Conceptual Primitive Decomposition for Knowledge Sharing via Natural Language

Natural language is an ideal mode of interaction and knowledge sharing between intelligent computer systems and their human users. But a major problem that natural language interaction poses is linguistic variation, or the paraphrase problem : there are a variety of ways of referring to the same idea. This is a special problem for intelligent systems in domains such as information retrieval, where a query presented in natural language is matched against an ontology or knowledge base, particularly when its representation uses a vocabulary based in natural language. This paper proposes solutions to these problems in primitive decomposition methods that represent concepts in terms of structures reflecting low-level, embodied human cognition. We argue that this type of representation system engenders richer relations between natural language expressions and knowledge structures, enabling more effective interactive knowledge sharing

Towards Modeling Conceptual Dependency Primitives with Image Schema Logic

Conceptual Dependency (CD) primitives and Image Schemas (IS) share a common goal of grounding symbols of natural language in a representation that allows for automated semantic interpretation. Both seek to establish a connection between high-level conceptualizations in natural language and abstract cognitive building blocks. Some previous approaches have established a CD-IS correspondence. In this paper, we build on this correspondence in order to apply a logic designed for image schemas to selected CD primitives with the goal of formally taking account of the CD inventory. The logic draws from Region Connection Calculus (RCC-8), Qualitative Trajectory Calculus (QTC), Cardinal Directions and Linear Temporal Logic (LTL). One of the primary premises of CD is a minimalist approach to its inventory of primitives, that is, it seeks to express natural language contents in an abstract manner with as few primitives as possible. In a formal analysis of physical primitives of CD we found a potential reduction since some primitives can be expressed as special cases of others

Crowdsourcing Image Schemas

With their potential to map experiental structures from the sensorimotor to the abstract cognitive realm, image schemas are believed to provide an embodied grounding to our cognitive conceptual system, including natural language. Few empirical studies have evaluated humans’ intuitive understanding of image schemas or the coherence of image-schematic annotations of natural language. In this paper we present the results of a human-subjects study in which 100 participants annotate 12 simple English sentences with one or more image schemas. We find that human subjects recruited from a crowdsourcing platform can understand image schema descriptions and use them to perform annotations of texts, but also that in many cases multiple image schema annotations apply to the same simple sentence, a phenomenon we call image schema collocations. This study carries implications both for methodologies of future studies of image schemas, and for the inexpensive and efficient creation of large text corpora with image schema annotations

Feasibility study of full-reactor gas core demonstration test

Separate studies of nuclear criticality, flow patterns, and thermodynamics for the gas core reactor concept have all given positive indications of its feasibility. However, before serious design for a full scale gas core application can be made, feasibility must be shown for operation with full interaction of the nuclear, thermal, and hydraulic effects. A minimum sized, and hence minimum expense, test arrangement is considered for a full gas core configuration. It is shown that the hydrogen coolant scattering effects dominate the nuclear considerations at elevated temperatures. A cavity diameter of somewhat larger than 4 ft (122 cm) will be needed if temperatures high enough to vaporize uranium are to be achieved

Linguistic Variation and Anomalies in Comparisons of Human and Machine-Generated Image Captions

Describing the content of a visual image is a fundamental ability of human vision and language systems. Over the past several years, researchers have published on major improvements on image captioning, largely due to the development of deep learning systems trained on large data sets of images and human-written captions. However, these systems have major limitations, and their development has been narrowly focused on improving scores on relatively simple “bag-of-words” metrics. Very little work has examined the overall complex patterns of the language produced by image-captioning systems and how it compares to captions written by humans. In this paper, we closely examine patterns in machine-generated captions and characterize how conventional metrics are inconsistent at penalizing them for nonhuman-like erroneous output. We also hypothesize that the complexity of a visual scene should be reflected in the linguistic variety of the captions and, in testing this hypothesis, we find that human-generated captions have a dramatically greater degree of lexical, syntactic, and semantic variation. These results have important implications for the design of performance metrics, gauging what deep learning captioning systems really understand in images, and the importance of the task of image captioning for cognitive systems researc

