This thesis presents a study into conflicts that emerge amongst sensor device rules when such devices are formed into networks. It describes conflicting patterns of communication and computation that can disturb the monitoring of subjects, and lower the quality of service. Such conflicts can negatively affect the lifetimes of the devices and cause incorrect information to be reported. A novel approach to detecting and resolving conflicts is presented. The approach is considered within the context of home-based psychiatric Ambulatory Assessment (AA). Rules are considered that can be used to control the behaviours of devices in a sensor network for AA. The research provides examples of rule conflict that can be found for AA sensor networks. Sensor networks and AA are active areas of research and many questions remain open regarding collaboration amongst collections of heterogeneous devices to collect data, process information in-network, and report personalised findings. This thesis presents an investigation into reliable rule-based service provisioning for a variety of stakeholders, including care providers, patients and technicians. It contributes a collection of rules for controlling AA sensor networks. This research makes a number of contributions to the field of rule-based sensor networks, including areas of knowledge representation, heterogeneous device support, system personalisation, and in particular, system reliability. This thesis provides evidence to support the conclusion that conflicts can be detected and resolved in adaptable rule-based sensor networks.

Knowledge representation and inference are the backbone of artificial intelligence, and logic programming is one of the most widely used knowledge representation tools. Logic programming with deduction/induction/abduction as the reasoning technique is serving numerous fields of artificial intelligence. In dynamic domains where there are constant changes in knowledge, updating the knowledge base is crucial to keep it stable. This thesis investigates the issues in updating the knowledge base. Two types of logic program based updates are considered, simple fact based updates where the knowledge base is updated by a simple fact, and rule based updates where the knowledge base is updated by a rule. A SLDNF based procedural approach is proposed to implement such updates. This thesis also investigates the issues involved in simple fact based and rule based abduction, and it is observed that updates are closely related to abduction. A SLDNF based procedural approach to perform simple fact/rule based updates and abduction is proposed as a result of this study / Master of Science (Hons)

Thesis (M. Sc.(Computer Science))--University of Pretoria, 2003. / Includes bibliographical references.

Thesis (M.S.)--Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Expert system techniques are now being incorporated successfully in commercial computer software packages. This thesis introduces the techniques of developing a rule-based expert system in a general-purpose programming language -- C. The topics of this thesis include significance of expert system shells and approaches used, structure of the knowledge base, loading of the knowledge base, manipulation of the probabilities of rule attributes, and implementation of the inference engine. The inference engine uses the information that users supply to find an object that matches. Today C language is one of the most popular programming languages in use and C compilers consistently produce extremely fast and efficient executable programs. Thisthesisdemonstrates that C language is an appropriate computer language for a rule-based expert system. / Department of Computer Science

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: Intelligent Tutoring Systems; Model Tracing; Machine Learning; Artificial Intelligence; Programming by Demonstration. Includes bibliographical references.

Thesis (M.Sc. (Hons.)) -- University of Western Sydney, 2001. / "A thesis submitted for the degree of Master of Science (Honours) - Computing and Information Technology at University of Western Sydney" Bibliography : leaves 93-98.

Univ., Diss.--Bern, 2004.

Rule-based systems are tested by developing a set of inputs which will produce already known outputs. The problem with this form of testing is that the system code is not considered when generating test cases. This makes software testing completeness difficult to measure. This is important because all the computational models are constructed within the code. Therefore, to show the models of the system are tested, it must be shown that the code is tested. Chem uses the Loci rule-based application framework to build computational fluid dynamics models. These models are tested using the Quick Test suite. The data flow structure built by Loci, along with Quick Test, provided a case study for the research. The test suite was compared against three levels of coverage. The measures indicated that the lowest level of coverage was not achieved. This shows us that structural coverage measures can be utilized to measure rule-based system testing completeness.

The purpose of this research was to apply machine learning techniques to automate rule generation in the construction of Intelligent Tutoring Systems. By using a pair of somewhat intelligent iterative-deepening, depth-first searches, we were able to generate production rules from a set of marked examples and domain background knowledge. Such production rules required independent searches for both the“if" and“then" portion of the rule. This automated rule generation allows generalized rules with a small number of sub-operations to be generated in a reasonable amount of time, and provides non-programmer domain experts with a tool for developing Intelligent Tutoring Systems.