Expert System for Drought Management Planning

An integrated expert decision support system for agricultural management

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

Expert systems (ES) are computer programs which make recommendations and draw conclusions from heuristic rules and from relationships derived out of human experience. A decision support system (DSS) is a computer-based management information system (MIS) linking one or more data bases to computer software for exploration and analysis of the data. Combined in a single system an ES and a DSS comprise an expert support system. A simple expert support system for use in fishery stock assessment and management is described. It demonstrates a number of practical benefits of the use of expert systems technology to implement and integrate some simple tasks, i.e., bioeconomic modelling, cost/benefit sampling analyses, and technical interpretation, namely: (1) the effects of different population structures and patterns of fishing on modelling results can be examined under the programmed guidance of the expert system; (2) the effects of different levels of sampling precision on modelling results can be similarly examined; (3) cost/benefit analyses of different sampling budgets can be examined; and (4) management advice specific to each sampling, surplus production (four different models), and net present value (NPV) case is given. An examination of the future of expert support systems in fishery stock assessment and management considers four possible levels of support, as follows: (1) to provide technical explanations, (2) to manage and catalog repetitive processes, (3) to guide “low-level decisions”, and (4) to contribute directly to strategic and policy decisions. Currently, movement in expert systems usage is toward so-called “embedded systems”, for example, an expert support system which comprises, in addition to computational models and databases, one or more expert systems. Problems related to the design and development of ESS for fishery stock assessment and management are challenging conceptual problems; the construction and programming of ESS are reasonably tractable tasks. Determining what an ESS will do is often the principle conceptual question. Expert support systems provide another tool for fishery stock assessment scientists and managers to increase their productivity.

Although the use of expert systems (ESs) as a problem-solving tool is relatively new in the transportation profession, it has been recognized that a well-designed ES can contribute in areas where knowledge domain requires an integration of expertise from various disciplines or is very complex and difficult to quantify. To minimize the effort involved in developing essential ES mechanisms, researchers often prefer to use ES shells which provide a standard inference procedure, knowledge representation, and a user-friendly design environment. However, commercial ES shells tend to be designed for only certain classes of problems, and are not as general and flexible as programming languages. More than 150 commercial ES shells are available in the microcomputer market. Most ES shells are ususally claimed by their vendors to be usable for almost any conceivable applications, but such claims have often proved to be extravagant. Hence, the selection of an efficient yet cost-effective commercial shell is one of the most critical tasks in ES development. In reviewing the the vital task of ES shell selection, this paper presents our experience in evaluating more than 30 ES shells for developing an ES for signal design. The proposed ES includes signal settings, phasing plans, controllers, and detector locations.

India is basically an agriculture country and the dairy sector plays a significant role in supplementing family income and generating employment in the rural areas, particularly among the landless, small and marginal farmers and farm women, besides providing cheap and nutritious food to millions of people. In India, the total milk production is extremely poor in terms of productivity in the world due to lack of proper extension system. Even though, information on various aspects of dairy cattle management is available, they are not reaching the required area of the farm units / farmers. To address this problem, a rule based expert system for dairy cattle management was developed with user friendly menus. This system interacts with users in two ways. As a decision support tool, the user can interact with the system using some arranged rules which are collection of if / then rules, collected from experts in the veterinary field. Using these rules, a knowledge base was designed for the expert system and programming codes were writeen in VB. Net. The proposed expert system provides instant access of the required knowledge on selected parameters, viz. feeding, breeding, diseases, cattle shed management, milking, fodder cultivation and health management. This expert system is useful as an online guide to the users who are involved in animal husbandry and dairy management.

In this paper we analyse the explanation facilities of several expert systems using a framework based on factors relevant to the structure and content of a generated explanation. We compare this with the lack of such facilities in expert system shells, and lay out criteria for an explanation facility within an expert system shell for education. We then describe their realisation in the explores (explanation oriented expert system) shell, where domain-independent templates allow questions to be asked about the domain of any knowledge base being consulted. In response, the shell generates domain-based explanations, with further information available through follow-up questions related to the original question, the generated explanation, and the available knowledge. This facility allows a user to explore and learn about a knowledge domain, and has implications for the use of expert system shells in education.

Expert systems in management and administration - are they really different from Decision Support Systems?

The management of the industrial production implies the use of some management systems and methods that take into account the nature of production, production type, production volume, the way of organising its performance, etc. Normally, the following can be used: cost objective management system; managerial cost management system; target cost management system; expert systems used in industrial production management. Within these systems and methods of management, the main issue is to compare the estimated costs with the target costs. The process of implementing the management system based on the costs is complex and implies: establishing the objectives, breaking down the objectives set by responsibilities, regularly analysing the results obtained, changing the objectives regarding the level of costs should it be needed, etc. The costs corresponding to the last batch, margins of production added or subtracted from the previous volume of costs are the marginal costs that differ from the average costs which concern the entire production of the industrial company. The size of the marginal cost may be established as a total differential cost or as a unitary differential cost, both being in mutual inter-conditioning relation. The expert system in the operational management of production can be used with good results, which is based on comparing some specific facts to those in the databases. Any expert system takes into account the existence of several modules, such as the knowledge base, rule base, knowledge purchase module, inference engine, inference mechanism, etc.

