Proposal For a Study of Commonsense Physical Reasoning

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-80-C-0505.Our common sense views of physics are the first coin in our intellectual capital; understanding precisely what they contain could be very important both for understanding ourselves and for making machines more like us. This proposal describes a domain that has been designed for studying reasoning about constrained motion and describes my theories about performing such reasoning. The issues examined include qualitative reasoning about shape and physical processes, as well as ways of using knowledge about motion other than "envisioning". Being a proposal, the treatment of these issues is necessarily cursory and incomplete.MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agenc

New Methodologies for Examining and Supporting Student Reasoning in Physics

Learning how to reason productively is an essential goal of an undergraduate education in any STEM-related discipline. Many non-physics STEM majors are required to take introductory physics as part of their undergraduate programs. While certain physics concepts and principles may be of use to these students in their future academic careers and beyond, many will not. Rather, it is often expected that the most valuable and longlasting learning outcomes from a physics course will be a repertoire of problem-solving strategies, a familiarity with mathematizing real-world situations, and the development of a strong set of qualitative inferential reasoning skills. For more than 40 years, the physics education research community has produced many research-based instructional materials that have been shown to improve student conceptual understanding and other targeted learning outcomes (e.g., problem solving). It is often tacitly assumed that such materials also improve students’ qualitative reasoning skills, but there is no documented evidence of this, to date, in the literature. Furthermore, a growing body of research has revealed that a focus on conceptual understanding does not always result in the anticipated performance outcomes. Indeed, students may demonstrate solid conceptual understanding on one physics question but fail to demonstrate that same understanding on a closely related question. This body of research suggests that reasoning processes general to all humans (i.e., domain-general processes) may impact how students understand and reason with physics concepts. Methodologies that separate (to the degree possible) the reasoning involved in a physics problem from the conceptual understanding necessary to correctly answer that problem are necessary for gaining insight into how conceptual understanding and domain-general reasoning processes interact. In order to explore such research questions, new research tools and analysis methodologies are required. Physics education researchers pursuing these questions have begun to embrace data-collection methodologies outside of the written free-response questions and think-aloud interviews that are ubiquitous in discipline-based education research. Some of these researchers have also begun to utilize dual-process theories of reasoning (DPToR) as an analysis framework. Dual-process theories arise from findings in cognitive science, social psychology, and the psychology of reasoning. These theories tend to be mechanistic in nature; as such, they provide a framework that can be prescriptive rather than solely descriptive, thereby providing a theoretical basis for examining the interplay of domain-general and domain-specific reasoning. In the work described in this thesis, we sought to gain greater insight into the nature of student reasoning in physics and the extent to which it is impacted by the domain-general phenomena explored by cognitive science. This was accomplished by developing and implementing new methodologies to examine qualitative inferential reasoning that separate reasoning skills from understanding of a particular physics concept. In this work we present two such methodologies: reasoning chain construction tasks, in which students are provided with correct reasoning elements (i.e., true statements about the physical situation as well as correct concepts and mathematical relationships) and are asked to assemble them into an argument in order to answer a physics question; and possibility exploration tasks, which are designed to measure student ability to consider multiple possibilities when answering a physics problem. The overarching goal of these novel tasks is to explore mechanistic processes related to the generation of qualitative inferential reasoning chains and to uncover insight into the nature of student reasoning more generally. The work reported in this dissertation has yielded a variety of important results. In concert with reasoning-chain construction tasks, the dual-process framework has been leveraged to provide testable hypotheses about student reasoning and to inform the design of an instructional intervention to support student reasoning. By applying network analysis approaches to data produced by reasoning chain construction tasks with network analysis, insights were uncovered regarding the structure of student reasoning in different contexts, and the development of a coherent reasoning structure over the course of a two-semester physics course was documented. Finally, students’ tendency to explore possibilities has been, both in the literature and in this dissertation, found to impact performance on physics questions. This tendency is examined and a possible mechanism controlling this tendency has been proposed. Taken together, these investigations and findings constitute substantive advances in how student reasoning is studied and serve to open new doors for future research

