scholarly journals Implementation of Hardware-Based Expert Systems and Comparison of Their Performance to Software-Based Expert Systems

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 361
Author(s):  
Noah Ritter ◽  
Jeremy Straub
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert Systems ◽  
Iterative Algorithm ◽  
Processing Speed ◽  
Implementation Approach ◽  
Potential Applications ◽  
Decision Making Processes ◽  
Software Implementations ◽  
Software And Hardware

Expert systems are a form of highly understandable artificial intelligence that allow humans to trace the decision-making processes that are used. While they are typically software implemented and use an iterative algorithm for rule-fact network processing, this is not the only possible implementation approach. This paper implements and evaluates the use of hardware-based expert systems. It shows that they work accurately and can be developed to parallel software implementations. It also compares the processing speed of software and hardware-based expert systems, showing that hardware-based systems typically operate two orders of magnitude faster than the software ones. The potential applications that hardware-based expert systems can be used for and the capabilities that they can provide are discussed.

Finding Cost-Effective Applications for Expert Systems

1993 ◽  
Vol 115 (1) ◽  
pp. 56-61
Author(s):  
P. J. Hartman
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert Systems ◽  
Processing Speed ◽  
Development Process ◽  
Cost Effective ◽  
Risk Assessments ◽  
Problem Complexity ◽  
Practical Application ◽  
The Right

Expert systems are one of the few areas of artificial intelligence which have successfully made the transition from research and development to practical application. The key to fielding a successful expert system is finding the right problem to solve. AI costs, including all the development and testing, are so high that the problems must be very important to justify the effort. This paper develops a systematic way of trying to predict the future. It provides robust decision-making criteria, which can be used to predict the success or failure of proposed expert systems. The methods focus on eliminating obviously unsuitable problems and performing risk assessments and cost evaluations of the program. These assessments include evaluation of need, problem complexity, value, user experience, and the processing speed required. If an application proves feasible, the information generated during the decision phase can be then used to speed the development process.

Cultivating Trustworthy Artificial Intelligence in Digital Government

2020 ◽  
pp. 089443932098012
Author(s):  
Teresa M. Harrison ◽  
Luis Felipe Luna-Reyes
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Policy Analysis ◽  
Public Trust ◽  
Governance Structures ◽  
Cognitive Tools ◽  
Lens Model ◽  
Digital Government ◽  
Government Decision ◽  
Decision Making Processes

While there is growing consensus that the analytical and cognitive tools of artificial intelligence (AI) have the potential to transform government in positive ways, it is also clear that AI challenges traditional government decision-making processes and threatens the democratic values within which they are framed. These conditions argue for conservative approaches to AI that focus on cultivating and sustaining public trust. We use the extended Brunswik lens model as a framework to illustrate the distinctions between policy analysis and decision making as we have traditionally understood and practiced them and how they are evolving in the current AI context along with the challenges this poses for the use of trustworthy AI. We offer a set of recommendations for practices, processes, and governance structures in government to provide for trust in AI and suggest lines of research that support them.

scholarly journals Statistics and Artificial Intelligence-Based Pavement Performance and Remaining Service Life Prediction Models for Flexible and Composite Pavement Systems

2020 ◽  
Vol 2674 (10) ◽  
pp. 448-460
Author(s):  
Orhan Kaya ◽  
Halil Ceylan ◽  
Sunghwan Kim ◽  
Danny Waid ◽  
Brian P. Moore
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Prediction Models ◽  
Pavement Management ◽  
Pavement Performance ◽  
Management Decision ◽  
Performance Models ◽  
Remaining Service Life ◽  
Management Decision Making ◽  
Decision Making Processes

In their pavement management decision-making processes, U.S. state highway agencies are required to develop performance-based approaches by the Moving Ahead for Progress in the 21st Century (MAP-21) federal transportation legislation. One of the performance-based approaches to facilitate pavement management decision-making processes is the use of remaining service life (RSL) models. In this study, a detailed step-by-step methodology for the development of pavement performance and RSL prediction models for flexible and composite (asphalt concrete [AC] over jointed plain concrete pavement [JPCP]) pavement systems in Iowa is described. To develop such RSL models, pavement performance models based on statistics and artificial intelligence (AI) techniques were initially developed. While statistically defined pavement performance models were found to be accurate in predicting pavement performance at project level, AI-based pavement performance models were found to be successful in predicting pavement performance in network level analysis. Network level pavement performance models using both statistics and AI-based approaches were also developed to evaluate the relative success of these two models for network level pavement performance modeling. As part of this study, in the development of pavement RSL prediction models, automation tools for future pavement performance predictions were developed and used along with the threshold limits for various pavement performance indicators specified by the Federal Highway Administration. These RSL models will help engineers in decision-making processes at both network and project levels and for different types of pavement management business decisions.

