Research on Knowledge Representation and Acquisition Based on Dynamic Regional Grid Net Antagonistic Project

The extraction of knowledge from domain experts for the purpose of building expert systems has been found to be a difficult and a time- consuming enterprise. Researchers have therefore looked at the - possibility of automating this task of knowledge acquisition. The existing knowledge acquisition systems rely on either some task-specific knowledge or general techniques for knowledge elicitation for obtaining problem-solving expertise from domain experts. Generally, systems which employ task-specific knowledge produce better problem-solving expertise than those systems which use elicitation techniques. However, the scope of applicability of former systems is relatively narrow. This is the central problem addressed in this thesis: to design a knowledge acquisition system which can be applied over a wide range of tasks with the purpose of acquiring useful problem-solving knowledge. The thesis presents a methodology and a system for knowledge acquisition called ASKE. The methodology prescribes that knowledge acquisition should start by defining the task and then use the developed task-model to acquire domain specific knowledge. ASKE is able to support this process by allowing the user to construct task-models and by being able to effectively use them for acquiring domain expertise. The advantage of progressing in this manner is two-fold: firstly, it widens the scope of applicability of the knowledge acquisition system; and, secondly, it makes possible the construction of knowledge-bases that exhibit expert performance. ASKE contains knowledge engineering expertise which it uses to help domain experts encode their problem-solving expertise directly into a knowledge-base. The system derives its power from the templates which encode knowledge. The templates serve a triple function: they represent knowledge - their normal function; they encode expectations of the kind of knowledge that is to be acquired; and, they serve as a guide for how problem-solving knowledge may be organized so as to facilitate its encapsulation into a knowledge-base.

A New Approach In Learning For Intelligent Multi Agent Systems

SummaryKnowledge acquisition for expert systems typically is a tedious, iterative process involving long hours of consultation between the domain experts and the computer scientists who serve as knowledge engineers. For well-understood domains, however, it may be possible to facilitate the knowledge acquisition process by allowing domain experts to develop and edit a knowledge base directly. Administration of protocol-directed cancer chemotherapy is such a well-understood application area, and a knowledge acquisition system, called OPAL, has been developed for eliciting chemotherapy-protocol knowledge directly from expert oncologists. OPAL’s knowledge acquisition approach is based on the interactive graphics environment available on current generation workstations. The use of graphics improves the interface by reducing typing, avoiding natural language interpretations, and allowing flexibility in entry sequence. The knowledge in OPAL is displayed using an arrangement of hierarchically related, graphical forms. The position of a particular form in the hierarchy defines the context of the knowledge contained in the form. Intelligent editing programs such as OPAL can streamline the knowledge engineering process for highly structured domains requiring repetitive knowledge entry.

The design of Renewable Energy Power Plants (REPPs) is crucial not only for the investments' performance and attractiveness measures, but also for the maximization of resource (source) usage (e.g. sun, water, and wind) and the minimization of raw materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te) consumption. Hence, several appropriate and satisfactory Multi-Objective Problems (MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only be managed by very well organized knowledge acquisition on all REPPs' design equations and models. The proposed MOPs need to be solved with one or more multi-objective algorithm, such as Multi-Objective Evolutionary Algorithms (MOEAs). In this respect, the first aim of this research study is to start gathering knowledge on the REPPs' MOPs. The second aim of this study is to gather detailed information about all MOEAs and available free software tools for their development. The main contribution of this research is the initialization of a proposed multi-objective evolutionary algorithm knowledge acquisition system for renewable energy power plants (MOEAs-KAS-F-REPPs) (research and development loopwise process: develop, train, validate, improve, test, improve, operate, and improve). As a simple representative example of this knowledge acquisition system research with two selective and elective proposed standard objectives (as test objectives) and eight selective and elective proposed standard constraints (as test constraints) are generated and applied as a standardized MOP for a virtual small hydropower plant design and investment. The maximization of energy generation (MWh) and the minimization of initial investment cost (million €) are achieved by the Multi-Objective Genetic Algorithm (MOGA), the Niched Sharing Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all proposed standardized MOEAs on two desktop computer configurations (Windows 10 Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB RAM with internet connection). The algorithm run-times (computation time) of the current applications vary between 20.64 and 59.98 seconds.

This research addresses the widening gap between research in intelligent tutoring systems and practical use of this technology by the educational community. In order to insure that intelligent tutoring systems (ITSs) are effective, teachers must be involved in their design and evaluation. We have followed a user participatory design process to build a set of ITS knowledge acquisition tools tailored for usability by teachers. The system facilitates rapid prototyping and testing of curriculum and multiple tutoring strategies. Teachers use the system to create, modify, and test the system's domain content and tutoring strategy knowledge bases. The design includes novel methodologies for strategy representation and overlay student modeling, and incorporates considerations from instructional design theory. Tools have been designed to provide the user with visual models of the concepts and structures of the underlying framework. In close collaboration with a veteran high school teacher, we have used the interface to design a tutor for statics (part of a high school physics course). In this paper we describe the system (called KAFITS), report on our experience involving educators in ITS development, discuss issues of ITS knowledge representation and acquisition, and compare the system with related research in generic tutoring systems and knowledge acquisition.

Representing strategic design knowledge

Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of "big data" provides unprecedented opportunities for data-driven research and "big picture" models. However, in-depth analyses-making use of various data types and data sources and extracting knowledge-have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.