In Memoriam: Dr. Clive L. Dym (1942–2016)

The use of an expert system shell (EXPERTECH Xi Plus) in the construction of an expert system for the diagnosis of infertility has been evaluated. A module was devised for predicting ovulation from the medical history alone. Two versions of this system were constructed, one using the expert system shell, and the other using QuickBASIC. The two systems have been compared with respect to: (1) ease of construction; (2) ease of knowledge base update; (3) help and explanation facilities; (4) diagnostic accuracy; (5) acceptability to patients and clinicians; (6) user-friendliness and ease of use; (7) use of memory space; and (8) run time. The responses of patients and clinicians were evaluated by questionnaires. The predictions made by the computer systems were compared to the conclusions reached by clinicians and to the "gold standard" of day 21 progesterone. The conclusions of this pilot study are: (1) the construction of this expert system was NOT facilitated by the use of this expert system shell; (2) update of the knowledge base was not facilitated either; (3) the expert system shell offered built-in help and explanation facilities, but as the system increased in complexity these became less useful; (4) after initial adjustment of decision thresholds the diagnostic accuracy of the system equalled that of the clinician; (5) the patient response to computer history-taking was very favorable but much less favorable to computer diagnosis; (6) the clinicians took a positive attitude to computer diagnosis; (7) the systems were easy to use; (8) the expert systems shell required much more memory space and had a much slower response time than the system written in BASIC.

This article provides a computational framework to model self‐adaptive expert systems using the Petri net (PN) formalism. Self‐adaptive expert systems are understood here as expert systems with the ability to autonomously learn from external inputs, like monitoring data. To this end, the Bayesian learning principles are investigated and also combined with the Plausible PNs (PPNs) methodology. PPNs are a variant within the PN paradigm, which are efficient to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about a state variable. The manuscript shows the mathematical conditions and computational procedure where the Bayesian updating becomes a particular case of a more general basic operation within the PPN execution semantics, which enables the uncertain knowledge being updated from monitoring data. The approach is general, but here it is demonstrated in a novel computational model acting as expert system for railway track inspection management taken as a case study using published data from a laboratory simulation of train loading on ballast. The results reveal self‐adaptability and uncertainty management as key enabling aspects to optimize inspection actions in railway track, only being adaptively and autonomously triggered based on the actual learnt state of track and other contextual issues, like resource availability, as opposed to scheduled periodic maintenance activities.

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) is responsible for the safe operation and mission success of manned space flights. The MITRE Corporation, working with MOD, is developing requirements for the Operations Management System (OMS) to automate many aspects of flight control for the Space Station onboard systems. To help develop these requirements, the Integrated Status Assessment (ISA) expert system prototype was built to perform Station-wide failure diagnosis. This paper describes the OMS, how the ISA prototype was built, and how the prototype is being used to help define the OMS.Currently the Space Shuttle is managed on the ground using a hierarchy of control personnel. A flight director responsible for the overall Shuttle mission, a number of front room flight controllers are each responsible for a different system, and many back room controllers support various front room controllers. This system has been used since the early days of space flight and is very manpower intensive. A similar hierarchy is seen in an automated system for the Space Station. The back room controllers can be augmented with smart components and sensors on the Station and the front room controllers can have their jobs aided with expert systems. At the highest level is the OMS which works at the flight director level getting summary information from the various systems in the Station.The OMS will include onboard automation, ground based automation, onboard manual operations, and ground based operations. One of the functions of the OMS is to perform a Station-wide status assessment and failure diagnosis of the Station. This Station-wide view is very important because all of the systems interact with each other and a failure in one system will have impacts on the other systems.It became evident early in discussion with to the MOD flight controllers that prototypes would be very helpful in the process of gathering requirements for the OMS. The prototypes were used to gather additional requirements, to expose people to new ideas and technologies, and study the feasibility of these technologies for systems management.Because the task of assessing the status of space vehicles is a complex job that requires “expert” knowledge to find heuristic solutions to problems, an expert system approach was chosen to prototype the ISA system. The initial domain of the expert system was the KU band portion of the communications and tracking system (one of the onboard “core” Space Station Systems).The ISA prototype expert system consists of a knowledge base, an inference engine, and a user interface. The ISA system was designed as a hybrid expert system using different methodologies: object-oriented programming, rule-based programming using both shallow and deep reasoning, and qualitative modeling.The ISA prototype has been very successful in its goals. ISA has demonstrated new technology and ideas to the NASA operations community, helped define requirements for the OMS, and shown the feasibility of using hierarchical expert systems to monitor complex systems. The ISA prototype, along with other OMS prototypes, are being integrated in a test bed environment with other space station prototypes to form the Space Station Information System (SSIS) End-to-end Test Capability (ETC) which will have components all over the world. The results of the ETC will influence the Space Station design and be a test environment for software and hardware for years to come.

Nowadays, expert systems being used in various fields have received a great deal of attention. Auditing is one such field, along with determining the audit opinion type. An expert system consists of a knowledge database and an inference engine. The objective of this research is to make an expert system that will be of help to auditors in predicting and determining the different types of audit reports. The expert system receives data or knowledge from financial reports and determines the types of audit opinions by using an artificial neural network and a decision tree as an inference engine. An expert system should be able to explain the solution, but presenting the reason for the results obtained with a neural network is difficult. This study attempts to provide a method that will present simple and understandable reasons for the results obtained with neural networks.

Rule-based systems are relatively easier to explain to end users because rules can be expressed as if/else conditions. Indeed, at times there are multiple if/else conditions. Expert systems are deterministic in nature and very accurate, because they are driven by well-established rules and conditions. The need for explainability arises when multiple rules either conflict or are combined in such a way that it is difficult to interpret. In the field of artificial intelligence, an expert system is a program designed to simulate a real decision making process. Expert systems are designed to solve complex problems by simple reasoning and are represented as if/else/then conditions. Expert systems are designed to act like an inference engine. The role of an inference engine is to make decisions. Sometimes expert systems are simple, like a decision tree, and sometimes expert systems are very complex. Complex expert systems require a knowledge base. Sometimes these knowledge bases are an ontology, which is a form of a body of knowledge, through which a particular rule can be framed and inferencing can be done.

Expert systems in agriculture and resource management