scholarly journals How does decisional capacity evolve with normal cognitive aging: systematic review of the literature

2019 ◽  
Vol 11 (1) ◽  
pp. 117-129 ◽  
Author(s):  
Thomas Tannou ◽  
Séverine Koeberlé ◽  
Régis Aubry ◽  
Emmanuel Haffen
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Processing Speed ◽  
Cognitive Aging ◽  
Social Issues ◽  
Meta Analysis ◽  
Decisional Capacity ◽  
Literature Reviews ◽  
Decision Making Processes ◽  
Motivation Profiles

Abstract Purpose Aging is associated with increased needs related to complex decisions, particularly in medical and social issues. However, the complexity of decision-making involves many neurological functions and structures which are potentially altered by cognitive aging. Methodology A systematic review was conducted in accordance with PRISMA guidelines to examine changes in decision-making occurring in normal cognitive aging. The keywords “decision making” and “normal aging” were used to find the clinical studies and literature reviews focused on these changes. Results A total of 97 articles were considered in the review, and ultimately 40 articles were selected, including 30 studies and 10 literature reviews. The data from these studies were of uneven quality and too disparate to allow meta-analysis according to PRISMA criteria. Nevertheless, a key result of the analysis is the decrease of processing speed with aging. In ambiguous decision-making situations, the alteration of the ventromedial system is associated with changes in motivation profiles. These changes can be compensated by experience. However, difficulties arise for older adults in the case of one-off decisions, which are very common in the medical or medico-social domains. Conclusions Cognitive aging is associated with a slowdown in processing speed of decision-making, especially in ambiguous situations. However, decision-making processes which are based on experience and cases in which sufficient time is available are less affected by aging. These results highlight the relativity of decision-making capacities in cognitive aging.

scholarly journals Knowledge Discovery in Data in Construction Projects

2016 ◽  
Vol 62 (2) ◽  
pp. 217-228 ◽  
Author(s):  
J. Szelka ◽  
Z. Wrona
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Discovery ◽  
Construction Projects ◽  
Domain Expert ◽  
Knowledge Engineer ◽  
Computer Aided ◽  
Structured Problems ◽  
Decision Making Processes

Abstract Decision-making processes, including the ones related to ill-structured problems, are of considerable significance in the area of construction projects. Computer-aided inference under such conditions requires the employment of specific methods and tools (non-algorithmic ones), the best recognized and successfully used in practice represented by expert systems. The knowledge indispensable for such systems to perform inference is most frequently acquired directly from experts (through a dialogue: a domain expert - a knowledge engineer) and from various source documents. Little is known, however, about the possibility of automating knowledge acquisition in this area and as a result, in practice it is scarcely ever used. It has to be noted that in numerous areas of management more and more attention is paid to the issue of acquiring knowledge from available data. What is known and successfully employed in the practice of aiding the decision-making is the different methods and tools. The paper attempts to select methods for knowledge discovery in data and presents possible ways of representing the acquired knowledge as well as sample tools (including programming ones), allowing for the use of this knowledge in the area under consideration.

scholarly journals Law and software analysis: A step closer to resolving investment disputes through artificial intelligence?

2021 ◽  
Vol 59 (2) ◽  
pp. 123-140
Author(s):  
Milena Galetin ◽  
Anica Milovanović
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Legal Issues ◽  
The Other ◽  
Judicial Discretion ◽  
Applicable Law ◽  
Software Analysis ◽  
Starting Point ◽  
Decision Making Processes ◽  
Larger Sample

Considering the possibility of using artificial intelligence in resolving legal disputes is becoming increasingly popular. The authors examine whether soft ware analysis can be applied to resolve a specific issue in investment disputes - to determine the applicable law to the substance of the dispute and highlight the application of artificial intelligence in the area of law, especially in predicting the outcome of a dispute. The starting point is a sample of 50 arbitral awards and the results of previously conducted research. It has been confirmed that soft ware analysis can be useful in decision-making processes, but not to the extent that arbitrators could exclusively rely on it. On the other hand, the development of an algorithm that would predict applicable law for different legal issues required a much larger sample. We also believe that the existence of different legal and factual circumstances in each case, as well as the personality of the arbitrator and arbitral/judicial discretion are limitations of the application of artificial intelligence in this area.

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