How can spatial language be learned?

How languages are learned is one of the deepest mysteries of cognitive science. This question can be addressed from multiple perspectives. This position paper considers two of them: (1) How do people learn spatial language? (2) Given the wide range of spatial terms in language, how might we bootstrap the linguistic capabilities of intelligent systems that need spatial language to achieve wide and accurate coverage? We discuss each question in turn

How should depiction be represented and reasoned about?

Interpreting a scene requires understanding how its visual properties and context yield evidence about the spatial and conceptual properties of what it depicts. Depiction is intimately tied to spatial language, since describing a scene linguistically, or imagining a scene described in language, involves connecting linguistic and spatial knowledge. We focus here on scenes described via sketching

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

Qualitative models for space system engineering

The objectives of this project were: (1) to investigate the implications of qualitative modeling techniques for problems arising in the monitoring, diagnosis, and design of Space Station subsystems and procedures; (2) to identify the issues involved in using qualitative models to enhance and automate engineering functions. These issues include representing operational criteria, fault models, alternate ontologies, and modeling continuous signals at a functional level of description; and (3) to develop a prototype collection of qualitative models for fluid and thermal systems commonly found in Space Station subsystems. Potential applications of qualitative modeling to space-systems engineering, including the notion of intelligent computer-aided engineering are summarized. Emphasis is given to determining which systems of the proposed Space Station provide the most leverage for study, given the current state of the art. Progress on using qualitative models, including development of the molecular collection ontology for reasoning about fluids, the interaction of qualitative and quantitative knowledge in analyzing thermodynamic cycles, and an experiment on building a natural language interface to qualitative reasoning is reported. Finally, some recommendations are made for future research

Answering Comparison Questions in SHAKEN: A Progress Report

Abstract An important class of questions for knowledge based systems concern comparisons, such as "How is X like r." and "How are X and Y different?" This paper describes how we have used a cognitive simulation of analogical processing to answer such questions, to support domain experts in entering new knowledge. We outline techniques for case construction and summarization of comparison results that have been developed and refined based on an independent formative evaluation. In addition to these techniques, we discuss the role of the comparison system in SHAKEN, the larger system in which they are embedded, and our plans for further improvements

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design

Qualitative Spatial Interpretation of Course-of-Action Diagrams

Abstract This paper demonstrates qualitative spatial reasonin g techniques in a real-world diagrammatic reasoning task: Course-of-Action (COA) diagrams . COA diagrams are military planning diagrams that depict unit movements an d tasks in a given region . COA diagrams are a useful test be d for researching diagram understanding due to their composable symbology, their intrinsically spatial task, an d their use across many types of military planning . W e constructed two COA diagram interpreters using ou r qualitative spatial reasoning engine, GeoRep . The firs t system uses GeoRep to interpret individual COA glyphs . The second system, building upon the first, takes preclassified symbol input and then uses GeoRep to describ e geographic relationships implied by the symbol arrangements . This latter system, in a recent DARPA initiative , answered dozens of geographic queries about many different COA diagrams . This research shows that qualitative spatial reasoning, through tools like GeoRep, provides a useful substrate for complex diagrammatic reasoning

Component-based Construction of a Science Learning Space.

Abstract. We present a vision for learning environments, called Science Learning Spaces, that are rich in engaging content and activities, provide constructive experiences in scientific process skills, and are as instructionally effective as a personal tutor. A Science Learning Space combines three independent software systems: 1) lab/field simulations in which experiments are run and data is collected, 2) modeling/construction tools in which data representations are created, analyzed and presented, and 3) tutor agents that provide just-in-time assistance in higher order skills like experimental strategy, representational tool choice, conjecturing, and argument. We believe that achieving this ambitious vision will require collaborative efforts facilitated by a component-based software architecture. We have created a feasibility demonstration that serves as an example and a call for further work toward achieving this vision. In our demonstration, we combined 1) the Active Illustrations lab simulation environment, 2) the Belvedere argumentation environment, and 3) a modeltracing Experimentation Tutor Agent. We illustrate student interaction in this Learning Space and discuss the requirements, advantages, and challenges in creating one. The Science Learning Space Vision Imagine an Internet filled with possibility for student discovery. A vast array of simulations are available to explore any scientific field you desire. Easy-to-use data representation and visualization tools are at your fingertips. As you work, intelligent tutor agents are watching silently in the background, available at any time to assist you as you engage in scientific inquiry practices: experimentation, analysis, discovery, argumentation. This is our vision for Science Learning Spaces. Table 1 summarizes how this vision contrasts with typical classroom experience