Grid-Computing

Grid-Computing, ein Mitte der 90er-Jahre eingeführter Begriff [1,2], bezeichnet eine Architektur für verteilte Systeme, die auf dem World Wide Web aufbaut und die Web-Vision erweitert. Mit dem Grid-Computing werden die Ressourcen einer Gemeinschaft, einer so genannten virtuellen Organisation, integriert. Die Hoffnung ist, dass hierdurch rechen- und/oder datenintensiven Aufgaben, die eine einzelne Organisation nicht lösen kann, handhabbar werden. Ein Grid bezeichnet eine nach dem Grid-Computing-Ansatz aufgebaute Rechner-, Netzwerk- und Software-Infrastruktur zur Teilung von Ressourcen mit dem Ziel, die Aufgaben einer virtuellen Organisation zu erledigen. * Vorschläge an Prof.Dr. Frank Puppe <puppe@informatik.uni-wuerzburg.de> oder Dieter Steinbauer <dieter.steinbauer@schufa.de>Alle Aktuellen Schlagwörter seit 1988 finden Sie unter: www.ai-wuerzburg.de/as     

Modelling structures in generic space, a condition for adaptiveness of monitoring cognitive agent

The adaptiveness of agents is one of the basic conditions for the autonomy. This paper describes an approach of adaptiveness forMonitoring Cognitive Agents based on the notion of generic spaces. This notion allows the definition of virtual generic processes so that any particular actual process is then a simple configuration of the generic process, that is to say a set of values of parameters. Consequently, generic domain ontology containing the generic knowledge for solving problems concerning the generic process can be developed. This lead to the design of Generic Monitoring Cognitive Agent, a class of agent in which the whole knowledge corpus is generic. In other words, modeling a process within a generic space becomes configuring a generic process and adaptiveness becomes genericity, that is to say independence regarding technology. In this paper, we present an application of this approach on Sachem, a Generic Monitoring Cognitive Agent designed in order to help the operators in operating a blast furnace. Specifically, the NeuroGaz module of Sachem will be used to present the notion of a generic blast furnace. The adaptiveness of Sachem can then be noted through the low cost of the deployment of a Sachem instance on different blast furnaces and the ability of NeuroGaz in solving problem and learning from various top gas instrumentation.     

Software engineering education: issues and alternatives

Postgraduate degree programs in software engineering have been in existence for some time and now undergraduate degree programs with this title are beginning to appear. A number of questions and issues of only moderate importance with respect to the postgraduate programs now become critical and of overriding importance. While one could (and did) by and large gloss over these issues earlier, this will be much more difficult, and probably impossible, in the future. These issues must be resolved in a wider context than that in which they have been dealt with before, involving engineering in the universities and professional engineering societies in the larger social context. Much of the disagreement regarding these issues can, ultimately, be traced back to differing fundamental views and definitions of the term engineering and whether software engineering should be treated as just another engineering discipline or in a significantly and fundamentally different way. After examining two different definitions and views of engineering, this paper states and discusses a number of the questions and issues which planners of new undergraduate software engineering degree programs must deal with. Some pros and cons of various alternative answers are presented and a few answers suggested. The issues discussed here begin with questions of general policy and concept (e.g., which culture – that of the traditional engineering disciplines or that of computer science – should be instilled in the students in these new degree programs), include organizational matters (e.g., the position of the new degree program in the university hierarchy) and end with selected, more detailed questions regarding the curriculum (e.g., how to deal with programming, design and management topics). It is conjectured that, although the scientific foundation for the undergraduate software engineering degree programs must and will come from computer science, their culture and orientation must come from engineering if they and their graduates are to be successful in satisfying society's needs in the long term.     

Redesign of a Systems Engineering Language: Formalisation of χ

This paper describes the redesign of a systems engineering language called . This is an engineering language designed to specify and analyse industrial systems. The main objective of this redesign was to enable mathematical reasoning about specifications. We discuss the original language, the requirements and design decisions, and the resulting syntax and semantics of the new version of , called . In particular, we elaborate on semantical aspects of s time model.     

Rapid Concept Learning for Mobile Robots

Concept learning in robotics is an extremely challenging problem: sensory data is often high-dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce features such as opening or wall. The second task is recycling, where the robot has to learn to recognize objects, such as a trash can. We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.     

