scholarly journals Variable Forgetting in Reasoning about Knowledge

2009 ◽  
Vol 35 ◽  
pp. 677-716 ◽  
Author(s):  
K. Su ◽  
A. Sattar ◽  
G. Lv ◽  
Y. Zhang
Keyword(s):  
Knowledge Base ◽  
Common Knowledge ◽  
Knowledge Structure ◽  
Background Knowledge ◽  
Automated Analysis ◽  
Sufficient Condition ◽  
Basic Operation ◽  
Knowledge Reasoning ◽  
Multi Agent ◽  
Available Information

In this paper, we investigate knowledge reasoning within a simple framework called knowledge structure. We use variable forgetting as a basic operation for one agent to reason about its own or other agents\' knowledge. In our framework, two notions namely agents\' observable variables and the weakest sufficient condition play important roles in knowledge reasoning. Given a background knowledge base and a set of observable variables for each agent, we show that the notion of an agent knowing a formula can be defined as a weakest sufficient condition of the formula under background knowledge base. Moreover, we show how to capture the notion of common knowledge by using a generalized notion of weakest sufficient condition. Also, we show that public announcement operator can be conveniently dealt with via our notion of knowledge structure. Further, we explore the computational complexity of the problem whether an epistemic formula is realized in a knowledge structure. In the general case, this problem is PSPACE-hard; however, for some interesting subcases, it can be reduced to co-NP. Finally, we discuss possible applications of our framework in some interesting domains such as the automated analysis of the well-known muddy children puzzle and the verification of the revised Needham-Schroeder protocol. We believe that there are many scenarios where the natural presentation of the available information about knowledge is under the form of a knowledge structure. What makes it valuable compared with the corresponding multi-agent S5 Kripke structure is that it can be much more succinct.

Remote Fault Diagnosis System of Ultrahigh Speed Grinding Based on Multi-Agent

2009 ◽  
Vol 76-78 ◽  
pp. 67-71
Author(s):  
Wan Shan Wang ◽  
Tian Biao Yu
Keyword(s):  
Fault Diagnosis ◽  
Theoretical Research ◽  
Case Based Reasoning ◽  
Diagnosis System ◽  
Knowledge Reasoning ◽  
Diagnosis Method ◽  
Multi Agent ◽  
Fault Diagnosis System ◽  
Set Up ◽  
Remote Fault Diagnosis

A remote fault diagnosis method for ultrahigh speeding grinding based on multi-agent is presented. The general faults of ultrahigh speed grinding are analyzed and diagnosis model based on multi-agent is established, the dialogue layer, problem decomposition layer, control layer and problem solving layer in the process of diagnosis are studied and the knowledge reasoning model of fault diagnosis is set up based case-based reasoning (CBR) combining rule-based reasoning (RBR). Based on theoretical research, a remote fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running prove the theory is correctness and the technology is feasibility.

scholarly journals Towards the Automated Analysis and Database Development of Defibrillator Data from Cardiac Arrest

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Trygve Eftestøl ◽  
Lawrence D. Sherman
Keyword(s):  
Cardiac Arrest ◽  
Automated Analysis ◽  
Case Review ◽  
Process Data ◽  
Considerable Effort ◽  
Minimal Set ◽  
Patient Response ◽  
Sequence Representation ◽  
Available Information

Background.During resuscitation of cardiac arrest victims a variety of information in electronic format is recorded as part of the documentation of the patient care contact and in order to be provided for case review for quality improvement. Such review requires considerable effort and resources. There is also the problem of interobserver effects.Objective.We show that it is possible to efficiently analyze resuscitation episodes automatically using a minimal set of the available information.Methods and Results.A minimal set of variables is defined which describe therapeutic events (compression sequences and defibrillations) and corresponding patient response events (annotated rhythm transitions). From this a state sequence representation of the resuscitation episode is constructed and an algorithm is developed for reasoning with this representation and extract review variables automatically. As a case study, the method is applied to the data abstraction process used in the King County EMS. The automatically generated variables are compared to the original ones with accuracies≥90%for 18 variables and≥85%for the remaining four variables.Conclusions.It is possible to use the information present in the CPR process data recorded by the AED along with rhythm and chest compression annotations to automate the episode review.

Analytical Study on Privacy Attack Models in Privacy Preserving Data Publishing

2019 ◽  
pp. 1518-1538
Author(s):  
Sowmyarani C. N. ◽  
Dayananda P.
Keyword(s):  
Private Information ◽  
Background Knowledge ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Background Information ◽  
Published Data ◽  
Privacy Attack ◽  
Privacy Preserving Data Publishing ◽  
Available Information ◽  
Attack Models

Privacy attack on individual records has great concern in privacy preserving data publishing. When an intruder who is interested to know the private information of particular person of his interest, will acquire background knowledge about the person. This background knowledge may be gained though publicly available information such as Voter's id or through social networks. Combining this background information with published data; intruder may get the private information causing a privacy attack of that person. There are many privacy attack models. Most popular attack models are discussed in this chapter. The study of these attack models plays a significant role towards the invention of robust Privacy preserving models.