Spatial Aggregation: Theory and Applications

Visual thinking plays an important role in scientific reasoning. Based on the research in automating diverse reasoning tasks about dynamical systems, nonlinear controllers, kinematic mechanisms, and fluid motion, we have identified a style of visual thinking, imagistic reasoning. Imagistic reasoning organizes computations around image-like, analogue representations so that perceptual and symbolic operations can be brought to bear to infer structure and behavior. Programs incorporating imagistic reasoning have been shown to perform at an expert level in domains that defy current analytic or numerical methods. We have developed a computational paradigm, spatial aggregation, to unify the description of a class of imagistic problem solvers. A program written in this paradigm has the following properties. It takes a continuous field and optional objective functions as input, and produces high-level descriptions of structure, behavior, or control actions. It computes a multi-layer of intermediate representations, called spatial aggregates, by forming equivalence classes and adjacency relations. It employs a small set of generic operators such as aggregation, classification, and localization to perform bidirectional mapping between the information-rich field and successively more abstract spatial aggregates. It uses a data structure, the neighborhood graph, as a common interface to modularize computations. To illustrate our theory, we describe the computational structure of three implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the spatial aggregation generic operators by mixing and matching a library of commonly used routines.Comment: See http://www.jair.org/ for any accompanying file

The mediating effect of task presentation on collaboration and children's acquisition of scientific reasoning

There has been considerable research concerning peer interaction and the acquisition of children's scientific reasoning. This study investigated differences in collaborative activity between pairs of children working around a computer with pairs of children working with physical apparatus and related any differences to the development of children's scientific reasoning. Children aged between 9 and 10 years old (48 boys and 48 girls) were placed into either same ability or mixed ability pairs according to their individual, pre-test performance on a scientific reasoning task. These pairs then worked on either a computer version or a physical version of Inhelder and Piaget's (1958) chemical combination task. Type of presentation was found to mediate the nature and type of collaborative activity. The mixed-ability pairs working around the computer talked proportionally more about the task and management of the task; had proportionally more transactive discussions and used the record more productively than children working with the physical apparatus. Type of presentation was also found to mediated children's learning. Children in same ability pairs who worked with the physical apparatus improved significantly more than same ability pairs who worked around the computer. These findings were partially predicted from a socio-cultural theory and show the importance of tools for mediating collaborative activity and collaborative learning

Theory formation by abduction : initial results of a case study based on the chemical revolution

Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" — dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction

A study of mapping exogenous knowledge representations into CONFIG

Qualitative reasoning is reasoning with a small set of qualitative values that is an abstraction of a larger and perhaps infinite set of quantitative values. The use of qualitative and quantitative reasoning together holds great promise for performance improvement in applications that suffer from large and/or imprecise knowledge domains. Included among these applications are the modeling, simulation, analysis, and fault diagnosis of physical systems. Several research groups continue to discover and experiment with new qualitative representations and reasoning techniques. However, due to the diversity of these techniques, it is difficult for the programs produced to exchange system models easily. The availability of mappings to transform knowledge from the form used by one of these programs to that used by another would open the doors for comparative analysis of these programs in areas such as completeness, correctness, and performance. A group at the Johnson Space Center (JSC) is working to develop CONFIG, a prototype qualitative modeling, simulation, and analysis tool for fault diagnosis applications in the U.S. space program. The availability of knowledge mappings from the programs produced by other research groups to CONFIG may provide savings in CONFIG's development costs and time, and may improve CONFIG's performance. The study of such mappings is the purpose of the research described in this paper. Two other research groups that have worked with the JSC group in the past are the Northwest University Group and the University of Texas at Austin Group. The former has produced a qualitative reasoning tool named SIMGEN, and the latter has produced one named QSIM. Another program produced by the Austin group is CC, a preprocessor that permits users to develop input for eventual use by QSIM, but in a more natural format. CONFIG and CC are both based on a component-connection ontology, so a mapping from CC's knowledge representation to CONFIG's knowledge representation was chosen as the focus of this study. A mapping from CC to CONFIG was developed. Due to differences between the two programs, however, the mapping transforms some of the CC knowledge to CONFIG as documentation rather than as knowledge in a form useful to computation. The study suggests that it may be worthwhile to pursue the mappings further. By implementing the mapping as a program, actual comparisons of computational efficiency and quality of results can be made between the QSIM and CONFIG programs. A secondary study may reveal that the results of the two programs augment one another, contradict one another, or differ only slightly. If the latter, the qualitative reasoning techniques may be compared in other areas, such as computational efficiency

Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press