Stable social foraging swarms in a noisy environment

Bacteria, bees, and birds often work together in groups to find food. A group of robots can be designed to coordinate their activities to search for and collect objects. Networked cooperative uninhabited autonomous vehicles are being developed for commercial and military applications. Suppose that we refer to all such groups of entities as "social foraging swarms". In order for such multiagent systems to succeed it is often critical that they can both maintain cohesive behaviors and appropriately respond to environmental stimuli (e.g., by optimizing the acquisition of nutrients in foraging for food). In this paper, we characterize swarm cohesiveness as a stability property and use a Lyapunov approach to develop conditions under which local agent actions will lead to cohesive foraging even in the presence of "noise" characterized by uncertainty on sensing other agent's position and velocity, and in sensing nutrients that each agent is foraging for. The results quantify earlier claims that social foraging is in a certain sense superior to individual foraging when noise is present, and provide clear connections between local agent-agent interactions and emergent group behavior. Moreover, the simulations show that very complicated but orderly group behaviors, reminiscent of those seen in biology, emerge in the presence of noise.     

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin’s car-like robot.     

Learning patterns in ambient intelligence environments: a survey

It is essential for environments that aim at helping people in their daily life that they have some sort of Ambient Intelligence. Learning the preferences and habits of users then becomes an important step in allowing a system to provide such personalized services. Thus far, the exploration of these issues by the scientific community has not been extensive, but interest in the area is growing. Ambient Intelligence environments have special characteristics that have to be taken into account during the learning process. We identify these characteristics and use them to highlight the strengths and weaknesses of developments so far, providing direction to encourage further development in this specific area of Ambient Intelligence.     

Performance of deterministic learning in noisy environments

In this paper, based on the previous results of deterministic learning, we investigate the performance of deterministic learning in noisy environments. Two different types of noises arising in practical implementations are considered: the system noise and the measurement noise. By employing the convergence results of a class of perturbed linear time-varying (LTV) systems, the effects of these noises upon the learning performance are revealed. It is shown that while there is little effect upon the learning speed, noises have much influence on the learning accuracy. Compared with system noise, the effects of measurement noise appear to be more complicated. Under the noisy environments, robustification technique on the learning algorithm is required to avoid parameter drift. Furthermore, it is shown that additive system noise can be used to enhance the generalization ability of the RBF networks. Simulation studies are included to illustrate the results.     

Multiple ellipses detection in noisy environments: A hierarchical approach

Detection of multiple ellipses in noisy environments is a basic yet challenging task in many vision related problems. The key area of difficulty is on distinguishing the pixels pertaining to each target in the presence of noise. To tackle with the issue, we propose a hierarchical approach which is motivated by the fact that any segment of an ellipse can identify itself in ellipse reconstruction. First, we find all the neat edges without any branches, followed by an ellipse fitting on each of them. Second, some target candidates are estimated based on the neat edges, by a proposed grouping strategy. Finally, the targets are detected based on the candidates, by a proposed selective competitive algorithm to distinguish the true pixels of each target. A real application of the proposed method is illustrated in addition to some other demonstrative experiments.     

Behavior emergence of a virtual creature living in complex environments

This study aimed at establishing a new computer-aided animation method using agent-based and physics modeling-based animation. The specific problem we addressed was to install adaptive behavior in a virtual creature placed in a complex environment, and to create its animated behavior automatically. The virtual creature is regarded as an autonomous agent who has sensors, actuators, and controllers. An artificial neural netword (ANN) and a central pattern generator (CPG) were adopted as the controllers. An optimization algorithm was introduced to train the controllers. Numerical experiments proved that the virtual creature acquires effective motions (walking, swimming) to move toward its destination, and to avoid obstacles and other creatures.     

