An Artificial Intelligence program to advise physicians regarding antimicrobial therapy

A New Approach In Learning For Intelligent Multi Agent Systems

The need to create mechanisms or systems to acquire, represent and convey knowledge is one of the problems that has been given priority in the passing of the years of research, the need for acquisition has generated the need for more compact models that conserve implicit and explicit knowledge generated within their organizations. In this environment, due to the change that has generated knowledge management, companies recognize the importance of the management of these intangible assets, as a requirement to stay in the market and adapt to organizational and technological changes, each organization must find its own way and recognize that intellectual capacity has become an important factor for the success of organizational development. The aim of this paper is to present the results of a systematic review of the literature by which we identify some acquisition techniques and knowledge representation, that can be replicated in organizations to leverage internal and external knowledge to the organization and achieve advantages sustainable competitive . The results present management techniques related to the culture and values organizational as well as the definition of different types of alliances also identified as expert systems technologies and social tools as useful for the acquisition and representation of knowledge.

Dual coding of knowledge in the human brain.

Artificial intelligence can assist in industrial control in many ways, but this paper concentrates on the role of knowledge-based systems in the areas of control, detection and diagnosis of faults, scheduling and planning. The paper argues that the application of knowledge-based techniques can provide a unification of these and other activities, and outlines the requirements of knowledge representation and inference techniques that are needed to make such systems a reality. The advances in hardware, including sensors as well as computers, that will be driven by and in turn stimulate the introduction of the knowledge-based industrial control systems are also considered.

One of the major unsolved problems in knowledge acquisition is reconciling knowledge originating from different sources. This paper proposes a technique for reconciling knowledge in two independent knowledge bases, describes a working program built to implement that technique, and discusses an exploratory study for validating the technique. The technique is based on the use of heuristics for identifying and resolving discrepancies between the knowledge bases. Each heuristic developed provides detection and resolution procedures for a distinct variety of discrepancy in the knowledge bases. Sample discrepancies include using synonyms for the same term, conflicting rules, and extra reasoning steps. Discrepancies are detected and resolved through the use of circumstantial evidence available from the knowledge bases themselves and by asking sharply focussed questions to the experts responsible for the knowledge bases. The technique was tested on two independently developed knowledge bases designed to aid novice statisticians in diagnosing problems in linear regression models. The heuristics located a significant number of the discrepancies between the knowledge bases and assisted the experts in creating a consensus knowledge base for diagnosing multicollinearity problems. We argue that the task of identifying discrepancies between independent bodies of knowledge is an inevitable part of any large knowledge acquisition effort. Hence the heuristics developed in this work are applicable even when knowledge acquisition is not done by reconciling two complete knowledge bases. We also suggest that our approach can be extended to other knowledge representations such as frames and database schemas, and speculate about its potential application to other domains involving the reconciliation of knowledge, such as requirements determination, negotiation, and design.

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation.

Knowledge graph technology is a critical part of artificial intelligence research. It establishes a knowledge base with the capacity of semantic processing and open interconnection in order to provide intelligent information service, such as search, question-answering, personalized recommendation, and so on. This article first presents a comprehensive study on definitions and architectures of knowledge graphs. Then we summarizes recent advances in knowledge graphs, including knowledge extraction, knowledge representation, knowledge fusion, and knowledge reasoning, with typical applications. Finally, this article concludes with future challenges of knowledge graphs.

This chapter contains sections titled: Automatic Programming, Cutting the Problem Down, Theorem-Proving Techniques, Deductive Synthesis, Program Verification, Transformational Approaches, Specification Techniques, Natural Language Specifications, Very High Level Languages, Programming by Example, Intelligent Assistants, Programming Tutors, Knowledge Representation, Programming Knowledge, Domain Knowledge, Artificial Intelligence Programming, Acknowledgments, References

Models of Expertise in Knowledge Acquisition

Dutch research on knowledge-based instructional systems: Introduction to the special issue

This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence (AI) has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can be defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools.

This paper aims to examine the impact of the various stages of managing knowledge - knowledge acquisition, dissemination and utilization - on the innovativeness of Technology and Engineering Consultancies (TEC) in Singapore, by drawing on results from questionnaires mailed to 446 TEC firms. According to the findings, knowledge acquisition and knowledge utilization have a positive impact on the innovativeness of TEC firms and this impact is stronger for firms operating in more volatile environments. TEC firms are typically relying on having a large portfolio of existing plans that can be reused rather than innovating. However, the clients of TEC firms are usually lead users in the industry, which may lead to the need for further development of inadequate current practices in order to remain innovative in the long run.

This article reviews approaches based on artificial intelligence (AI), which contributes to the security of cyber environments. We examine existing techniques using several indicators: explainability, performance and robustness. These indicators have been chosen based on their importance for user acceptance and interpretability of the approach. Indeed, the AI field is vast and is divided into several sub-domains. The two most well-known sub-domains are symbolic AI (representation of knowledge, rules and operations based on symbols) and numeric AI (calculations and algorithms using numeric information, focusing on the result, not the representation of knowledge). While most approaches investigated come from numeric AI, we conclude on the need for hybrid AI systems, combining the advantages of both AI sub-fields while maximising the protection provided against cyberattacks.

Rough set based lattice structure for knowledge representation in medical expert systems: Low back pain management case study