A system of serial computation for classified rules prediction in non-regular ontology trees

Objects or structures that are regular take uniform dimensions. Based on the concepts of regular models, our previous research work has developed a system of a regular ontology that models learning structures in a multiagent system for uniform pre-assessments in a learning environment. This regular ontology has led to the modelling of a classified rules learning algorithm that predicts the actual number of rules needed for inductive learning processes and decision making in a multiagent system. But not all processes or models are regular. Thus this paper presents a system of polynomial equation that can estimate and predict the required number of rules of a non-regular ontology model given some defined parameters

Computational estimate visualisation and evaluation of agent classified rules learning system

Student modelling and agent classified rules learning as applied in the development of the intelligent Preassessment System has been presented in [10],[11]. In this paper, we now demystify the theory behind the development of the pre-assessment system followed by some computational experimentation and graph visualisation of the agent classified rules learning algorithm in the estimation and prediction of classified rules. In addition, we present some preliminary results of the pre-assessment system evaluation. From the results, it is gathered that the system has performed according to its design specification

Proposing goal refinement for multi-agent with agent UML tool for the control of explosive terror threats

One of the security challenges faced by our contemporary world is terror threats and attacks. This is posing threats to lives, properties and businesses in which Africa is no exception; and this has no doubt affected the way we live or travel. Terror attacks have been perpetrated in diverse ways either from organised terror networks through coordinated attacks or by some lone individuals. In that regards, this presentation proposes an agent oriented system analysis and design for the detection of potential chemical terror attacks. Agents are autonomous entities that are capable of observing their environment in which they are situated and act according to their design objectives. Through goals and plans, agents possess capabilities to perform actions. Exploring the Prometheus Design Tool (PDT), the paper describes the Prometheus three phases of intelligent systems design, refinement of goals for multi-agent organisation, situating agents in our cities, and inter-agent communication for the prevention of chemical explosives means of terror

An agent based approach for improvised explosive device detection, public alertness and safety

One of the security challenges faced by our contemporary world is terror threats and attacks, and this is no doubt posing potential threats to lives, properties and businesses all around us; affecting the way we live and also travel. Terror attacks have been perpetrated in diverse ways whether from organized terror networks through coordinated attacks or by some lone individuals such that it is now a major concern to people and government. Indeed, there are numerous forms of terror attacks. In this proposal, we look at how the explosive substance kind of threats can be perceived and taken care of prior to potential attacks using intelligent agent systems requirement analysis. Thus, the paper demonstrates using an agent-oriented system analysis and design methodology to decompose. Through defined percepts, goals and plans, agents possess capabilities to observe and perform actions. This proposal demonstrates: how agents can be situated in our cities, goal refinement for agents in the detection and rescue of potential terror attacks, and inter-agent communication for the prevention of chemical terror attack

Student modelling and classification rules learning for educational resource prediction in a multiagent system

To model support for human learning, rules (i.e. triggering event-conditions-actions) can be classified to encompass any state of student learning activity enroute to appropriate learning material prediction. In an agent based system, each component of an adaptive multiagent system can be represented as agents having individual autonomy and responsibility to realise the overall goal of the system. In this paper, we present an extended work on a multiagent based Pre-assessment System in which a modelling agent employs the technique of One v All Multiple Classification rules to make predictions for learning materials after some prerequisite assessment facts to a desired concept or topic are communicated by the pre-assessment agent. Using SQL ontology tree structure as the domain of learning content, a learning algorithm is described as a process for estimating the total number of classified rules required for the pre-assessment system. This estimate is proven to be dependent on: 1) two binary state values, 2) the number of leaf-nodes in the ontology tree, and 3) the number of prerequisite concept(s) to a desired concept. In addition, is the learning algorithm with which a modelling agent can increment or decrement its classified number of rules

Optimal recycle price game theory model for second-hand mobile phone recycling

Human societies develop rapidly through the advancement of technology; however, with these advancements, many problems are emerging. The topic chosen for this study surrounds the e-waste, which has become a major problem around the world. Second-hand and unused mobile phones are a big part of globally generated e-waste. If these devices are properly recycled, they can generate substantial economic and resource value. Yet if they are indiscriminately discarded, they cause a profound environmental impact. Given the current low recovery rate of mobile phones, an increase in recovery rates becomes critical in lessening economic and environmental impacts. Based on the status quo of second-hand mobile phone recycling processes in China, this article analyzes the behavior of individuals and recyclers through a comprehensive static information game theory and finds ways to increase the recycling rate of second-hand mobile phones. The study helps the customers, to clearly identify the recycle price. In case of market, the government policy can be introduced with a reward and punishment mechanism. Furthermore, under the ideological guidance of game theory, this paper also establishes a corresponding price model of second-hand mobile phone recycling based on best response dynamics like search, variable neighborhood search, and hybrid meta-heuristic method. This model shows that the recovery time differences have a significant impact on the recovery price. Moreover, to an extent, this model can promote the possibility and initiative of customers choosing cell phone recycling