Graham Kendall

Individual Projects (3rd Year Undergraduates)

(Academic Year 1999/2000)


This page details the third year individual projects that are/have been supervised by Graham Kendall who is a lecturer in the Department of Computer Science at The University of Nottingham. This page contains details of projects supervised in the academic year 2001/02.

The main project page can be found here.

Downloadable Dissertations

Some of the projects are available for download, so that you can view them. This facility is for current third year students, being supervised by me, who can use previous dissertations for their own research and also to look at previous dissertations to see how to lay them out etc. (it also saves the inevitable requests I receive asking to see a "good" project from last year, with the risk that I never see it again!).

As such, these files are password protected and, to get the username/password you need to EMAIL me. People outside of the university will not be given access.

In addition, I cannot give access to those students outside of my tutor group. You should approach your own tutor to get copies of previous dissertations they have supervised.

Note : I cannot make the files available in formats other than those already supplied.

3rd Year Projects Supervised in 1999/00

  1. Evolving Collective Behaviour by Chris Ward (PDF/Word)
    Abstract : Collective behaviour refers to coordinated group motion, common to many animals. The dynamics of a group can be seen as a distributed model, each animal applying the same rule set. This study investigates the use of an evolved controller to produce schooling behaviour. A set of artificial creatures ‘live’ in an artificial world with hazards and food. Each creature has an artificial neural network brain that controls these behaviours in different situations. A genome encodes the network structure, which may be combined using artificial evolution with another if a creature should choose to mate. Prey and predators coevolve to produce sophisticated behaviour.
    This project won the prize for the most outstanding project of the year (across all of computer science, not just those I supervised)
    An article arising from this project has also been published in Artificial Life

    (Ward C.R., Gobet F. & Kendall G. Evolving Collective Behavior in an Artificial Ecology, Artificial Life 7(2), 191-209. 2001 (special issue on "Evolution of Sensors in Nature, Hardware and Simulation"))

  2. Artificial Intelligence Chess with Adaptive Learning by Glenn Whitwell (Word)
    Abstract : This document is the culmination of one year’s work as part of a final year assessed project for a Computer Science Single Honours Degree. It contains a discussion of the artificial intelligence concept of Adaptive Learning with Genetic Algorithms and of how they were used in the developing or ‘evolving’ of a chess-playing computer program. Through the implementation of game tree search techniques such as minimax, alpha-beta pruning, evaluation quiescence, and genetic-based methods applied to a population of evaluation functions, the chess program displayed an autonomous improvement in performance after 4400 training games. This report details areas such as the motivation for the project, the research involved in completing the project, the design of the chess program, the implementation of the program, the reasoning for the techniques used, a discussion of the results obtained, the success of the project, and a discussion of the potential project extensions.
    A paper arising from this project was accepted by CEC2001

    (Kendall, G and Whitwell, G. An Evolutionary Approach for the Tuning of a Chess Evaluation Function using Population Dynamics. In proceedings of Congress on Evolutionary Computation 2001 (CEC'01), COEX Center, Seoul, Korea, May 27-29, 2001, pp 995-1002 (an IEEE conference). (pdf:265KB)

  3. Implementation of the No Fit Polygon using Minkowski Sums by Amar Qureshi
    Abstract : The No Fit Polygon (NFP) is a commonly used geometric operation in many industrial processes, such as two-dimensional irregular stock cutting. There have been many approaches to calculating the NFP, which have tackled convex shapes quite efficiently, but have failed to tackle non-convex shapes. This project aims to implement an algorithm that calcuates the NFP based on Minkowski sums. The resultant application of this project will calculate the NFP for a set of user specified polygons, which can either be convex or non-convex.

  4. Experiments in Simulated Soccer by Philip Smith
    Abstract : Robotic Soccer (RoboCup) is the attempt by the Multiagent System community to create teams of robotic agents (players) that can play Association Football. This dissertation focuses exclusively on simulated Robotic Soccer. The main focus of this work is the creation of a team of autonomous agents each working to achieve a common goal; winning RoboCup games. A significant sub-goal is to achieve good team structure and shape without having to explicity code it. The decision-making framework of my agents is a system of layers that control increasingly complex behaviours. The first layer controls the agents individual footballing skills. The second coordinates these skills in a way that is appropriate for the situation. The third layer controls the second layer in a way that causes the emergence of team structure and shape. The agents are implemented in C++ under Linux using a standard RoboCup agent template. The final team was able to win games of RoboCup and had a flexible formation structure that emerged as a result of the interactions between the individual skills of each agent.

  5. Playing Poker Using an Adaptive Learning Algorithm by Mark Willdig (Word)
    Abstract : Using adaptive learning methods, specialised computer programs can evolve into expert systems based on simple principles. Adaptive learning involves creating a program that can adapt and evolve to certain circumstances by mutating its internal structure. Previous work has shown that by using adaptive learning, an agent can be created that plays poker successfully, able to play in a number of different environments against a variety of opponents.
    This study uses the game of poker to show how agents can evolve and adapt, and expands on previous work by introducing bluffing and deal rotation.
    Internal parameters are manipualted as a direct result of how the evolving player performs, values being altered according to numbers returned by a normal distribution graph.
    The results of this were that the agent learnt successfully, initially losing through the training period before learning was complete, and then playing to maximise winnings.
    The system performed well against all opponents, and the learning curve of the evolving player proves that the system can adapt successfully to the rules of poker. Initial limitation of previous systems are overcome by successfully introducing deal rotation and bluffing and two rounds of betting with a trade of cards in between.
    This shows how the evolutionary strategy of parameter adjustment by increasing and decreasing internal learning values as the agent wins and loses, accomplishes the original objectives and makes significant advances on previous work in the area.

    A paper arising from this project was accepted by AI'01

    Kendall G. and Willdig M. . An Investigation of an Adaptive Poker player. In proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI'01), Adelaide, Australia, 10 - 14 December 2001, LNAI 2256, pp189-200, Springer-Verlag, ISBN 2-540-42960-3 (pdf:52KB) (word:227KB)

  6. Artificial Life by Detlef Brendle
    Abstract : This project Artificial Life is based on the former simulation of John Conways Game of Life, which is the most common root in the vast number of A-Life simulations nowadays. The main differences are that the project is concerned about two life forms on the same grid instead of only one and the project can also evolve the species on the same living space. The main purpose is to show how evolution effects the chance of survival of an organism.

  7. Internet Financial Information Filtering Intelligent Agent by Amanda Banks
    Abstract : This dissertation documents the research and design for a system using an intelligent agent that filetrs financial information retrieved from the internet, given user specified keywords relating to a given stock portfolio. The agent uses a learning algorithm to improve performance of filtering results and takes feedback from the user based on these results.
    The application is designed for the first time personal investors looking to research a stock portfolio, and the primary specification therefore was ease of use. The system uses an intelligent agent framework to implement the application allowing for future expansion, and a Kohonen map algorithm is used to implement the learning capability.
    This document details research done into the area of intelligent agents, specifically on their definition, considerations, current systems and design issues. A full specification of the problem is given, and an overview of the design for the application demonstrated, given the lack of standard methodology for designing intelligent agents. Details of the implementation and how the application works are discussed, and the problems encountered during this stage are also examined. The results are produced and possible enhancements to the system suggested. The dissertation is summarised with the lessons learnt from the project.

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Last Updated 15th June 2001