Dimensions of knowledge sharing and reuse

EON: A Component-Based Approach to Automation of Protocol-Directed Therapy

Beginning with McCarthy's Advice Taker (1959), AI has pursued the goal of providing a system with explicit, general knowledge and having the system reason over that knowledge. However, expressing the knowledge in a formal (logical or probabilistic) representation has been a major obstacle to this research. This paper investigates a modern approach to this problem where the facts and rules are provided as natural language sentences, thus bypassing a formal representation. We train transformers to reason (or emulate reasoning) over these sentences using synthetically generated data. Our models, that we call RuleTakers, provide the first empirical demonstration that this kind of soft reasoning over language is learnable, can achieve high (99%) accuracy, and generalizes to test data requiring substantially deeper chaining than seen during training (95%+ scores). We also demonstrate that the models transfer well to two hand-authored rulebases, and to rulebases paraphrased into more natural language. These findings are significant as it suggests a new role for transformers, namely as limited "soft theorem provers" operating over explicit theories in language. This in turn suggests new possibilities for explainability, correctability, and counterfactual reasoning in question-answering.

pdf/transformers-as-soft-reasoners-over-language-utnkn2auaj.pdf

Transformers as Soft Reasoners over Language

A computer implemented simulation and evaluation method simulates interventions to a patient by a user, and evaluates the interventions responsive to predetermined criteria and the interventions. The method includes defining a test area to evaluate the user to at least one of predetermined criteria and a user profile, selecting genetic information of the patient responsive to the test area, and generating a patient history responsive to the test area and the genetic information. The method also includes receiving at least one intervention input by the user, and evaluating the user responsive to the intervention and predetermined criteria.

Computer architecture and process of patient generation, evolution, and simulation for computer based testing system

A method and system for patient generation and evolution for a computer-based testing system and/or expert system. One or more belief networks, which describe parallel health state networks are accessed by a user or a computer. A knowledge base, at least in part, is scripted from the one or more belief networks by the computer. A model patient at least in part, is instantiated by the computer from the scripted knowledge base. Optionally, the model patient is evolved by the computer in accordance with the parallel health state networks and responsive to a received course of action.

Computer architecture and process of patient generation, evolution, and simulation for computer based testing system using bayesian networks as a scripting language

A model for critiquing based on automated medical records

As tools for the construction of expert systems have become commonly available, workers in artificial intelligence (AI) have begun to pay increasing attention to the problems of building and maintaining large knowledge bases. In particular, considerable discussion has concentrated on the difficulty of eliciting useful and complete knowledge about a given application task from experts—a problem widely referred to as the knowledgeacquisition bottleneck. At the same time, AI researchers have often noted that the systems that they build are brittle, showing marked degradation in reasoning performance when confronted with unusual or atypical cases. Most expert systems, like pattern-recognition systems, are concerned with the classification of entities in the world. The process of building knowledge bases for expert systems that perform classification, like the process of developing statistical classifiers, requires that someone who is familiar with the application area both determine the relevant types of objects in the domain and identify the observable features of those objects that may be germane to the classification problem. In the case of both expert systems and pattern-recognition systems, developers must create models of the application area. Although the pattern-recognition literature does not generally mention concepts such as “knowledge acquisition” or “brittleness,” these problems are also important in the construction of statistical models. This paper shows how builders of expert and patternrecognition systems face many of the same challenges, and discusses ways in which the two research communities can learn from each other's experiences in creating different types of computational models for classification tasks.

Of Brittleness and Bottlenecks: Challenges in the Creation of Pattern-Recognition and Expert-System Models

This paper describes the evaluation of a critiquing system, HYPERCRITIC, that relies on automated medical records for its data input. The purpose of HYPERCRITIC is to offer comments to general practitioners on their treatment of hypertension. HYPERCRITIC has access to the data stored in a primary-care information system that supports a fully automated medical record. Medical records of 20 patients with hypertension were submitted to both physicians and HYPERCRITIC. The critique generated by the physicians was subsequently compared to the critique generated by HYPERCRITIC. Of the 260 comments made by the majority of the physicians, 118 were also made by HYPERCRITIC. The reasons why HYPERCRITIC failed to reproduce the remaining 142 comments are evaluated.

Knowledge-Based Systems. Methods for Developing and Evaluating Expert Systems: Critiquing Physician Decision Making Using Data from Automated Medical Records: Assessing the Limitations

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Johan van der Lei

Author Tools

Chat about Author

Papers

A model for critiquing based on automated medical records

Of Brittleness and Bottlenecks: Challenges in the Creation of Pattern-Recognition and Expert-System Models

Knowledge-Based Systems. Methods for Developing and Evaluating Expert Systems: Critiquing Physician Decision Making Using Data from Automated Medical Records: Assessing the Limitations