Adaptive agents in changing environments, the role of modularity

We explored the role of modularity as a means to improve evolvability in populations of adaptive agents. We performed two sets of artificial life experiments. In the first, the adaptive agents were neural networks controlling the behavior of simulated garbage collecting robots, where modularity referred to the networks architectural organization and evolvability to the capacity of the population to adapt to environmental changes measured by the agents performance. In the second, the agents were programs that control the changes in network's synaptic weights (learning algorithms), the modules were emerged clusters of symbols with a well defined function and evolvability was measured through the level of symbol diversity across programs. We found that the presence of modularity (either imposed by construction or as an emergent property in a favorable environment) is strongly correlated to the presence of very fit agents adapting effectively to environmental changes. In the case of learning algorithms we also observed that character diversity and modularity are also strongly correlated quantities. © 2014 Springer Science+Business Media New York.

Liposomes:Formulation and characterisation as contrast agents and as vaccine delivery systems

Liposome systems are well reported for their activity as vaccine adjuvants; however novel lipid-based microbubbles have also been reported to enhance the targeting of antigens into dendritic cells (DCs) in cancer immunotherapy (Suzuki et al 2009). This research initially focused on the formulation of gas-filled lipid coated microbubbles and their potential activation of macrophages using in vitro models. Further studies in the thesis concentrated on aqueous-filled liposomes as vaccine delivery systems. Initial work involved formulating and characterising four different methods of producing lipid-coated microbubbles (sometimes referred to as gas-filled liposomes), by homogenisation, sonication, a gas-releasing chemical reaction and agitation/pressurisation in terms of stability and physico-chemical characteristics. Two of the preparations were tested as pressure probes in MRI studies. The first preparation composed of a standard phospholipid (DSPC) filled with air or nitrogen (N2), whilst in the second method the microbubbles were composed of a fluorinated phospholipid (F-GPC) filled with a fluorocarbon saturated gas. The studies showed that whilst maintaining high sensitivity, a novel contrast agent which allows stable MRI measurements of fluid pressure over time, could be produced using lipid-coated microbubbles. The F-GPC microbubbles were found to withstand pressures up to 2.6 bar with minimal damage as opposed to the DSPC microbubbles, which were damaged at above 1.3 bar. However, it was also found that DSPC-filled with N2 microbubbles were also extremely robust to pressure and their performance was similar to that of F-GPC based microbubbles. Following on from the MRI studies, the DSPC-air and N2 filled lipid-based microbubbles were assessed for their potential activation of macrophages using in vitro models and compared to equivalent aqueous-filled liposomes. The microbubble formulations did not stimulate macrophage uptake, so studies thereafter focused on aqueous-filled liposomes. Further studies concentrated on formulating and characterising, both physico-chemically and immunologically, cationic liposomes based on the potent adjuvant dimethyldioctadecylammonium (DDA) and immunomodulatory trehalose dibehenate (TDB) with the addition of polyethylene glycol (PEG). One of the proposed hypotheses for the mechanism behind the immunostimulatory effect obtained with DDA:TDB is the ‘depot effect’ in which the liposomal carrier helps to retain the antigen at the injection site thereby increasing the time of vaccine exposure to the immune cells. The depot effect has been suggested to be primarily due to their cationic nature. Results reported within this thesis demonstrate that higher levels of PEG i.e. 25 % were able to significantly inhibit the formation of a liposome depot at the injection site and also severely limit the retention of antigen at the site. This therefore resulted in a faster drainage of the liposomes from the site of injection. The versatility of cationic liposomes based on DDA:TDB in combination with different immunostimulatory ligands including, polyinosinic-polycytidylic acid (poly (I:C), TLR 3 ligand), and CpG (TLR 9 ligand) either entrapped within the vesicles or adsorbed onto the liposome surface was investigated for immunogenic capacity as vaccine adjuvants. Small unilamellar (SUV) DDA:TDB vesicles (20-100 nm native size) with protein antigen adsorbed to the vesicle surface were the most potent in inducing both T cell (7-fold increase) and antibody (up to 2 log increase) antigen specific responses. The addition of TLR agonists poly(I:C) and CpG to SUV liposomes had small or no effect on their adjuvanticity. Finally, threitol ceramide (ThrCer), a new mmunostimulatory agent, was incorporated into the bilayers of liposomes composed of DDA or DSPC to investigate the uptake of ThrCer, by dendritic cells (DCs), and presentation on CD1d molecules to invariant natural killer T cells. These systems were prepared both as multilamellar vesicles (MLV) and Small unilamellar (SUV). It was demonstrated that the IFN-g secretion was higher for DDA SUV liposome formulation (p<0.05), suggesting that ThrCer encapsulation in this liposome formulation resulted in a higher uptake by DCs.

