Using artificial intelligence techniques to support project management

1987 ◽  
Vol 1 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Raymond E. Levitt ◽  
John C. Kunz
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Project Management ◽  
Knowledge Engineering ◽  
Project Planning ◽  
Ai Planning ◽  
Engineering Project ◽  
Knowledge Processing ◽  
Domain Specific ◽  
Knowledge Based

AbstractThis paper develops a philosophy for the use of Artificial Intelligence (AI) techniques as aids in engineering project management.First, we propose that traditional domain-independent, ‘means–and’ planners, may be valuable aids for planning detailed subtasks on projects, but that domain-specific planning tools are needed for work package or executive level project planning. Next, we propose that hybrid computer systems, using knowledge processing techniques in conjunction with procedural techniques such as decision analysis and network-based scheduling, can provide valuable new kinds of decision support for project objective-setting and project control, respectively. Finally we suggest that knowledge-based interactive graphics, developed for providing graphical explanations and user control in advanced knowledge processing environments, can provide powerful new kinds of decision support for project management.The first claim is supported by a review and analysis of previous work in the area of automated AI planning techniques. Our experience with PLATFORM I, II and III, a series of prototype AI-leveraged project management systems built using the IntelliCorp Knowledge Engineering Environment (KEE™), provides the justification for the latter two claims.

scholarly journals A META-TOOL TO SUPPORT THE DEVELOPMENT OF KNOWLEDGE ENGINEERING METHODOLOGIES AND PROJECTS

2012 ◽  
Vol 22 (08) ◽  
pp. 1055-1083
Author(s):  
JOSÉ ELOY FLÓREZ ◽  
JAVIER CARBÓ ◽  
FERNANDO FERNÁNDEZ
Keyword(s):  
Knowledge Acquisition ◽  
Ad Hoc ◽  
Knowledge Engineering ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Combined Application ◽  
Processing Information ◽  
Knowledge Acquisition Methods ◽  
Applied Knowledge

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

Multi-Agent Architecture for Knowledge-Driven Decision Support

2010 ◽  
pp. 28-46
Author(s):  
Rahul Singh
Keyword(s):  
Decision Support ◽  
Intelligent Agents ◽  
Agent Architecture ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Intelligent Software Agents ◽  
Intelligent Decision ◽  
Domain Specific Knowledge ◽  
Multi Agent

Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers. Increasingly, artificial intelligence (AI) techniques for knowledge representation are being used to deliver knowledge-driven decision support in multiple forms. In this chapter, we present an Architecture for knowledge-based decision support, delivered through a Multi-Agent Architecture. We illustrate how to represent and exchange domain-specific knowledge in XML-format through intelligent agents to create exchange and use knowledge to provide intelligent decision support. We show the integration of knowledge discovery techniques to create knowledge from organizational data; and knowledge repositories (KR) to store, manage and use data by intelligent software agents for effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.

Overview of Intelligent Design on Mold

2012 ◽  
Vol 459 ◽  
pp. 394-397
Author(s):  
Bin Wang ◽  
Jian Jun Chen ◽  
Jie Tao
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Design ◽  
Knowledge Engineering ◽  
Design Method ◽  
Mold Design ◽  
Engineering Technology ◽  
Knowledge Based ◽  
Knowledge Reasoning ◽  
Artificial Intelligence Technology ◽  
Development Tendency

Applying Artificial Intelligence technology on mold design can help realize the design automation of mold. Because knowledge-based engineering is an effective intelligent design method, the paper systematically introduced the application and development of knowledge representation, knowledge reasoning, knowledge acquisition and other key technologies of knowledge engineering technology used in mold design field. Finally, the development tendency of mold design based on artificial intelligence was analyzed in detail

Managing an Engineering Project

2016 ◽  
Vol 7 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Nayem Rahman ◽  
Alexis Wittman ◽  
Sallam Thabet
Keyword(s):  
Project Management ◽  
Construction Project ◽  
Project Planning ◽  
Engineering Project ◽  
Engineering Plant ◽  
Project Plan ◽  
Complex Engineering ◽  
Tools And Methods

This article provides an overview of the comprehensive process in creating a “Project Plan” for an engineering project. The authors discuss the challenges of project management tasks, tools, and methods used. They also discuss and compare other commonly used project planning practices and techniques. This article includes authors' experiences drawn from their careers and industries that are applicable to projects of this nature. They propose methodical approaches to handle a large, and complex engineering and construction project that takes several years to complete. The project selected is a hypothetical biomass engineering plant considered for this examination.

