"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Improving Artificial-Immune-System-based computing by exploiting intrinsic features of computer architectures

Biological systems have become highly significant for traditional computer architectures as examples of highly complex self-organizing systems that perform tasks in parallel with no centralized control. However, few researchers have compared the suitability of different computing approaches for the unique features of Artificial Immune Systems (AIS) when trying to introduce novel computing architectures, and few consider the practicality of their solutions for real world machine learning problems. We propose that the efficacy of AIS-based computing for tackling real world datasets can be improved by the exploitation of intrinsic features of computer architectures. This paper reviews and evaluates current existing implementation solutions for AIS on different computing paradigms and introduces the idea of “C Principles” and “A Principles”. Three Artificial Immune Systems implemented on different architectures are compared using these principles to examine the possibility of improving AIS through taking advantage of intrinsic hardware features

Adaptive and Online Health Monitoring System for Autonomous Aircraft

Good situation awareness is one of the key attributes required to maintain safe flight, especially for an Unmanned Aerial System (UAS). Good situation awareness can be achieved by incorporating an Adaptive Health Monitoring System (AHMS) to the aircraft. The AHMS monitors the flight outcome or flight behaviours of the aircraft based on its external environmental conditions and the behaviour of its internal systems. The AHMS does this by associating a health value to the aircraft's behaviour based on the progression of its sensory values produced by the aircraft's modules, components and/or subsystems. The AHMS indicates erroneous flight behaviour when a deviation to this health information is produced. This will be useful for a UAS because the pilot is taken out of the control loop and is unaware of how the environment and/or faults are affecting the behaviour of the aircraft. The autonomous pilot can use this health information to help produce safer and securer flight behaviour or fault tolerance to the aircraft. This allows the aircraft to fly safely in whatever the environmental conditions. This health information can also be used to help increase the endurance of the aircraft. This paper describes how the AHMS performs its capabilities

Can Developmental AIS Provides Immunity to a Multi-cellular Robotics System?

The major challenge to multi-cellular robotics system is how to ensure the system is homeostatically stable. This position paper pro- poses a developmental artificial immune system (dev-AIS) framework that tries to provide and maintain homeostasis to the multi-cellular robotics system. If immunity is defined as the ability to maintain home- ostasis; the dev-AIS framework will be designed based on the under- standing and the abstraction of how different organisms attain for this property through evolution and developmental process. Early form of In- nate Immunity evolve from the predator-and-anti prey relationship of the single-celled organism. Progress in evolution drove the evolution of im- munity from this simple relationship to the development of the immune system in multi-cellular organisms

Fault detection and prediction with application to rotating machinery

In this thesis, the detection and prediction of faults in rotating machinery is undertaken and presented in two papers. In the first paper, Principal Component Analysis (PCA), a well known data-driven dimension reduction technique, is applied to data for normal operation and four fault conditions from a one-half horsepower centrifugal water pump. Fault isolation in this scheme is done by observing the location of the data points in the Principal Component domain, and the time to failure (TTF) is calculated by applying statistical regression on the resulting PC scores. The application of the proposed scheme demonstrated that PCA was able to detect and isolate all four faults. Additionally, the TTF calculation for the impeller failure was found to yield satisfactory results. On the other hand, in the second paper, the fault detection and failure prediction are done by using a model based approach which utilizes a nonlinear observer consisting of an online approximator in discrete-time (OLAD) and a robust adaptive term. Once a fault has been detected, both the OLAD and the robust adaptive term are initiated and the OLAD then utilizes its update law to learn the unknown dynamics of the encountered fault. While in similar applications it is common to use neural networks to be used for the OLAD, in this paper an Artificial Immune System (AIS) is used for the OLAD. The proposed approach was verified through implementation on data from an axial piston pump. The scheme was able to satisfactorily detect and learn both an incipient piston wear fault and an abrupt sensor failure --Abstract, page iv

CODA Algorithm: An Immune Algorithm for Reinforcement Learning Tasks

This document presents the design of an algorithm that takes on its basis: reinforcement learning, learning from demonstration and most importantly Artificial Immune Systems. The main advantage of this algorithm named CODA (Cognition from Data). Is; it can learn from limited data samples- that is given a single example and the algorithm will create its own knowledge. The algorithm imitates from the Natural Immune System the clonal procedure for obtaining a repertoire of antibodies from a single antigen. It also uses the self-organised memory in order to reduce searching time in the whole action-state space by searching in specific clusters. CODA algorithm is presented and explained in detail in order to understand how these three principles are used. The algorithm is explained with pseudocode, flowcharts and block diagrams. The clonal/mutation results are presented with a simple example. It can be seen graphically how new data that has a completely new probability distribution. Finally, the first application where CODA is used, a humanoid hand is presented. In this application the algorithm created affordable grasping postures from limited examples, creates its own knowledge and stores data in memory data in memory in order to recognise whether it has been on a similar situation

Artificial immune system agent model

The Artificial Immune Systems (AIS) is a biologically inspired techniques that emulates a natural system, in particular the vertebrate immune system, in order to develop computational tools for solving engineering problems.Immunity-based technique emerge as a new branch of artificial intelligence (AI).The human biological immune system has become the source of inspiration for developing intelligent problem-solving techniques.The powerful information processing capabilities of the human system, such as feature extraction, pattern extraction, learning, memory and its distributive nature provide rich metaphors for its artificial counterpart. Hence, the goal of this study is to develop an Artificial Immune System (AIS) model using agent approach for incremental learning.The main issue handled was how to integrate a multiagent system into an AIS application.This model proposed was simulated using cases for the performance measurement.The step by step activities performed in developing the agent based AIS model can be a guideline in developing an AIS application. Besides that, the simulation of the AIS model can be further enhanced to be used for teaching and learning purposes