A framework for the collective movement of mobile robots based on distributed decisions

A novel framework for the control of the collective movement of mobile robots is presented and analyzed in this article. It allows a group of robots to move as a unique entity performing the following functions: obstacle avoidance at group level, speed control and modification of the inter-robot distance. Its flocking controller is distributed among the robots, allowing them to move in the desired common direction and maintain a desired inter-robot distance. The framework is made up of different modules that modify the behavior of the group thus allowing different functions. They are based on consensus algorithms that allow the robots to agree on different parameters, taking into account which robot has more relevant information. New modules can be easily designed and incorporated into the framework in order to augment its capabilities. It can be easily implemented on any mobile robot capable of measuring the relative positions of neighboring robots and communicating with them. It has been successfully tested using 8 real robots and in simulation with up to 40 robots, demonstrating experimentally its scalability with an increasing number of robots.     

On model design for simulation of collective intelligence

The study of collective intelligence (CI) systems is increasingly gaining interest in a variety of research and application domains. Those domains range from existing research areas such as computer networks and collective robotics to upcoming areas of agent-based and insect-based computing; also including applications on the internet and in games and movies. CI systems are complex by nature and (1) are effectively adaptive in uncertain and unknown environments, (2) can organise themselves autonomously, and (3) exhibit ‘emergent’ behaviour. Among others, multi-agent systems, complex adaptive systems, swarm intelligence and self-organising systems are considered to be such systems. The explosive wild growth of research studies of CI systems has not yet led to a systematic approach for model design of these kinds of systems. Although there have been recent efforts on the issue of system design (the complete design trajectory from identifying system requirements up to implementation), the problem of choosing and specifying a good model of a CI system is often done implicitly and sometimes even completely ignored. The aim of this article is to bring to the attention that model design is an essential as well as an integral part of system design. We present a constructive approach to systematically design, build and test models of CI systems. Because simulation is often used as a way to research CI systems, we particularly focus on models that can be used for simulation. Additionally, we show that it is not necessary to re-invent the wheel: here, we show how existing models and algorithms can be used for CI model design. The approach is illustrated by means of two example studies on a (semi-automated) multi-player game and collaborative robotics.     

On the distributed orchestration of stochastic discrete event simulations

Discrete event simulations are a powerful technique for modeling stochastic systems with multiple components where interactions between these components are governed by the probability distribution functions associated with them. Complex discrete event simulations are often computationally intensive with long completion times. This paper describes our solution to the problem of orchestrating the execution of a stochastic, discrete event simulation where computational hot spots evolve spatially over time. Our performance benchmarks report on our ability to balance computational loads in these settings. Copyright © 2013 John Wiley & Sons, Ltd.     

A tuple channel-based coordination model for parallel and distributed programming

This paper presents tuple channel model (TCM), a new coordination model for parallel and distributed programming. Our proposal is based on the use of tuple channels (TCs) to model the communication and synchronization of different activities. TCs are multi-point channels that allow complex data structures to be communicated among multiple producers and consumers. This communication model allows incremental and backward communication to be expressed, providing an elegant way of implicit and direct communication and reactive control. TCs can be dynamically interconnected through the use of user-defined connectors, providing great flexibility for the definition of complex and dynamic interaction protocols. TCM also provides a simple service management mechanism, by means of which open systems can be implemented in an appropriate way. The suitability, expressiveness and programming techniques of the model are presented by means of some illustrative examples. In addition, some implementation details of the developed prototypes are sketched and we show the preliminary results demonstrating the efficiency of the proposal.     

A Fault-tolerant model for tuple space coordination in distributed environments

In distributed systems, tuple space is one of the coordination models that significantly maximizes system performance against failure due to its space and time decoupling features. With the growing popularity of distributed computing and increasing complexity in the network, host and link failure occurs frequently, resulting in poor system performance. This article proposes a fault-tolerant model named Tuple Space Replication (TSR) for tuple space coordination in distributed environments. The model introduces a multi-agent system that consists of multiple hosts. Each host in a multi-agent system comprises an agent space with a tuple space for coordination. In this model, we introduce three novel fault-tolerant algorithms for tuple space primitives to provide coordination among hosts with tolerance to multiple links and hosts failure. The first algorithm is given for out() operation to insert tuples in the tuple space. The second algorithm is presented for rdp() operation to read any tuple from the tuple space. The third algorithm is given for inp() operation to delete or withdraw tuples from the tuple space. These algorithms use less number of messages to ensure consistency in the system. The message complexity of the proposed algorithms is analyzed and found O(n) for out(), O(1) for rdp(), and O(n) for inp() operations which is comparable and better than existing works, where n is the number of hosts. The testbed experiment reveals that the proposed TSR model gives performance improvement up to 88%, 70.94%, and 63.80% for out(), rdp(), and inp() operations compared to existing models such as FT-SHE, LBTS, DEPSPACE, and E-DEPSPACE.     