Decision Support Systems and their Application in Construction

2011 ◽  
pp. 276-298
Author(s):  
Alaa Abdou ◽  
Moh’d Radaideh ◽  
John Lewis
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support Systems ◽  
Construction Industry ◽  
Support Systems ◽  
Integrated Systems ◽  
The Internet ◽  
Research Projects ◽  
Knowledge Based ◽  
Potential Applications

Decisions are activities that we face and deal with every day. Decision support systems are used to support and improve decision making. They help people make better and faster decisions than they could make themselves. The construction industry witnessed a growth in the application of knowledge-based expert systems in the eighties and early nineties, followed by the application of fuzzy, artificial neural networks and hybrid (integrated) systems. Potential applications of the Internet in the construction industry have generated many research projects recently. The purpose of this chapter is to understand decision support systems and their basic technologies, and to review their application in the construction industry. The construction industry is rapidly realising the need to integrate information technology and artificial intelligence into its processes in order to remain competitive.

Decision Support Systems and their Application in Construction

2010 ◽  
pp. 1024-1042 ◽  
Author(s):  
Alaa Abdou ◽  
Moh’d Radaideh ◽  
John Lewis
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support Systems ◽  
Construction Industry ◽  
Support Systems ◽  
Integrated Systems ◽  
The Internet ◽  
Research Projects ◽  
Knowledge Based ◽  
Potential Applications

Decisions are activities that we face and deal with every day. Decision support systems are used to support and improve decision making. They help people make better and faster decisions than they could make themselves. The construction industry witnessed a growth in the application of knowledge-based expert systems in the eighties and early nineties, followed by the application of fuzzy, artificial neural networks and hybrid (integrated) systems. Potential applications of the Internet in the construction industry have generated many research projects recently. The purpose of this chapter is to understand decision support systems and their basic technologies, and to review their application in the construction industry. The construction industry is rapidly realising the need to integrate information technology and artificial intelligence into its processes in order to remain competitive.

Intelligent Expert Decision Support Systems

2021 ◽  
pp. 510-531
Author(s):  
Abdel-Badeeh M. Salem ◽  
Tetiana Shmelova
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support Systems ◽  
Knowledge Engineering ◽  
Support Systems ◽  
Case Based Reasoning ◽  
Web Based ◽  
Engineering Research ◽  
New Generation ◽  
Application Developers

In this chapter, the authors present Intelligent Expert Decision Support Systems (IEDSSs) technology and conceptual models of Expert systems(ES) for Human-Operator (H-O) of different areas and Air Navigation System (ANS) too. The authors demonstrate some interesting applications of IEDSS. Intelligent Expert Decision Support Systems technology is a challenging field that has witnessed great advances in the last few years. Artificial intelligence (AI) theories and approaches receive increasing attention within this emerging technology .Researchers have been used the AI concepts and theories to develop a robust generation of IEDSSs. Moreover, the convergence of AI technologies and web technologies (WT) is enabling the creation of a new generation of web-based IEDSSs for all domains and tasks. This chapter discusses the AI methodologies and techniques for developing the IEDSSs. Two most popular paradigms are discussed namely; case-based reasoning and ontological engineering. Moreover, the chapter addresses the challenges faced by the application developers and knowledge engineers in developing and deploying AI-based expert decision support systems. In addition, the chapter presents some examples of ES by the author and colleagues at National Aviation University, Ukraine and some cases of IEDSSs developed by the author and his colleagues at Artificial intelligence and Knowledge Engineering Research Labs, Ain Shams University, AIKE Labs-ASU, Cairo, Egypt.