Clinical Practice Ontology Automatic Learning from SOAP Reports

2016 ◽  
pp. 349-363
Author(s):  
David Mendes ◽  
Irene Pimenta Rodrigues ◽  
Carlos Fernandes Baeta
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Natural Language ◽  
Knowledge Base ◽  
Clinical Information ◽  
Knowledge Structure ◽  
Daily Routine ◽  
Automatic Learning ◽  
Controlled Natural Language ◽  
Practice Knowledge

We show how we implemented an end-to-end process to automatically develop a clinical practice knowledge base acquiring from SOAP notes. With our contribution we intend to overcome the “Knowledge Acquisition Bottleneck” problem by jump-starting the knowledge gathering from the most widely available source of clinical information that are natural language reports. We present the different phases of our process to populate automatically a proposed ontology with clinical assertions extracted from daily routine SOAP notes. The enriched ontology becomes a reasoning able knowledge base that depicts accurately and realistically the clinical practice represented by the source reports. With this knowledge structure in place and novel state-of-the-art reasoning capabilities, based in consequence driven reasoners, a clinical QA system based in controlled natural language is introduced that reveals breakthrough possibilities regarding the applicability of Artificial Intelligence techniques to the medical field.

scholarly journals Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Li ◽  
Guimin Huang ◽  
Jianheng Chen ◽  
Yabing Wang
Keyword(s):  
Knowledge Base ◽  
Relation Extraction ◽  
Background Knowledge ◽  
Word Embedding ◽  
Entity Recognition ◽  
Instance Selection ◽  
Attention Model ◽  
Training Tool ◽  
Knowledge Based ◽  
Proposed Model

Relation extraction is the underlying critical task of textual understanding. However, the existing methods currently have defects in instance selection and lack background knowledge for entity recognition. In this paper, we propose a knowledge-based attention model, which can make full use of supervised information from a knowledge base, to select an entity. We also design a method of dual convolutional neural networks (CNNs) considering the word embedding of each word is restricted by using a single training tool. The proposed model combines a CNN with an attention mechanism. The model inserts the word embedding and supervised information from the knowledge base into the CNN, performs convolution and pooling, and combines the knowledge base and CNN in the full connection layer. Based on these processes, the model not only obtains better entity representations but also improves the performance of relation extraction with the help of rich background knowledge. The experimental results demonstrate that the proposed model achieves competitive performance.

Identifying the Task Requirements Essential to the Success of a Professional Ice Hockey Player: A Scout’s Perspective

1994 ◽  
Vol 13 (2) ◽  
pp. 180-195 ◽  
Author(s):  
Ralph Renger
Keyword(s):  
Qualitative Analysis ◽  
Knowledge Base ◽  
Knowledge Structure ◽  
Ice Hockey ◽  
Relative Importance ◽  
Disciplinary Knowledge ◽  
Knowledge Engineer ◽  
Hockey Player ◽  
Task Requirements ◽  
Professional Hockey

Vickers (1990) developed a cross-disciplinary knowledge structure of ice hockey by soliciting the expertise of various knowledge engineers (e.g., elite players, coaches, scientists). However, in developing this knowledge structure, the expertise of one important knowledge engineer, the professional hockey scout, was overlooked. The purpose of this investigation was to improve the knowledge base of ice hockey by utilizing professional hockey scouts as knowledge engineers. Through a qualitative analysis of NHL scouting reports filed between 1982 and 1990, several task requirements that were deemed essential by scouts for success as a professional player were identified. Having identified these task requirements, scouts were solicited to provide insight regarding the relative importance of such task requirements. Results established significant differences for between- and within-task requirements for the positions of forward and defense. The importance of these findings to coaching are discussed.

Consensus for discrete-time heterogeneous networked systems consisting of third-order and first-order agents under fixed and switching topologies

2017 ◽  
Vol 40 (9) ◽  
pp. 2748-2755 ◽  
Author(s):  
Huanyu Zhao ◽  
Shumin Fei
Keyword(s):  
System Theory ◽  
Graph Theory ◽  
Sufficient Condition ◽  
Consensus Problem ◽  
Multi Agent Systems ◽  
Algebraic Graph Theory ◽  
Third Order ◽  
Agent Systems ◽  
First Order ◽  
Multi Agent

This paper investigates the consensus problem for heterogeneous multi-agent systems consisting of third-order and first-order agents. The interaction topology includes both fixed and switching cases. First, by a model transformation, heterogeneous multi-agent systems are converted into equivalent error systems. Then we analyze the consensus problem of the multi-agent systems by analyzing the stability problem of the error systems. For a fixed topology, a sufficient condition for consensus of heterogeneous multi-agent systems is obtained based on algebraic graph theory and linear system theory. For a switching topology, a necessary and sufficient condition for mean-square consensus of multi-agent systems is obtained based on algebraic graph theory and Markovian jump system theory. Finally, we give some simulation examples.

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