On null controllability of linear systems in Banach spaces

Our aim here is to give characterizations for the null controllability of linear systems in general Banach spaces. The starting point of this paper is the work of Pengnian Chen and Huashu Qin (Systems Control Lett. 45 (2002) 155), which solves the problem of exact controllability. In fact, the results of (Systems Control Lett. 45 (2002) 155) say that, in case of setting in general Banach spaces, there are the same characterizations of exact controllability as in the case of reflexive Banach spaces. An open problem raised in (Systems Control Lett. 45 (2002) 155) is the validity of a similar result for null controllability. We give here a positive answer to this problem. However, it seems to us that the technique of (Systems Control Lett. 45 (2002) 155) does not work for null controllability, and, consequently, our approach is completely different.     

Version spaces and the consistency problem

A version space is a collection of concepts consistent with a given set of positive and negative examples. Mitchell [Artificial Intelligence 18 (1982) 203-226] proposed representing a version space by its boundary sets: the maximally general (G) and maximally specific consistent concepts (S). For many simple concept classes, the size of G and S is known to grow exponentially in the number of positive and negative examples. This paper argues that previous work on alternative representations of version spaces has disguised the real question underlying version space reasoning. We instead show that tractable reasoning with version spaces turns out to depend on the consistency problem, i.e., determining if there is any concept consistent with a set of positive and negative examples. Indeed, we show that tractable version space reasoning is possible if and only if there is an efficient algorithm for the consistency problem. Our observations give rise to new concept classes for which tractable version space reasoning is now possible, e.g., 1-decision lists, monotone depth two formulas, and halfspaces.     

Prominence convergence in the collective synchronization of situated multi-agents

In previous work on collective synchronization of multi-agents, they always follow the assumptions that the synchronization is flat where all agents have the same synchronization capacity and the final synchronization result always converges on a common average strategy. However, in many circumstances the above assumption does not match the peculiarities of real multi-agent societies where each agent plays a different role in the synchronization. To make up the restrictions of related work, this paper presents a non-flat synchronization model where the synchronization capacity of each agent is different regarding its social rank and strategy dominance. In the presented model, all agents are situated in a synchronization field where each agent can sense the collective synchronization forces from other agents; if some agents are more prominent than other ordinary agents (e.g., they have the dominance of social ranks or behavior strategies), they will have strong synchronization capacities in the field; and finally the collective synchronization results may incline to converge at such prominent agents' strategies, which is called prominence convergence in collective synchronization and can be proved by our theoretical analyses and experimental results.     

On solving the minimization problem and the fixed-point problem for a finite family of non-expansive mappings in CAT(0) spaces

In this paper, we propose a new modified proximal point algorithm for a finite family of non-expansive mappings in the framework of CAT(0) spaces. We establish Δ-convergence and strong convergence theorems under some mild conditions. Our results extend some known results which appeared in the literature.     

On the triangle inequalities in fuzzy metric spaces

This paper presents level forms of the triangle inequalities in fuzzy metric spaces (X, d, L, R). To aid discussion, a fuzzy pre-metric condition is introduced. It is first pointed out that under the fuzzy pre-metric condition the first triangle inequality is always equivalent to its level form. The second triangle inequality is equivalent to one level form when R is right continuous, and to another level form also when further conditions are imposed on R. In a fuzzy metric space, the level form of the first triangle inequality and one of the level forms of the second triangle inequality are always valid. The other level form of the second triangle inequality holds for all but at most countable α ∈ [0, 1). Finally, a fixed point theorem for fuzzy metric spaces is derived as an application of the preceding results.     

On the linear-quadratic regulator problem in one-dimensional linear fractional stochastic systems

In this paper, for one-dimensional stochastic linear fractional systems in terms of the Riemann–Liouville fractional derivative, the optimal control is derived. It is assumed that the state is completely observable and all the information regarding this is available. The formulation leads to a set of stochastic fractional forward and backward equation in the Riemann–Liouville sense. The proposed method has been checked via some numerical simulations which show the effectiveness of the fractional stochastic optimal algorithm.     

