Genetic Algorithm to Generate the Automatic Time-Table – An Over View

In this paper we glance through the various approaches used by the researchers to develop an automatic timetable using Genetic algorithms. The optimized genetic algorithm can be used with the heuristic approach to design and develop the timetable of an institute. At stake during the process of development, the stakeholders are the professors and the students. The efficient utilization of the infrastructure is the main aim of the authors. The crossover, mutation and the fitness function is to be calculated for the implementation. In genetic algorithm every individual are characterized by a fitness function. After analysis if there is higher fitness then it means better solution and then after based on their fitness, parents are selected to reproduce offspring for a new generation where fitter individuals have more chance to reproduce. The objective of the work is to create a model used to generate the acceptable schedule using probabilistic operators

A SURVEY OF THE PROPERTIES OF AGENTS

In the past decade agent systems were considered to be as one of the major fields of study in Artificial Intelligence (AI) field. Many different definitions of agents were presented and several different approaches describing agency can be distinguished. While some authors have tried to define “what” an agent really is, others have tried to identify agents by means of properties which they should possess. Most authors agree on these properties (at least basic set of properties) which are intrinsic to agents. Since agent\u27s definitions are not consistent, we are going to give an overview and list the properties intrinsic to an agent. Many different adjectives were attached to the term agent as well and many different kinds of agents and different architectures emerged too. The aim of this paper it go give an overview of what was going on in the field while taking into consideration main streams and projects. We will also present some guidelines important when modelling agent systems and say something about security issues. Also, some existing problems which restrict the wider usage of agents will be mentioned too

Implementation patterns for supporting learning and group interactions

This thesis covers research on group learning by using a computer as the medium. The computer software provides the basic blending of the students contributions augmented by the effects generated for the specific learning domain by a system of agents to guide the process of the students learning. The research is based on the approach that the computer as a medium is not an end point of the interaction. The development of agents in based on Human-Computer-Human interaction or HCH. HCH is about removing the idea that the role of the computer is that of an intelligent agent and reducing its role to that of a mixer, with the ability to insert adaptive electronic (software) components that add extra effects and depth to the product of the human-human interactions. For the computer to achieve this support, it must be able to analyse the input from the individuals and the group as a whole. Experiments have been conducted on groups working face to face, and then on groups using software developed for the research. Patterns of interaction and learning have been extracted from the logs and files of these group sessions. Also a pattern language has been developed by which to describe these patterns, so that the agent support needed to analyse and respond appropriately to each pattern can be developed. The research has led to the derivation of a structure that encompasses all the types of support required, and provides the format for implementing each type of support. The main difficulty in this work is the limited ability of computers to analyse human thoughts through their actions. However progress is made in analysing the level of approach by students to a range of learning concepts. The research identified the separate patterns that contribute to learning agents development and form a language of learning processes, and the agents derived from these patterns could in future be linked into a multi-agent system to support learning

Optimisation of Active Rule Agents using a Genetic Algorithm approach

Intelligent agents and active databases have a number of common characteristics, the most important of which is that they both execute actions by firing rules upon events occuring provided certain conditions hold. This paper assumes that the knowledge of an intelligent agent is expressed using a set of active rules and proposes a method for optimising the rule-base of such an agent using a Genetic Algorithm. We illustrate the applicability of this method by using it to optimise the performance of a self-adaptive network. The benefits of our approach are simplified design and reduced development and maintenance times of rule-based agents in the face of dynamically evolving environments. 1. Introduction Beliefs-desires-intentions (BDI) agnets have been around for some years now and have been extensively studied ([5], [9], [11], [2]). A BDI agent has the following components: · Beliefs Database: Contains facts about the sate of the world, as well as about the agent's internal state. ..

Optimisation of Active Rule Agents Using a Genetic Algorithm Approach

. Intelligent agents and active databases have a number of common characteristics, the most important of which is that they both execute actions by firing rules upon events occurring provided certain conditions hold. This paper assumes that the knowledge of an intelligent agent is expressed using a set of active rules and proposes a method for optimising the rule-base of such an agent using a Genetic Algorithm. We illustrate the applicability of this method by using it to optimise the performance of a self-adaptive network. The benefits of our approach are simplified design and reduced development and maintenance times of rule-based agents in the face of dynamically evolving environments. 1 Introduction Beliefs-desires-intentions (BDI) agents have been around for some years now and have been extensively studied ([5], [9], [11], [2]). A BDI agent has the following components: Beliefs Database: Contains facts about the sate of the world, as well as about the agent's interna..