scholarly journals THE SUCCESS OF ENTERPRISE SYSTEM PROJECTS: FROM A KNOWLEDGE TRANSFER PERSPECTIVE

2021 ◽  
Vol 6 (22) ◽  
pp. 128-147
Author(s):  
Jamal Hussien ◽  
Mansoor Abdullateef Abdulgabber ◽  
Hasan Kahtan ◽  
Riza Sulaiman
Keyword(s):  
Project Management ◽  
Knowledge Transfer ◽  
Project Planning ◽  
Business Systems ◽  
Project Proposal ◽  
Global Business ◽  
Implementation Phase ◽  
Knowledge Based ◽  
Two Phases ◽  
And Control

We have certainly already arrived in a knowledge-based world economy, where knowledge transfer is a crucial factor in global business competition. In the era of knowledge-based management, the way we use knowledge determines the success or failure of business systems. This paper revises the project phases of enterprise systems (ES), which have been divided into three phases (pre-implementation, during implementation, and post-implementation), by expanding the relationship between the Knowledge Transfer (KT) and Project Management Process Groups (PMPG) in each phase to improve the success of ES by increasing the understanding of knowledge in each ES phase. The pre-implementation phase has two phases: Project Origination with (3) PMPG (Develop Project Proposal, Evaluate Project Proposals, and Select Projects), (8) tasks, (8) deliverables, and (3) roles. Project Initiation phase, with (3) PMPG (Initiate the Project, Approve the Project Charter, and Conduct Kick-off Meeting), (7) tasks, (7) deliverables, and (4) roles. In the implementation phase of the project ES there are two phases: Project Planning with (3) PMPG (Prepare the Project Planning, Perform the Planning Activities - Detail the Project Plan, and Confirm Approval to Proceed), (19) Tasks, (21) Deliverables, and (7) Roles. Project implementation and control with (3) PMPG (Launch Project, Management Project-Execution and control, and Gain Project Acceptance), (17) tasks, (17) deliverables, and (7) roles. Additionally, in the post-implementation phase, there are a phase called project closure with (2) PMPG (Perform, Initiate Project Follow-up, and Administrative Closure), (5) tasks, (6) deliverables, and (5) roles.

Decision Support Systems and their Application in Construction

2011 ◽  
pp. 1285-1303
Author(s):  
Alaa Abdou ◽  
Moh’d Radaideh ◽  
John Lewis
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support Systems ◽  
Construction Industry ◽  
Support Systems ◽  
Integrated Systems ◽  
The Internet ◽  
Research Projects ◽  
Knowledge Based ◽  
Potential Applications

Decisions are activities that we face and deal with every day. Decision support systems are used to support and improve decision making. They help people make better and faster decisions than they could make themselves. The construction industry witnessed a growth in the application of knowledge-based expert systems in the eighties and early nineties, followed by the application of fuzzy, artificial neural networks and hybrid (integrated) systems. Potential applications of the Internet in the construction industry have generated many research projects recently. The purpose of this chapter is to understand decision support systems and their basic technologies, and to review their application in the construction industry. The construction industry is rapidly realising the need to integrate information technology and artificial intelligence into its processes in order to remain competitive.

scholarly journals Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

2019 ◽  
Vol 28 (01) ◽  
pp. 120-127 ◽  
Author(s):  
Stefania Montani ◽  
Manuel Striani
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Data Driven ◽  
Knowledge Based ◽  
Hybrid Approaches

Objectives: This survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to “classical" knowledge-based ones, and to consider the issues raised and their possible solutions. Methods: We included PubMed and Web of ScienceTM publications, focusing on contributions describing clinical DSSs that adopted one or more AI methodologies. Results: We selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data. Conclusions: Recent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.

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