Supervector-based approaches in a discriminative framework for speaker verification in noisy environments

This paper explores the robustness of supervector-based speaker modeling approaches for speaker verification (SV) in noisy environments. In this paper speaker modeling is carried out in two different frameworks: (i) Gaussian mixture model-support vector machine (GMM-SVM) combined method and (ii) total variability modeling method. In the GMM-SVM combined method, supervectors obtained by concatenating the mean of an adapted speaker GMMs are used to train speaker-specific SVMs during the training/enrollment phase of SV. During the evaluation/testing phase, noisy test utterances transformed into supervectors are subjected to SVM-based pattern matching and classification. In the total variability modeling method, large size supervectors are reduced to a low dimensional channel robust vector (i-vector) prior to SVM training and subsequent evaluation. Special emphasis has been laid on the significance of a utterance partitioning technique for mitigating data-imbalance and utterance duration mismatches. An adaptive boosting algorithm is proposed in the total variability modeling framework for enhancing the accuracy of SVM classifiers. Experiments performed on the NIST-SRE-2003 database with training and test utterances corrupted with additive noises indicate that the aforementioned modeling methods outperform the standard GMM-universal background model (GMM-UBM) framework for SV. It is observed that the use of utterance partitioning and adaptive boosting in the speaker modeling frameworks result in substantial performance improvements under degraded conditions.     

PRISMATICA: toward ambient intelligence in public transport environments

On-line surveillance to improve safety and security is a major requirement for the management of public transport networks and other public places. The surveillance task is a complex one involving people, management procedures, and technology. This work describes an architecture that takes into account the distributed nature of the detection processes and the need to allow for different types of devices and actuators. This was part of a major European initiative on intelligent transport systems. Because of the dominant nature of closed circuit television in surveillance, This work describes in detail a computer-vision module used in the system and its particular ability to detect situations of interest in busy conditions. The system components have been implemented, integrated, and tested in real metropolitan railway environments and are considered to be the first step toward providing ambient intelligence in such complex scenarios. Results are presented that not only deal with detection performance, but also on the perception of people who used the system on its effectiveness and potential impact.     

CoWSAMI: Interface-aware context gathering in ambient intelligence environments

In this paper we present CoWSAMI, a middleware infrastructure that enables context awareness in open ambient intelligence environments, consisting of mobile users and context sources that become dynamically available as the users move from one location to another. A central requirement in such dynamic scenarios is to be able to integrate new context sources and users at run-time. CoWSAMI exploits a novel approach towards this goal. The proposed approach is based on utilizing Web services as interfaces to context sources and dynamically updatable relational views for storing, aggregating and interpreting context. Context rules are employed to provide mappings that specify how to populate context relations, with respect to the different context sources that become dynamically available. An underlying context sources discovery mechanism is utilized to maintain context information up to date as context sources, and users get dynamically involved.     

Critical phenomena in a collective computation algorithm for combinatorial optimization problems

This paper discusses the critical temperature (control parameter) of an annealed neural network, a typical collective computation for combinatorial optimization problems.It is shown that the theoretical critical temperatures determined by our estimation agree with those derived by computational experiments in graph partitioning problems and in the travelling salesman problem.     

Interactive perception for amplification of intended behavior in complex noisy environments

abstract  The detection of a human’s intended behavior is one of the most important skills that a social robot should have in order to become acceptable as a part of human society, because humans are used to understand the actions of other humans in a goal-directed manner and they will expect the social robot to behave similarly. A breakthrough in this area can advance several research branches related to social intelligence such as learning by imitation and mutual adaptation. To achieve this goal the robot needs to integrate all possible evidence of intention and neglect the unintended behavior, and a complete solution should use low-level signal processing and high-level reasoning. This work explores the low-level signal processing part of the solution by proposing an interactive adaptive perception scheme that uses four important features of human behavior to amplify the signals originating from intended behavior with respect to signals originating from unintended behavior and other noise sources such as instrumental noise. This work follows the vision that intelligence is not only a function of a centralized sophisticated artificial brain, but can be presented in different forms in the entire robot including its perception and motion systems (the mind is not contained in the brain, but distributed in every cell in the body). A simple example of using the proposed scheme was implemented and the results of two experiments with it are also presented.     