Further results on dissimilarity spaces for hyperspectral images RF-CBIR

Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance feedback (RF) is an iterative process that uses machine learning techniques and user’s feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions defined either on spectral and spatial features extracted by spectral unmixing techniques, or on dictionaries extracted by dictionary-based compressors. These dissimilarity functions were not suitable for direct application in common machine learning techniques. We propose to use a RF general approach based on dissimilarity spaces which is more appropriate for the application of machine learning algorithms to the hyperspectral RF-CBIR. We validate the proposed RF method for hyperspectral CBIR systems over a real hyperspectral dataset.     

On the blow-up criterion for the quasi-geostrophic equations in homogeneous Besov spaces

In this paper, we consider the blow-up criterion for the quasi-geostrophic equations with dissipation ${\Lambda }^{\gamma }$ ($0<\gamma <1$). By establishing a new trilinear estimate, we show that if

$\theta \in L^{\frac{\gamma }{\gamma +s?1}}(0,T;{\stackrel{?}{B}}_{\infty ,\infty }^s\left({\mathbb{R}}^2\right))$

for some $s\in \left(1?\frac{\gamma }{2},1\right)$, then the solution can be extended smoothly past $T$. This improves and extends the corresponding results in Dong and Pavlovi? (2009) ([32]) and Yuan (2010).     

Asymptotic estimates for the higher moments of the expected behavior of straight insertion sort

Perfect elimination (PE) bipartite graphs were introduced by Golumbic and Goss where they were shown to represent sparse matrices (non-symmetric) on which Gaussian elimination may be performed without fill-in. We propose an O(n³) recognition algorithm for PE bipartite graphs which improves on the O(n⁵) procedure suggested by Golumbic and Goss for this problem.     

Dynamic hierarchical control for distributed problem solving

Distributed problem solving (DPS) has become one of the central topics in AI. Much research has been concerned with finding an appropriate distributed control regime. We propose the concept of dynamic, hierarchical control (DHC) for distributed problem solving.

In many domains, DPS has a natural hierarchy or a subproblem hierarchy can be imposed by utilizing appropriate decomposition techniques. DHC aims at exploiting the power inherent in the hierarchical approach. It also enables the control of the problem solving process to fit the structure of a domain. This results in well-coordinated cooperation and coherent negotiation among distributed controllers.

We have used the DHC to perform plant combine production planning (PCPP). This task involves the activity of a network of production units, each with possibly different characteristics, collaborating to produce various items under dynamically changing conditions. We also describe the general structure of a single controller which adopts a blackboard and a knowledge source (KS) scheduling mechanism to carry out dynamic process execution.

The paper describes the results of our first solution to this problem. Finally, we discuss on-going research that aims at handling additional problems.     

On stochastic Riccati equations for the stochastic LQR problem

This paper studies an approximation of stochastic Riccati equations for stochastic LQR problems some of which may be even with indefinite control weight costs.     

A distributed solution to the network reconstruction problem

It has been recently shown in Ren et al. (2010) that by collecting noise-contaminated time series generated by a coupled-oscillator system at each node of a network, it is possible to robustly reconstruct its topology, i.e. determine the graph Laplacian. Restricting ourselves to linear consensus dynamics over undirected communication networks, in this paper we introduce a new dynamic average consensus least-squares algorithm to locally estimate these time series at each node, thus making the reconstruction process fully distributed and more easily applicable in the real world. We also propose a novel efficient method for separating the off-diagonal entries of the reconstructed Laplacian, and examine several concepts related to the trace of the dynamic correlation matrix of the coupled single integrators, which is a distinctive element of our network reconstruction method. The theory is illustrated with examples from computer, power and transportation systems.     

Optimal quadratic solution for the non-Gaussian finite-horizon regulator problem

In this paper, the problem of the optimal quadratic regulator for non-Gaussian discrete-time stochastic systems with a quadratic cost function is considered. The main result here obtained is that such optimal control can be derived from the classical LQG solution by substituting the linear filtering part with a quadratic optimal filter. Numerical results show high performance of this method.     

On the stability problem for distributed parameter energy systems†

In this paper asymptotic stability of the non-trivial or operating equilibrium state of a distributed parameter energy system is considered. Sufficient conditions are obtained by the method of comparison functions. This enables one to obtain bounds on the maximum value of the state variables. The criteria derived depend on the eigenvalues of the linearized system as well as on a bound for the feedback control law.