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

Wireless Sensor Networks And Data Fusion For Structural Health Monitoring Of Aircraft

This thesis discusses an architecture and design of a sensor web to be used for structural health monitoring of an aircraft. Also presented are several prototypes of critical parts of the sensor web. The proposed sensor web will utilize sensor nodes situated throughout the structure. These nodes and one or more workstations will support agents that communicate and collaborate to monitor the health of the structure. Agents can be any internal or external autonomous entity that has direct access to affect a given system. For the purposes of this document, an agent will be defined as an autonomous software resource that has the ability to make decisions for itself based on given tasks and abilities while also collaborating with others to find a feasible answer to a given problem regarding the structural health monitoring system. Once the agents have received relevant data from nodes, they will utilize applications that perform data fusion techniques to classify events and further improve the functionality of the system for more accurate future classifications. Agents will also pass alerts up a self-configuring hierarchy of monitor agents and make them available for review by personnel. This thesis makes use of previous results from applying the Gaia methodology for analysis and design of the multiagent system

Wireless Sensor Networks And Data Fusion For Structural Health Monitoring Of Aircraft

This thesis discusses an architecture and design of a sensor web to be used for structural health monitoring of an aircraft. Also presented are several prototypes of critical parts of the sensor web. The proposed sensor web will utilize sensor nodes situated throughout the structure. These nodes and one or more workstations will support agents that communicate and collaborate to monitor the health of the structure. Agents can be any internal or external autonomous entity that has direct access to affect a given system. For the purposes of this document, an agent will be defined as an autonomous software resource that has the ability to make decisions for itself based on given tasks and abilities while also collaborating with others to find a feasible answer to a given problem regarding the structural health monitoring system. Once the agents have received relevant data from nodes, they will utilize applications that perform data fusion techniques to classify events and further improve the functionality of the system for more accurate future classifications. Agents will also pass alerts up a self-configuring hierarchy of monitor agents and make them available for review by personnel. This thesis makes use of previous results from applying the Gaia methodology for analysis and design of the multiagent system

Computational Modeling of Trust Factors Using Reinforcement Learning

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous decision-making algorithms. The approach relies on the implementation of a neural network based reinforcement learning paradigm known as adaptive critic design to model an adaptive decision making process that is regulated by a quantitative measure of risk associated with each possible decision. Specifically, this work expands on the risk-directed exploration strategies of reinforcement learning to obtain quantitative risk factors for an automated object recognition process in the presence of imprecise data. Accordingly, this work addresses the challenge of automated risk quantification based on the confidence of the decision model and the nature of given data. Additionally, further analysis into risk directed policy development for improved object recognition is presented

Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop

Although information workers may complain about meetings, they are an essential part of their work life. Consequently, busy people spend a significant amount of time scheduling meetings. We present Calendar.help, a system that provides fast, efficient scheduling through structured workflows. Users interact with the system via email, delegating their scheduling needs to the system as if it were a human personal assistant. Common scheduling scenarios are broken down using well-defined workflows and completed as a series of microtasks that are automated when possible and executed by a human otherwise. Unusual scenarios fall back to a trained human assistant who executes them as unstructured macrotasks. We describe the iterative approach we used to develop Calendar.help, and share the lessons learned from scheduling thousands of meetings during a year of real-world deployments. Our findings provide insight into how complex information tasks can be broken down into repeatable components that can be executed efficiently to improve productivity.Comment: 10 page

Towards personalised and adaptive QoS assessments via context awareness

Quality of Service (QoS ) properties play an important role in distinguishing between functionally-equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, utilising the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been utilised to a limited extent by existing approaches. In response, we propose a context-aware quality learning model, realised via a learning-enabled service agent, exploiting the contextual characteristics of the domain in order to provide more personalised, accurate and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments

Aisimam - An Artificial immune system based intelligent multiangent model

The goal of this thesis is to develop a biological model for multiagent systems. This thesis explores artificial immune systems, a novel evolutionary paradigm based on the immunological principles. Artificial Immune systems (AIS) are found to be powerful to solve complex computational tasks. The main focus of the thesis is to develop a generic mathematical model that uses the principles of the human immune system in multiagent systems (MAS). The components and properties of the human immune system are studied. On understanding the concepts of A/5, a literature survey of multiagent systems is performed to understand and compare the multiagent concepts and AIS concepts. An analogy between the immune system parameters and the agent theory was derived. Then, an intelligent multiagent model named AISIMAM is derived. It exploits several properties and features of the immune system in multiagent systems. In other words, the intelligence of the immune systems to kill the antigen and the characteristics of the agents are combined in the model. The model is expressed in terms of mathematical expressions. The model is applied to a specific application namely the mine detection and defusion. The simulations are done in MATLAB that runs on a PC. The experimental results of AISIMAM applied to the mine detection problem are discussed. The results are successful and shows that AISIMAM could be an alternative solution to agent based problems. Artificial Immune System is also applied to a pattern recognition problem. The problem experimented is a color image classification problem useful in a real time industrial application. The images are those of wooden components that need to be classified according to the color and type of wood. To solve the classification task, a simple negative selection and genetic algorithm based A/5 algorithm was developed and simulated. The results are compared with the radial basis function approach applied to the same set of input images