Efecto del polimorfismo rs8179183 sobre la obesidad

would be an ideal example of this) then we can afford to increase the complexity of the controllers or environment.

Genetic-fuzzy systems can be quite effective at relieving the time requirements of hand tuning a large number of fuzzy rules in a static training situation, and optimising fuzzy rule-bases over hand- optimised sets by using fixed runs through obstacle courses, but the effectiveness of training depends on a number of different training tools being calibrated correctly. Firstly, the environment itself needs to contain the same type of environment as expected in final application. The environment elements (obstacles, etc.) must be regularly distributed over the test area to ensure a more or less uniform training environment. The length of course runs needs to be long enough to collect enough fitness data to differentiate “good” runs from “bad” runs. This will only be clear after studying graphs of results from a control study. The environment needs to include some sort of randomised element to prevent overly-brittle training and the learning of quirks in the environment, as discussed in Chapter 2.

Summarising the work on genetic-fuzzy systems in this thesis, we can say that it introduces the following original contributions:

1. A GFS and architecture specific to vehicle motion control. 2. An adaptive, real-time variation of the above.

3. A generalised genetic architecture that can be linked to any motion controller. 4. Evaluations of all of the above.

Introducing genetic systems may help to solve more difficult stochastic calibration problems, however, a number of new constraint-based problems are also introduced. The genetic system itself has a large number of parameters which need to be optimised to ensure efficiency of the learning/op- timisation process, particularly if it is to be run with real-time constraints. An exhaustive evaluation study of these parameters is provided in this work for one environment, with the optimal configu- ration presented. There is no guarantee, however, that this configuration will apply equally well to other environments, and as such only serves as a starting point for environment-specific studies. Fu- ture implementations of hybrid genetic systems should expect to conduct a similar meta-optimisation process (optimising the optimiser) before normal operation can proceed.

Considering the amount of work involved in manual tuning compared to setting up the training process, we can conclude that using a genetic hybrid it is only a clear advantage to fuzzy motion control systems that have to solve a difficult stochastic problem with a large number of constraints. There are two types of application where this is the case; either where the rules need to change in real time, or where the standard approach of optimising by test case will not guarantee a good balance across a dynamic environment. Examples of ideal applications are:

1. crowd-type simulations where the desired overall (group) motion is hard to arrive at by indi- vidual case scenarios

2. vehicle motion in unfamiliar, mixed, or changing environments where the rules need to adapt in real-time

3. motion calibration with “black box” constraints such as physics or some animation systems

13.3 Overarching Conclusions

Although it was not a stated aim of this thesis, one of the main contributions of this work was the development of a robust 3D simulation design framework for scientific work. The features of this being robust toolkits for rapid design of simulation environments for running experiments, a

software design pattern for partitioning different simulation components and libraries in a modular, flexible configuration, a series of data visualisation and transparency tools and graphing approaches, and benchmarking systems for running repeatable motion control experiments with detailed discus- sion of how performance data should be collected from these experiments with measurements of uncertainty. Perhaps most important in this particular outcome is the vehicle mechanical simulation framework of Chapter 11, which has not been published elsewhere in academic literature, and the benchmarking system of Chapter 12, which develops this into a well-defined scenario for making objective measurements of motion control performance, as mechanical and physical constraints have not been considered in benchmarking systems in the literature.

A second outcome of this thesis was the evaluation and development of a range of fuzzy motion controller “hybrids”. The most interesting result of these works was the discovery of how difficult it is to put the evaluation of simulation-based motion controllers into a objective (scientific) evaluation framework. This is due to the inherent difficulties of comparing motion controllers that are custom- designed for solving motion control problems of a specific simulation. These are “apples-to-pears” comparisons. It is a nonsense to try to compare the performance of a mechanically simulated agent with an agent that does not respect the same physical constraints. The major conclusion drawn here is that the evaluation frameworks need to evolve alongside new simulations and controllers. Both for demonstration to peers, and also to provide an effective self-evaluation tool for machine-learning algorithms such as the GFS.