KRITON: a knowledge-acquisition tool for expert systems

The applications of Petri net modeling to the design and operations of sequential control systems (SCSs) are widely developed. Automatic Petri net generation is also possible. The bottleneck in SCS development is the acquisition of knowledge, as the adaptation and utilization of experience have not been done smoothly. The objective of this work is to simplify the acquisition of knowledge and the transition from the specification of the SCS design to Petri net modeling. A Petri-net-based representation is used to explain the acquisition of knowledge in SCSs by learning and to develop a system for Petri net generation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Knowledge Acquisition has historically been a bottleneck in the development of knowledge based systems. While efficient and powerful techniques have been developed for knowledge representation and processing, transferring knowledge from the problem domain into the knowledge base is characterized by the inordinate effort required by knowledge engineers; a lack of any substantial automation of the knowledge acquisition process; and in particular, the difficulty and undue complexity of the knowledge acquisition interfaces for many knowledge based systems. This paper proposes a Natural Language Interface or Front-End as a means of effective knowledge acquisition for knowledge based systems. This would have the advantage of transferring the burden of knowledge acquisition from the user to the system, while providing ease of natural language interaction to the user. By constraining such a front-end to use within limited problem domains, many of the difficulties associated with natural language processing may be avoided, while reaping the benefits of more effective knowledge acquisition. It is also recognized that the knowledge acquisition activity is one that possesses expertise of itself, and using a knowledge acquisition expert system in parallel to this front-end will likely offer further advantages in the development of part or wholly-automated knowledge acquisition.

Considering the lack of visualization technology applications and e-learning in the current digital library construction, a visualization knowledge acquisition and knowledge innovation prototype system is designed in the paper. The visualization display and visualization mapping are used in the prototype system, and it realizes a complete knowledge chain from information extraction, knowledge acquisition, knowledge construction and knowledge innovation to userspsila knowledge storage. It may provide ideas of applying visualization technology in digital library.

In dynamic environments (e.g. an Air Operations Center (AOC)), effective decision-making is highly dependent on situation awareness (SA). SA is formally defined as a person's perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future. Belief networks (BNs) are an ideal tool for modeling and meeting the requirements of tactical situation awareness. BNs emulate a skilled human's information fusion and reasoning process in a multi-task environment in the presence of uncertainty. While belief networks offer significant advantages for SA, a key drawback to their use is the daunting issue of how the requisite knowledge is captured or elicited to both build the network and populate the Conditional Probability Tables (CPTs). To address this issue, we have built the Situation Awareness and Knowledge Acquisition (OSAKA) system. This system consists of two parts: development of a library of BN components that can be combined to describe different air operation situations and enhancing our BNet:Builder© toolkit to learn CPT values for these components based on data obtained from running an external simulation. This allows the initial CPT values obtained from our Subject Matter Expert to be tuned based on what actually happens. The ability to tune the network over time can aid in supplying information not initially available when constructing the network and to help ensure that it continues to provide current, useful, information.

This study investigated knowledge acquisition and transfer system among traditional crafts in Bida and Gusau towns in Northern Nigeria. Five research questions guided the study and include variables such as types of crafts practiced, sources of knowledge and skills; people involved sustainability and challenges to traditional crafts in the two towns. Case study was used with population of all traditional craftsmen in Bida and Gusau (Niger and Zamfara States) respectively. A sample size of 90 craftsmen was drawn through stratified random sampling. Descriptive statistics involving frequency counts and percentages were used to analyze the collected data. Results showed that embroidery and blacksmithing were the major crafts practiced; knowledge and skills were obtained through inheritance/initiation and that the unemployed were the major craftsmen. Other findings include ‘no alternative’ jobs as a factor that sustain traditional crafts and that limited capital was the major obstacle to traditional crafts in the two towns. The study recommended amongst others the inclusion of limitless type of crafts to various skills through formal education, vocational training, apprenticeship, etc.&#x0D; Keywords: Traditional crafts, Apprenticeship, Knowledge, Skills, Nigeria

Explicit schematic information in knowledge representation and acquisition

Before a future with household robots is really feasible, those robots need to be easily adaptable to novel environments and users, be able to apply previously acquired knowledge, and able to learn from perceiving and interacting with the world and users around them. This thesis proposes a cognitive architecture and a set of underlining methods for automatic knowledge acquisition that will bring household robots one step closer to reality. The thesis was inspired by the cognitive development of babies and subsequently treats the topics: Knowledge representation, Sensory motor integration, Knowledge acquisition & learning and Real world applications. We derived ways to describe and efficiently combine visual information, despite cluttered environments and changing illumination. We propose a method to combine different key-point extractors and to select only a small subset based on their information entropy. Since object appearances can vary in colour, texture and 2D/3D shape, we extract feature vectors from multiple viewpoints of the object and calculate the dominant features, which leads to a more robust object recognition process. This also automatically calculates the most efficient way to store the representative features of unknown objects. This method is similar to the information storage process in humans; an important characteristic of human learning is the pruning of information in the working memory to the most relevant information to be stored in long term memory. In order to detect and segment objects without any prior knowledge on their appearance or their background, we designed a visual-attention module that is able to find the salient parts of an image and clusters them into objects ranked by their saliency. Once these objects are localized, the system must act upon them to efficiently learn their properties. This is achieved by simultaneous exploration and manipulation of the selected - most salient - unknown object. However, such a cognitive system cannot be realized without a robot body, and therefore we designed a simple and affordable robot. Another cognitive ability that a future household robot certainly must have is knowledge accumulation from previous experiences. Consequently, we designed a cognitive architecture that allows a robot that has no pre- insight in the world and its objects to incrementally acquire all its expertise from interaction with the environment. We proposed models of working and long term memory as well as algorithms for novelty detection. Finally, we treat two realized applications for external parties, based on our algorithms: Bin-picking by an industrial robot and action detection in ceiling mounted cameras in elderly homes.