No abstract

Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases.

On our everyday operations there is need to engage agents to perform some duties on our behalf, hence they are gaining acceptance as a technology and are being used. Most of the networked offices, networked homes, cyber cafes, learning institutions and other arenas where computers are interconnected on a Wi-Fi network, have peer-to-peer networks. In Wi-Fi peer-to-peer networks, it is difficult to identify the network details of all the network devices connected such as the IP addresses, Mac addresses and computer names of all computers connected on the Wi-Fi peer to peer network at one go; which we hereby refer to as fundamental network details. This is mainly possible in a client-server based architecture where the server monitors all the computers on the network. From the above gap, we developed a mobile agent that could be run in any computer on the Wi-Fi peer-to-peer network and it lists these fundamental details of all the computers connected to the Wi-Fi peer-to-peer network. In developing this mobile agent, we used the MaSE agent methodology. The mobile agent was coded, implemented and tested. The agent was then subjected to various controls which it overcame and managed to return the desired fundamental details with over 80% accuracy. They had the capacity to classify every computer on the network as either intruder or non intruder based on the list of authorized computers supplied by the user. The agent suffered major limitation which included: -the agent took longer time to learn and return the results, as well as it could not communicate to the intruders or shut them down. In future, the agent could therefore be improved to reduce its processing time, communicate with the intruding computers, and shut down the intruding computers or deny them network access. General TermsMultiagent systems, computer networking

In our daily engagement with technology we interact with software in many aspects, rendering software engineering field a very robust area with a lot of dynamism. In this paper we have comprehensively surveyed on software engineering approaches for designing knowledge based systems. First we looked at knowledge based systems in detail, then at software engineering basics, and finally match the various software engineering approaches that have been used in the field of knowledge based systems in the current research and surveys. Some of the current approaches in knowledge approaches that we surveyed include: case-based approach, software reuse approach, Model driven software engineering approaches, ontology based approach, Open metadata approach, cloud based approach, agile approach, Traditional integration approach, Automatic software generation code approach, process based approach, and the Knowledge based system approach.

On our daily life, we need to engage with our neighbors on several issues ranging from social, security and general neighborhood wellbeing. In this regard, we have designed a "nyumba kumi" information system which uses artificial intelligence and the facts stored in its knowledge base to answer various "nyumba kumi" neighborhood queries. We collected a sample "nyumba kumi" concerns and converted them into facts and the system rules. Hence, we fed the system with sample facts converted into predicate logics, which we collected from a certain "nyumba kumi" neighborhood. The system is able to answer various neighborhood queries using artificial intelligence knowledge from the facts and rules fed into the system, which we designed using prolog. The system could be customized by adding as many facts and rules as possible as long as the facts and rules are not contradicting. General TermsArtificial intelligence, "nyumba kumi" (ten homes), KeywordsArtificial intelligence, prolog, neighborhood information system, "nyumba kumi"