The importance of effectively demonstrating the performance of motion controllers remains, and the best practice discovered in this work is to use a “mixed arms” approach, or a varied toolkit of visualisation and data collection tools; recorded videos of simulation runs, performance data from well defined scenarios and simulations, real-time graphs, and if possible, replication of well-known problems and test-environments.

13.4 Future Works

One of the future works directly stemming from this research is the development of more standard- ised test environments utilising physical-mechanical simulation; designing the car-and-trailer reverse problem, and the pendulum balance problem as well-defined test cases with effective evaluation cri- teria would be particularly interesting works for this area.

An ongoing work is the integration of a generic genetic-fuzzy system library into mechanically simulated vehicle simulation with objective benchmarking tools such as the scenario developed in Chapter 12. Given the larger number of constraints and rules to optimise for these systems it is of interest to discover if a GFS is capable of this more sophisticated level of optimisation, how long this would take in terms of training hours, and indeed how this might compare to a human driver’s learning curve.

Given the range of simulations, including mechanical vehicle simulations, which fuzzy con- trollers have been successfully applied to it is reasonable to assume that larger numbers of fuzzy controllers will be used in real vehicles. We have already seen a commercialisation of embedded fuzzy control chips for consumer appliances such as washing machines. It is not unrealistic to expect that fuzzy controllers will be used in most light rail (tram) and that we will see much greater uptake by heavy rail applications by the end of this decade. Emerging technologies for autonomous control of heavy road traffic (lorries and truck-and-trailer units) will certainly be an area of study sponsored by industry before autonomous private vehicles are commercialised. The goal of this research is to reduce transport costs and to act as an emergency “auto pilot” to help reduce driver tiredness related collisions on long trips. Fuzzy controllers may well be investigated as a vehicle-driving technology in this area.

Simulation will continue to play a key rˆole in the development of any fuzzy motion control technology, and the toolkits used for visualisation and physical simulation of real problem domains will have to evolve alongside the hardware technology.

13.4. FUTURE WORKS 149 The development of fuzzy controllers for computer animation will continue, based on the success of Massive’s fuzzy logic for simulation of very large, interacting crowds and battle scenes. At some point within this decade the interactive media technology used in video games will converge with the level of technology currently used to render motion pictures. We will see true “interactive films”, that is, computer games rendered on home-theatre type systems, using motion-sensing hardware and 3D projection technology to immerse players, but also featuring real actors, and using the same interactive, large-scale crowds and armies currently limited to film production. When this cross-over occurs we will have a surge of technologies used to animate and move plausible, interactive crowds, and a high demand for computational efficiency and realism.

Appendices

Appendix A

Summary of Publications

The following works were published during the course of this thesis.

A.1 Peer-Reviewed Articles

1. Cathy Ennis, Anton Gerdelan and Carol O’Sullivan, “Plausible Methods for Populating Virtual Scenes”, Crowd Simulation Workshop, Computer Animation and Social Agents, June 2010, Sant-Malo, France.

2. Anton Gerdelan and Carol O’Sullivan, “A Genetic-Fuzzy System for Optimising Agent Steer- ing”, Computer Animation and Virtual Worlds, May 2010, v.21, pp.453-461. [58]

3. S´ebastien Paris, Anton Gerdelan, and Carol O’Sullivan, “CA-LOD: Collision Avoidance Level of Detail for Scalable, Controllable Crowds”, Motion in Games, Springer Berlin / Heidelberg, 2009, v.5884, pp.13-28. [13]

4. Anton Gerdelan and Napoleon H. Reyes, “Towards A Generalised Hybrid Path-Planning and Motion Control System with Auto-Calibration for Animated Characters in 3D Envi- ronments”, Advances in Neuro-Information Processing, Springer Berlin / Heidelberg, 2009, v.5506, pp.1079-1086. [55]

5. Daniel P. Playne and Anton Gerdelan and Arno Leist and Chris J. Scogings and Ken A. Haw- ick, “Simulation Modelling and Visualisation: Toolkits for Building Simulated Worlds”, Re- search Letters in the Information and Mathematical Sciences, Massey University, 2008, v.12, pp.25-50. [59]