Image Schemas and Conceptual Dependency Primitives: A Comparison

A major challenge in natural language understanding research in artificial intelligence (AI) has been and still is the grounding of symbols in a representation that allows for rich semantic interpretation, inference, and deduction. Across cognitive linguistics and other disciplines, a number of principled methods for meaning representation of natural language have been proposed that aim to emulate capacities of human cognition. However, little cross-fertilization among those methods has taken place. A joint effort of human-level meaning representation from AI research and from cognitive linguistics holds the potential of contributing new insights to this profound challenge. To this end, this paper presents a first comparison of image schemas to an AI meaning representation system called Conceptual Dependency (CD). Restricting our study to the domain of physical and spatial conceptual primitives, we find connections and mappings from a set of action primitives in CD to a remarkably similar set of image schemas. We also discuss important implications of this connection, from formalizing image schemas to improving meaning representation systems in AI

Community? What do you mean? An investigation into how differing understandings of the term ‘community’ shapes care-leavers’ move to independence

We explored the ways in which a performance-led approach to community formation and personal development can inform care-leavers’ move to independence. We adopted a case study approach, working with two groups of participants: young people leaving care and their support workers in Devon County Council’s Children in Care team (DCCCCS). The project was co-designed and co-developed with participants from both groups and our colleagues from Exstream Theatre Company. In this action–research project all researchers acted as participant-observers. It had two key objectives: first, to better understand how the term ‘community’ shaped the processes that the participating young people engaged with, and second, to take a practice-led approach to explore if, and how, performance-based methods and principles could be used to support the participants as they left care. A number of findings challenged our early assumptions, in particular the complexity of working with people with unstable and unpredictable lives. By working with the young people and DCCCCS managers and support workers, we developed a new way of using the creation of a theatrical performance, a deeply-supportive, peer-enabled environment and a theatre company, to enable a space in which participants could reimagine both their sense of self and community. The development and implementation of this model raises further research questions relating to how new communities might be formed, as well as suggesting a number of uses that may inform the future development of the Connected Communities community engagement strategy

Modeling the Impact of Operator Trust on Performance in Multiple Robot Control,

We developed a system dynamics model to simulate the impact of operator trust on performance in multiple robot control. Analysis of a simulated urban search and rescue experiment showed that operators decided to manually control the robots when they lost trust in the autonomous planner that was directing the robots. Operators who rarely used manual control performed the worst. However, the operators who most frequently used manual control reported higher workload and did not perform any better than operators with moderate manual control usage. Based on these findings, we implemented a model where trust and performance form a feedback loop, in which operators perceive the performance of the system, calibrate their trust, and adjust their control of the robots. A second feedback loop incorporates the impact of trust on cognitive workload and system performance. The model was able to replicate the quantitative performance of three groups of operators within 2.3%. This model could help us gain a greater understanding of how operators build and lose trust in automation and the impact of those changes in trust on performance and workload, which is crucial to the development of future systems involving humanautomation collaboration.This research is sponsored by the Office of Naval Research and the Air Force Office of Scientific Research

Interface Design for Unmanned Vehicle Supervision through Hybrid Cognitive Task Analysis

While there is currently significant interest in developing Unmanned Aerial Systems (UASs) that can be supervised by a single operator, the majority of these systems focus on Intelligence, Surveillance, and Reconnaissance (ISR) domains. One domain that has received significantly less attention is the use of multiple UASs to insert or extract supplies or people. To this end, MAVIES (Multi-Autonomous Vehicle Insertion-Extraction System) was developed to allow a single operator the ability to supervise a primary cargo Unmanned Aerial Vehicle (UAV) along with multiple scouting UAVs. This paper will detail the development of the design requirements generated through a Hybrid Cognitive Task Analysis (hCTA) and the display that resulted from these efforts. A major innovation in the hCTA process in this effort was the alteration of the traditional decision ladder process to specifically identify decision-making tasks that must be augmented with automation

Modeling the Impact of Operator Trust on Performance in Multiple Robot Control

We developed a system dynamics model to simulate the impact of operator trust on performance in multiple robot control. Analysis of a simulated urban search and rescue experiment showed that operators decided to manually control the robots when they lost trust in the autonomous planner that was directing the robots. Operators who rarely used manual control performed the worst. However, the operators who most frequently used manual control reported higher workload and did not perform any better than operators with moderate manual control usage. Based on these findings, we implemented a model where trust and performance form a feedback loop, in which operators perceive the performance of the system, calibrate their trust, and adjust their control of the robots. A second feedback loop incorporates the impact of trust on cognitive workload and system performance. The model was able to replicate the quantitative performance of three groups of operators within 2.3%. This model could help us gain a greater understanding of how operators build and lose trust in automation and the impact of those changes in trust on performance and workload, which is crucial to the development of future systems involving human-automation collaboration