The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Detecting and correcting errors in ruled-based expert systems : an integration of empirical and explanation-based learning

In this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quinlan's FOIL program, can learn in spite of an incorrect domain theory (e.g., a knowledge base of an expert system that contains some erroneous rules). A prototype knowledge acquisition tool, KR-FOCL, has been constructed that can utilize a trace of FOCL to suggest revisions to a rule base

PaperRobot: Incremental Draft Generation of Scientific Ideas

We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively.Comment: 12 pages. Accepted by ACL 2019 Code and resource is available at https://github.com/EagleW/PaperRobo

Increasingly automated procedure acquisition in dynamic systems

Procedures are widely used by operators for controlling complex dynamic systems. Currently, most development of such procedures is done manually, consuming a large amount of paper, time, and manpower in the process. While automated knowledge acquisition is an active field of research, not much attention has been paid to the problem of computer-assisted acquisition and refinement of complex procedures for dynamic systems. The Procedure Acquisition for Reactive Control Assistant (PARC), which is designed to assist users in more systematically and automatically encoding and refining complex procedures. PARC is able to elicit knowledge interactively from the user during operation of the dynamic system. We categorize procedure refinement into two stages: diagnosis - diagnose the failure and choose a repair - and repair - plan and perform the repair. The basic approach taken in PARC is to assist the user in all steps of this process by providing increased levels of assistance with layered tools. We illustrate the operation of PARC in refining procedures for the control of a robot arm

Effective skill refinement: Focusing on process to ensure outcome

In contrast to the abundance of motor skill acquisition and performance research, there is a paucity of work which addresses how athletes with an already learnt and well-established skill may go about making a subtle change, or refinement, to that skill. Accordingly, the purpose of this review paper is to provide a comprehensive overview of current understanding pertaining to such practice. Specifically, this review addresses deliberately initiated refinements to closed and self-paced skills (e.g., javelin throwing, golf swing and horizontal jumps). In doing so, focus is directed to three fundamental considerations within applied coaching practice and future research endeavours; the intended outcomes, process and evaluative measures of skill refinement. Conclusions suggest that skill refinement is not the same as skill acquisition or performing already learnt skills with high-levels of automaticity. Due to the complexity of challenge faced, refinements are best addressed as an interdisciplinary solution, with objective measures informing coach decision making

Knowledge dependencies in fuzzy information systems evaluation

Experience and research within the field of Information Systems Evaluation (ISE), has traditionally centered on providing tools and techniques for investment justification and appraisal, based upon explicit knowledge which encodes financial and other direct situational factors (such as accounting, costing and risk metrics). However, such approaches tend not to include additional causal interdependencies that are based upon tacit knowledge and are inherent within such a decision-making task. The authors show the results of applying a cognitive mapping approach, in the guise of a Fuzzy Cognitive Mapping (FCM) simulation, i.e. Fuzzy Information Systems Evaluation (F-ISE), in order to highlight the usefulness of applying such a technique. The authors highlight those contingent and necessary knowledge dependencies, in an exploratory sense, which relate to the investment appraisal decision-making task, in terms of the interplay between tacit and explicit knowledge, in this regard

The Cognitive Atlas: Employing Interaction Design Processes to Facilitate Collaborative Ontology Creation

The Cognitive Atlas is a collaborative knowledge-building project that aims to develop an ontology that characterizes the current conceptual framework among researchers in cognitive science and neuroscience. The project objectives from the beginning focused on usability, simplicity, and utility for end users. Support for Semantic Web technologies was also a priority in order to support interoperability with other neuroscience projects and knowledge bases. Current off-the-shelf semantic web or semantic wiki technologies, however, do not often lend themselves to simple user interaction designs for non-technical researchers and practitioners; the abstract nature and complexity of these systems acts as point of friction for user interaction, inhibiting usability and utility. Instead, we take an alternate interaction design approach driven by user centered design processes rather than a base set of semantic technologies. This paper reviews the initial two rounds of design and development of the Cognitive Atlas system, including interactive design decisions and their implementation as guided by current industry practices for the development of complex interactive systems