published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

Over the past decade, two types of decision aids, i.e., decision support systems (DSS) and expert systems (ES), have been developed along parallel paths, showing some significant differences in their software architectures, capabilities, limitations, and other characteristics. The synergy of DSS and ES, however, has great potential for helping make possible a generalized approach to developing a decision aid that is powerful, intelligent, and friendly. This research establishes a framework for such decision aids in order to determine the elementary components and their interactions. Based on this framework, a generalized intelligent problem solving system (GIPSS) is deveolped as a decision aid generator. A relational model is designed to provide a unified logical view of each type of knowledge including factual data, modeling knowledge, and heuristic rules. In this knowledge model, a currently existing relational DBMS, with some extension, is utilized to manage each type of knowledge. For this purpose a relational resolution inference mechanism has been devised. A prototype GIPSS has been developed based on this framework. Two domain specific decision aids, COCOMO which estimates software development effort and cost, and CAPO which finds optimal process organization, have been implemented by using the GIPSS as a decision aid generator, demonstrating such features as its dynamic modeling capabilities and learning capabilities.

This report addresses the research applied towards the automation of the engineering design process, in particular the structural design process. The three important stages of the structural design process are: the specifications, preliminary design and the detailed design. An iterative redesign architecture of the structural design process lends itself to automation. The automation of the structural design can improve both the cost and the reliability, and enhance the productivity of the human designers. To the extent that the assumptions involved in the design process are explicitly represented and automatically inforced, the design errors resulting from the violated assumptions can be avoided. Artificial Intelligence (AI) addresses the automation of complex and knowledge-intensive tasks such as the structural design process. It involves the development of the Knowledge Based Expert System (KBES). There are several tools, also known as expert shells, and languages available for the development of knowledge-based expert systems. A general purpose language, called LISP, is very popular among researchers in AI and is used as an environmental tool for the development of the KBES for the structural design process. The resulting system, called Expert-SEISD, is very generic in nature. The Expert-SEISD is composed of the user interface, inference engine, domain specific knowledge and data bases and the knowledge acquisition. The present domain of the Expert-SEISD encompasses the design of structural components such as beams and plates. The knowledge acquisition module is developed to facilitate the incorporation of new capabilities (knowledge or data) for beams, plates and for new structural components. The decision making is an integral part of any design process. A decision-making model suitable for the specifications extraction and the preliminary design phases of the structural design process is proposed and developed based on the theory of fuzzy sets. The methods developed here are evaluated and compared with similar methods available in the literature. The new method, based on the union of fuzzy sets and contrast intensification, was found suitable for the proposed model. It was implemented as a separate module in the Expert-SEISD. A session with the Expert-SEISD is presented to demonstrate its capabilities of beam and plate designs and knowledge acquisition.

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Įmonių sėkmė labai priklauso nuo veiklos organizavimo strategijos, metodikos ir principų bei vadovų priimamų sprendimų savalaikiškumo. Kad įmonės personalas sugebėtų laiku priimti strateginius sprendimus, reikia kuo operatyviau reaguoti į įmonės veiklą lemiančius dinaminius mikro ir makro aplinkos veiksnius, juos sisteminti bei analizuoti turimą ir naujai gautą informaciją. Šioje srityje labai svarbi organizaciją pasiekiančių informacijos srautų judėjimo greičio bei apdorojimo kokybė. Informacijos cirkuliacija rinkoje, jos įsisavinimas ir kryptingas panaudojimas priklauso ne tik nuo pačių informacijos vartotojų, bet ir nuo rinkoje sudarytų sąlygų šiai informacijai gauti ir naudotis. Dinamiškoje rinkos bei žinių ekonomikos aplinkoje labai reikšmingi tampa sprendimų priėmimo ir greito bei tikslaus reagavimo į rinkos pokyčius įgūdžiai, kuriuos kiekvienas verslininkas turėtų nuolat lavinti. Juk kiekvienos verslo organizacijos sugebėjimą dirbti pelningai ir būti konkurencingais lemia žinios apie tam tikrus rinkos veiksnius ir padėtį joje. / In a dynamic market environment and economy it is becoming a very important to be able to react immediately to the changes, that occurs in companies environment. Moreover, every business organization's ability to operate profitably and to be competitive depends on knowledge of certain market factors and situation there. Trying to reach the highest company management and decision-making quality is very useful and important for companies to make conditions for staff to train in business management. This type of trainings should be implemented in the way of using software for business modelling .This type of software would make it possible to educate business leaders and company decision-makers. In order to maximize the system usefulness, it is appropriate to use the expert systems. Expert system gives the opportunity to evaluate how unexpected factors can affect the company and its profit. Expert system helps to adopt theoretical knowledge in practical situations, so in this way the theoretical knowledge would be used in company administration process. In nowadays markets there is no public available business modelling software. So it is very topical to create this type of system. Finally, it was created business management modelling software that is orientated to educational process. The usage of an expert systems made up the opportunity to relate theoretical knowledge to practical experience.

