Understanding the Regulation of Predatory and Anti-Prey Behaviours for an Artificial Organism

An organism’s behaviour can be categorised as being either predatory or anti-prey. Predatory behaviours are behaviours that try to improve the life of an organism. Anti-prey behaviours are those that attempt to prevent death. Regulation between these two opposing behaviours is necessary to ensure survivability—and gene regulatory networks and metabolic networks are the mechanisms that provide this regulation. We know that such regulatory behaviour is encoded in an organism’s genes. The question is, how is it encoded? The understanding of this encoding can help with the development of an artificial organism, for example an autonomous robotic system; whereby the robot will have the ability to autonomously regulate the switching between the opposing behaviours using this encoded mechanism, in order to ensure its sustainable and continuous system operations. This paper aims to look into the properties of an artificial bio-chemical network consisting of a genetic regulatory network and a metabolic network that can provide these capabilities

Investigating the properties of bio-chemical networks of artificial organisms with opposing behaviours

Organisms, be it singled-celled organisms or multi-cellular organisms, are constantly faced with opposing objectives requiring different sets of behaviours. These behaviours can be classified into two, predatory behaviours or anti-prey behaviours, with one set of behaviours causing an opposite effect to the other. A healthy organism aims to achieve its equilibrium state or to be in homeostasis. Homeostasis is achieved when a balance between the two opposing behaviours is created and maintained. This raises some questions: is there an innate mechanism that encodes for these categories of behaviours? Is there also an innate mechanism(s) that resolves conflicts and allows switching between these two opposing behaviours? If we consider artificial organisms as single-celled organisms, how do the organisms’ gene regulatory network, metabolic network and/or signalling network (their biochemical networks) maintain homeostasis of the organisms? This paper investigates the properties of the networks of best evolved artificial organisms, in order to help answer these questions, and guide the evolutionary development of controllers for artificial systems

An ARTMAP-incorporated Multi-Agent System for Building Intelligent Heat Management

This paper presents an ARTMAP-incorporated multi-agent system (MAS) for building heat management, which aims to maintain the desired space temperature defined by the building occupants (thermal comfort management) and improve energy efficiency by intelligently controlling the energy flow and usage in the building (building energy control). Existing MAS typically uses rule-based approaches to describe the behaviours and the processes of its agents, and the rules are fixed. The incorporation of artificial neural network (ANN) techniques to the agents can provide for the required online learning and adaptation capabilities. A three-layer MAS is proposed for building heat management and ARTMAP is incorporated into the agents so as to facilitate online learning and adaptation capabilities. Simulation results demonstrate that ARTMAP incorporated MAS provides better (automated) energy control and thermal comfort management for a building environment in comparison to its existing rule-based MAS approach

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

Comparing the Online Learning Capabilities of Gaussian ARTMAP and Fuzzy ARTMAP for Building Energy Management Systems

Recently, there has been a growing interest in the application of Fuzzy ARTMAP for use in building energy management systems or EMS. However, a number of papers have indicated that there are important weaknesses to the Fuzzy ARTMAP approach, such as sensitivity to noisy data and category proliferation. Gaussian ARTMAP was developed to help overcome these weaknesses, raising the question of whether Gaussian ARTMAP could be a more effective approach for building energy management systems? This paper aims to answer this question. In particular, our results show that Gaussian ARTMAP not only has the capability to address the weaknesses of Fuzzy ARTMAP but, by doing this, provides better and more efficient EMS controls with online learning capabilities

Increasing Endurance of an Autonomous Robot using an Immune-Inspired Framework

This paper describes the implementation of an online immune-inspired framework to help increase endurance of an autonomous robot. Endurance is defined as the ability of the robot to exert itself for a long period of time. The immune-inspired framework provides such capability by monitoring the behavior of the robot to ensure continuous and safe behavior. The immune-inspired framework combines innate and adaptive immune inspired algorithms. Innate uses a dendritic cell based innate immune algorithm, and adaptive uses an instance based B-cell approach. Results presented in this paper shows that when the robot is implemented with the immune-inspired framework, health and survivability of a robot is improved, therefore increasing its endurance

A Multi-Objective Planning Framework for Optimal Integration of Distributed Generations

This paper presents an evolutionary algorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power generation, line losses and CO2 emissions, and maximizing the benefits from the DG over a 20 years planning horizon. The method assesses the fault current constraint imposed on the distribution network by the existing and new DG in order not to violate the short circuit capacity of existing switchgear. The analysis utilizes one of the highly regarded evolutionary algorithm, the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for multi-objective optimization and MATPOWER for solving the optimal power flow problems

A SPEA2 Based Planning Framework for Optimal Integration of Distributed Generations

The paper presents a multi-objective optimisation method for analysing the best mix of renewable and non- renewable distributed generations (DG) in a distribution network. The method aims at minimising the total cost of the real power generation, line losses and CO2 emissions, and maximising the benefits from DG installations over a planning horizon of 20 years. The paper proposes new objective functions that take into account the longevity of DG operations as one of its selection criteria. The analysis utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for optimisation and MATPOWER for solving the optimal power flow problems

Energy Network Communications and Expandable Control Mechanisms

A modular, expandable network requiring little or no calibration is something that is well sought after and would offer great benefits when used for distributed energy generation. Intelligent and adaptive control of such a network offers stability of supply from intermittent sources which, to date, has been hard to achieve. Key to the effective use of such control systems is communications, specifically the exchange of commands and status information between the control systems and the attached devices. Power-line communications has been used in various applications for years and would offer a good mechanism for interconnecting devices on a power grid without the expense of laying new cabling. By using clusters of devices managed by an IEMS (Intelligent Energy Management System) in a branching network fashion (not unlike the grid itself) it would be possible to manage large numbers of devices and high speed with relatively low bandwidth usage increasing the usable range of transmission. Implications of this include improving network efficiency through managed power distribution and increased security of supply

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