Adapting populations of agents

We control a population of interacting software agents. The agents have a strategy, and receive a payoff for executing that strategy. Unsuccessful agents become extinct. We investigate the repercussions of maintaining a diversity of agents. There is often no economic rationale for this. If maintaining diversity is to be successful, i.e. without lowering too much the payoff for the non-endangered strategies, it has to go on forever, because the non-endangered strategies still get a good payoff, so that they continue to thrive, and continue to endanger the endangered strategies. This is not sustainable if the number of endangered ones is of the same order as the number of non-endangered ones. We also discuss niches, islands. Finally, we combine learning as adaptation of individual agents with learning via selection in a population. © Springer-Verlag Berlin Heidelberg 2003.

Evolutionary market agents and heterogeneous service providers:achieving desired resource allocations

In future massively distributed service-based computational systems, resources will span many locations, organisations and platforms. In such systems, the ability to allocate resources in a desired configuration, in a scalable and robust manner, will be essential.We build upon a previous evolutionary market-based approach to achieving resource allocation in decentralised systems, by considering heterogeneous providers. In such scenarios, providers may be said to value their resources differently. We demonstrate how, given such valuations, the outcome allocation may be predicted. Furthermore, we describe how the approach may be used to achieve a stable, uneven load-balance of our choosing. We analyse the system's expected behaviour, and validate our predictions in simulation. Our approach is fully decentralised; no part of the system is weaker than any other. No cooperation between nodes is assumed; only self-interest is relied upon. A particular desired allocation is achieved transparently to users, as no modification to the buyers is required.

Adaptive power and rate control for QoS guarantee of DSRC based collaborative road safety applications

In this paper we propose an adaptive power and message rate control method for safety applications at road intersections. The design objectives are to firstly provide guaranteed QoS support to both high priority emergency safety applications and low priority routine safety applications and secondly maximize channel utilization. We use an offline simulation based approach to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network with certain safety QoS requirements. The identified configurations are then used online by roadside access points (AP) adaptively according to estimated number of vehicles. Simulation results show that this adaptive method could provide required QoS support to safety applications and it significantly outperforms a fixed control method. © 2013 International Information Institute.

An intelligent approach for deploying applications in WSN's

Wireless sensor networks have been identified as one of the key technologies for the 21st century. They consist of tiny devices with limited processing and power capabilities, called motes that can be deployed in large numbers of useful sensing capabilities. Even though, they are flexible and easy to deploy, there are a number of considerations when it comes to their fault tolerance, conserving energy and re-programmability that need to be addressed before we draw any substantial conclusions about the effectiveness of this technology. In order to overcome their limitations, we propose a middleware solution. The proposed scheme is composed based on two main methods. The first method involves the creation of a flexible communication protocol based on technologies such as Mobile Code/Agents and Linda-like tuple spaces. In this way, every node of the wireless sensor network will produce and process data based on what is the best for it but also for the group that it belongs too. The second method incorporates the above protocol in a middleware that will aim to bridge the gap between the application layer and low level constructs such as the physical layer of the wireless sensor network. A fault tolerant platform for deploying and monitoring applications in real time offers a number of possibilities for the end user giving him in parallel the freedom to experiment with various parameters, in an effort towards the deployed applications running in an energy efficient manner inside the network. The proposed scheme is evaluated through a number of trials aiming to test its merits under real time conditions and to identify its effectiveness against other similar approaches. Finally, parameters which determine the characteristics of the proposed scheme are also examined.

Adaptive electrical signal post-processing in optical communications systems

Improving bit error rates in optical communication systems is a difficult and important problem. The error correction must take place at high speed and be extremely accurate. We show the feasibility of using hardware implementable machine learning techniques. This may enable some error correction at the speed required.

QoS guarantee with adaptive transmit power and message rate control for DSRC vehicle network based road safety applications

Quality of services (QoS) support is critical for dedicated short range communications (DSRC) vehicle networks based collaborative road safety applications. In this paper we propose an adaptive power and message rate control method for DSRC vehicle networks at road intersections. The design objective is to provide high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method an offline simulation based approach is used to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network. The identified best configurations are then used online by roadside access points (AP) according to estimated number of vehicles. Simulation results show that this adaptive method significantly outperforms a fixed control method. © 2011 Springer-Verlag.

Adaptive electrical signal post-processing in optical communications systems

Improving bit error rates in optical communication systems is a difficult and important problem. The error correction must take place at high speed and be extremely accurate. We show the feasibility of using hardware implementable machine learning techniques. This may enable some error correction at the speed required.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.