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

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 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

Simulating Similarity-Based Retrieval: A Comparison of ARCS and MAC/FAC

Current theories and supporting simulations of similaritybased retrieval disagree in their process model of semantic similarity decisions. We compare two current computational simulations of similarity-based retrieval, MAC/FA C and ARCS, with particular attention to the semantic similarity models used in each. Four experiments are presented comparing the performance of these simulations on a common set of representations. The results suggest that MAC/FAC, with its identicality-based ccmstraint on semantic similarity, provides a better account of retrieval than ARCS, with its similarity-table based model

Children’s Sentential Complement Use Leads the Theory of Mind Development Period: Evidence from the CHILDES Corpus

Converging evidence suggests that children’s linguistic and theory of mind (ToM) development are linked. Specifically, learning the sentential complement grammatical structure has been shown to play a causal role in the development of some false belief reasoning skills. Here, we extend this line of work to examine this relationship in the wild by means of a corpus analysis of children’s speech during the typical period of ToM development. We show that children’s use of the sentential complement grammatical structure increases immediately preceding the ToM development period and plateaus shortly thereafter. Furthermore, we find that parents’ child-directed speech follows a similar pattern

Incremental Structure-Mapping

Many cognitive tasks involving analogy, such as understanding metaphors, problem-solving, and learning, require the ability to extend mappings as new information is found. This paper describes a new version of SME, called I-SME, that operates incrementally. I-SME is inspired by Keane's lAM model and the use of incremental mapping in Falkenhainer's PHINEAS learning system. W e describe the I-SME algorithm and discuss tradeoffs introduced by incremental mapping, including parallel versus serial pnxessing and pragmatic influences. The utility of 1-SME is illustrated by two examples. First, we show that I-SME can account for the psychological results found by Keane on a serial version of the Holyoak & Thagard attribute mapping task. Second, we describe how I-SME is used in the Minimal Analogical Reasoning System (MARS), which uses analogy to solve engineering thermodynamics problems

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