In many settings, agents engage in problem-solving activities, which require them to share resources, act on each others behalf, coordinate individual acts, etc. If autonomous agents are to e ectively interact (or support interaction among humans) in situations such as deciding whom and how to approach the provision of a resource or the performance of an action, there are a number of important questions to address. Who do I choose to delegate a task to? What do I need to say to convince him/her to do something? Were similar requests granted from similar agents in similar circumstances? What arguments were most persuasive? What are the costs involved in putting certain arguments forward? Research in argumentation strategies has received signi cant attention in recent years, and a number of approaches has been proposed to enable agents to reason about arguments to present in order to persuade another. However, current approaches do not adequately address situations where agents may be operating under social constraints (e.g., policies) that regulate behaviour in a society. In this thesis, we propose a novel combination of techniques that takes into consideration the policies that others may be operating with. First, we present an approach where evidence derived from dialogue is utilised to learn the policies of others. We show that this approach enables agents to build more accurate and stable models of others more rapidly. Secondly, we present an agent decision-making mechanism where models of others are used to guide future argumentation strategy. This approach takes into account the learned policy constraints of others, the cost of revealing in- formation, and anticipated resource availability in deciding whom to approach. We empirically evaluate our approach within a simulated multi-agent frame- work, and demonstrate that through the use of informed strategies agents can improve their performance.

The thesis investigates two domains, initially the kite domain and then part of a more demanding Rolls-Royce domain (jet engine design). Four main types of refinement rules that use the associated application conditions and domain ontology to support the maintenance of constraints are proposed. The refinement rules have been implemented in ConEditor and the extended system is known as ConEditor+. With the help of ConEditor+, the thesis demonstrates that an explicit representation of application conditions together with the corresponding constraints and the domain ontology can be used to detect inconsistencies, redundancy, subsumption and fusion, reduce the number of spurious inconsistencies and prevent the identification of inappropriate refinements of redundancy, subsumption and fusion between pairs of constraints.

When a tropical cyclone (TC) is within 360 nautical miles of Kadena AB, the Air Force's Typhoon Determination (TYDET) program is used to estimate TC-induced winds expected at the base. Best-track data and Joint Typhoon Warning Center (JTWC) forecasts are used to evaluate systematic errors in TYDET. The largest contributors to errors in TYDET are a systematic error by which wind speeds are too large and the lack of size and symmetry parameters. To examine these parameters, best-track and forecasts are used to classify TCs as small or large and symmetric or asymmetric. A linear regression technique is then used to adjust TYDET forecasts based on the best-track and forecast position, size, and symmetry categories. Using independent data, over 65 percent of the overall cross-wind forecasts were improved and more than 60 percent of the cross-wind forecasts were improved when verifying conditions noted a cross-wind of 20 knots or greater. The effectiveness of the corrections and implications for TYDET forecasts are examined in relation to errors in forecast data used to initialize TYDET. A similar approach as developed here for the TYDET model at Kadena AB is proposed for other bases within the Pacific theater.

D.Ing. (Electrical and Electronic Engineering) / The electricity shortage in South Africa has required the implementation of demand side management (DSM) projects. The DSM projects were implemented by installing energy monitoring and control systems to monitor certain mining aspects such as water pumping systems. Certain energy saving procedures and control systems followed by the mining industry are not sustainable and must be updated regularly in order to meet any changes in the water pumping system. In addition, the present water pumping, monitoring, and control system does not predict the energy consumption or the underground water dam levels. Hence, there is a need to introduce new monitoring system that could control and predict the energy consumption of the underground water pumping system and dam levels based on present and historical data. The work is undertaken to investigate the feasibility of using artificial intelligence in certain aspects of the mining industry. If successful, artificial intelligence systems could lead to improved safety and reduced electrical energy consumption, and decreased human error that could occur throughout the pump station monitoring and control process ...