A survey of service composition in ambient intelligence environments

This article presents a comparative review of systems performing service composition in Ambient Intelligence Environments. Such environments should comply to ubiquitous or pervasive computing guidelines by sensing the user needs or wishes and offering intuitive human-computer interaction and a comfortable non-intrusive experience. To achieve this goal service orientation is widely used and tightly linked with AmI systems. Some of these employ the Web Service technology, which involves well-defined web technologies and standards that facilitate interoperable machine to machine interaction. Other systems regard services of different technologies (e.g. UPnP, OSGi etc) or generally as abstractions of various actions. Service operations are sometimes implemented as software based functions or actions over hardware equipment (e.g. UPnP players). However, a single service satisfies an atomic only user need, so services need to be composed (i.e. combined), in order to provide the usually requested complex tasks. Since manual service composition is obviously a hassle for the user, ambient systems struggle to automate this process by applying various methods. The approaches that have been adopted during the last years vary widely in many aspects, like domain of application, modeling of services, composition method, knowledge representation and interfaces. This work presents a comparative view of these approaches revealing similarities and differences, while providing additional information.     

Self-adaptation of learning rate in XCS working in noisy and dynamic environments

An extended classifier system (XCS) is an adaptive rule-based technique that uses evolutionary search and reinforcement learning to evolve complete, accurate, and maximally general payoff map of an environment. The payoff map is represented by a set of condition-action rules called classifiers. Despite this insight, till now parameter-setting problem associated with LCS/XCS has important drawbacks. Moreover, the optimal values of some parameters are strongly influenced by properties of the environment like its complexity, changeability, and the level of noise. The aim of this paper is to overcome some of these difficulties by a self-adaptation of a learning rate parameter, which plays a key role in reinforcement learning, since it is used for updates of classifier parameters: prediction, prediction error, fitness, and action set estimation. Self-adaptive control of prediction learning rate is investigated in the XCS, whereas the fitness and error learning rates remain fixed. Simultaneous self-adaptation of prediction learning rate and mutation rate also undergo experiments. Self-adaptive XCS solves one-step problems in noisy and dynamic environments.     

Analysis of synchronization constraints and associated errors in collective work environments

This paper presents a framework of analysis of the requirements of synchronization and the associated errors in collective work environments. It proposes a formalism for describing the temporal relations that synchronization constraints take on in a work situation, giving special attention to the temporal information strategies used by the operators. A relational conception of time helps in understanding how the operators perceive, retain and utilize information to temporally co-ordinate their behaviours. An example application of the framework of analysis is developed for a situation of concrete work -anaesthesia- in the final section which concludes with the analysis of two cases of errors related to synchronization.     

Business intelligence success: The roles of BI capabilities and decision environments

This study examines the role of the decision environment in how well business intelligence (BI) capabilities are leveraged to achieve BI success. We examine the decision environment in terms of the types of decisions made and the information processing needs of the organization. Our findings suggest that technological capabilities such as data quality, user access and the integration of BI with other systems are necessary for BI success, regardless of the decision environment. However, the decision environment does influence the relationship between BI success and capabilities, such as the extent to which BI supports flexibility and risk in decision making.     

Comparison of the approximate solutions of a noisy non-linear system based on Monte Carlo simulation

This paper is concerned with the comparison of the approximate methods in order to evaluate the values of state variables of a noisy nonlinear system by Monte Carlo simulation. The approximate methods considered here are (1) the stochastic linearisation, (2) the first-order approximation and (3) the second-order approximation. It is shown that the order of the accuracies of the covariance based on the approximate equation of Riccati type does not always coincide with the order of the practical error covariance of the original system which is obtained by using Monte Carlo simulation. Therefore the comparison of the accuracies should not be carried out by the solution obtained from the covariance equation but by the practical error covariance of the original system directly. This result is demonstrated with numerical examples. The characteristics of the random numbers which are used for Monte Carlo simulation are examined by various kinds of tests.