaluations allowed deeper insights into learning and reasoning processes. Three groups of participants were interviewed about the pretest, lesson and posttest. The pre- and posttests were standardized, and several new and widespread misconceptions about levers have been discovered that are less accurate or general than conventional conceptions. In experiment 1 the pre-posttest comparison between the control group and experimental group 1 showed that there were no differences and the instruction in experiment 2 was revised considerably as a result of the formative evaluation findings. Significant improvements were apparent for experimental group 2 with regard to conceptual change and for transfer when compared with experimental group 1--evident in group 2 students\u27 ability to transfer their acquired knowledge to complex and compound levers and in conceptual changes apparent in simple levers questions. Lesson 1 was essentially a bridging lesson where intuitive anchoring examples were extended analogically via intermediate bridging cases to a target situation. The findings from lesson 1 suggested that reasoning from extreme case situations of levers might be instructionally useful, and this hypothesis was confirmed by results from experiment 2, where the instructional sequences based on extreme case reasoning proved to be powerful facilitators of the construction of mechanistic models by the students that fostered conceptual change and learning. The following directions for further research are suggested: students\u27 conceptual models have implications for teaching and learning that are poorly understood at this stage, and research on instruction that employs experts\u27 non-formal reasoning strategies should be encouraged

Artificial Intelligence and Human Error Prevention: A Computer Aided Decision Making Approach: Technical Report No. 4: Survey and Analysis of Research on Learning Systems from Artificial Intelligence

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryU.S. Department of Transportation / DOT FA79WA-4360 ABFederal Aviation Administratio

Uso de garantías empíricas o teóricas por estudiantes en resolución de problemas mediante argumentación dialógica

RESUMEN: Esta investigación informa el uso de garantías empíricas o teóricas en la resolución de tres problemas que se plantean a estudiantes del CLEI VI (grado undécimo). El primer problema se relaciona con la durabilidad de un lapicero, el segundo con una barbería y el tercero con el ahorro e inversión. Estos problemas surgen de intereses comunicados por estudiantes en una entrevista semiestructurada, y el proceso de resolución de los mismos se desarrolla mediante preguntas orientadoras y anidadas que conllevan a argumentación dialógica. El marco conceptual utiliza teorías de Pólya (1945), Schoenfeld (1992) y la argumentación dialógica desde los planteamientos de Nardi, Biza y Zachariades (2011). El análisis de datos se realiza mediante un paradigma cualitativo bajo un enfoque hermenéutico, y considera componentes de un argumento según Toulmin (2007). Por último, las principales conclusiones son: las garantías empíricas y teóricas se usan en etapas de resolución de problemas (Pólya, 1945), y se encuentra una estrecha relación entre preguntas tanto orientadoras como anidadas propuestas por el profesor y tipo de garantías que utiliza el estudiante para justificar la solución del problema.ABSTRACT: This research reports on the use of empirical or theoretical warrants to resolve three problems presented to students of CLEI VI (eleventh grade). The first one is related to the durability of a pen, the second problem relates to a barber shop and the third is related to savings and investment. These problems arise from interests communicated by students through an interview, and their solution is developed through guiding and nested questions, which lead the students to dialogic argumentation. Conceptual framework uses theories by Pólya (1945) and Schoenfeld (1992). Data analysis is carried out through a qualitative paradigm from a hermeneutical approach, considering components of an argument according to Toulmin (2007). Finally, the main conclusions are: empirical and theoretical warrants are used in problem-solving stages (Pólya, 1945) and, there is a close relationship between guiding and nested questions proposed by the teacher and type of warrants used by students to argue the problem solving

Automatic Qualitative Modeling of Dynamic Physical Systems

This report describes MM, a computer program that can model a variety of mechanical and fluid systems. Given a system's structure and qualitative behavior, MM searches for models using an energy-based modeling framework. MM uses general facts about physical systems to relate behavioral and model properties. These facts enable a more focussed search for models than would be obtained by mere comparison of desired and predicted behaviors. When these facts do not apply, MM uses behavior-constrained qualitative simulation to verify candidate models efficiently. MM can also design experiments to distinguish among multiple candidate models