Toward Third Generation Threaded Discussions for Mobile Learning: Opportunities and Challenges for Ubiquitous Collaborative Environments

The mobile communication revolution has led to pervasive connectedness—as evidenced by the explosive growth of instant messaging in the home, and more recently, the enterprise–and, together with the convergence of mobile computing, provides a basis for extending collaborative environments toward truly ubiquitous immersion. Leveraging the true anytime/anywhere access afforded by mobile computing, it becomes possible to develop applications that not only are capable of responding to users whenever/wherever, on demand, but that also may actively seek out and engage users when the need arises. Thus, immersive environments need no longer be thought of strictly in terms of physical immersion with clearly discernable enter and exit events, but rather they may be extended, through mobile-enabled computing, toward ubiquity in terms of both time and space. Based on Media Synchronicity Theory, potential benefits are envisioned, particularly in the case of collaborative learning environments, from shortened response cycles and increased real time interaction opportunities. At the same time, a number of challenging issues must be addressed in designing such an environment to ensure user acceptance and to maximize realization of the potential. Third Generation (3G) Threaded Discussion has been conceptualized as an environment, well suited to mobile learning (m-learning) that could leverage mobile-enabled ubiquity to achieve a degree of extended immersion and thereby accrue the associated collaboration benefits. Exploring this conceptualization serves to help surface both the opportunities and the challenges associated with such environments and to identify promising design approaches, such as the use of intelligent agents.This revised version was published online in March 2005 with corrections to the cover date     

Separating complexity classes related to bounded alternating ω-branching programs

We develop a theory of communication within branching programs that provides exponential lower bounds on the size of branching programs that are bounded alternating. Our theory is based on the algebraic concept of -branching programs, : , a semiring homomorphism, that generalizes ordinary branching programs, -branching programs [M2] andMOD _p-branching programs [DKMW].Due to certain exponential lower and polynomial upper bounds on the size of bounded alternating -branching programs we are able to separate the corresponding complexity classesN _ba ,co-N _{ba ba} , andMOD _p - _ba ,p prime, from each other, and from that classes corresponding to oblivious linear length-bounded branching programs investigated in the past.     

Local determinacy of abstract local dynamical systems

O. Hájek proved in his book Dynamical Systems in the Plane (Chapter III) that there isat most one abstract local dynamical system which is locally equivalent to, or equivalently an extension of, a given elementary dynamical system, and suggested a question of finding reasonable conditions on the latter for the existence ofat least one such abstract local dynamical system. An elementary dynamical system is said to satisfy the No-Intersection Axiom and is called an abstract germ if(x ₁, t) = (x ₂,t) impliesx ₁ =x ₂. We show that is (uniquely) extendable to an abstract local dynamical system if and only if is an abstract germ, and hence the question is completely answered. After introducing various kinds of isomorphisms of abstract germs and abstract local dynamical systems corresponding to those of continuous germs and continuous local dynamical systems, we obtain some sufficient conditions for extendability of isomorphisms and possibility of restriction of them, and thus establish the local determinacy of abstract local dynamical systems up to isomorphisms in some wider categories.Dedicated to Professor Yusuke HAGIHARA in Commemoration of His Seventy-Seventh Anniversary     

General Convergence Results for Linear Discriminant Updates

The problem of learning linear-discriminant concepts can be solved by various mistake-driven update procedures, including the Winnow family of algorithms and the well-known Perceptron algorithm. In this paper we define the general class of quasi-additive algorithms, which includes Perceptron and Winnow as special cases. We give a single proof of convergence that covers a broad subset of algorithms in this class, including both Perceptron and Winnow, but also many new algorithms. Our proof hinges on analyzing a generic measure of progress construction that gives insight as to when and how such algorithms converge.Our measure of progress construction also permits us to obtain good mistake bounds for individual algorithms. We apply our unified analysis to new algorithms as well as existing algorithms. When applied to known algorithms, our method automatically produces close variants of existing proofs (recovering similar bounds)—thus showing that, in a certain sense, these seemingly diverse results are fundamentally isomorphic. However, we also demonstrate that the unifying principles are more broadly applicable, and analyze a new class of algorithms that smoothly interpolate between the additive-update behavior of Perceptron and the multiplicative-update behavior of Winnow.     

Stable adaptive control for a plant with randomly varying parameters

Adaptive control is considered for a two-dimensional linear discrete-time plant with randomly drifting parameters. The certainty equivalent minimum variance control law along with the projection-like identification algorithm are used. The stability of the parameter estimates and exponential stability of the closed-loop system are proved in the absence of any persistent excitation assumption.     

Human–Machine Interaction in Intelligent Robotic Systems: A Unifying Consideration with Implementation Examples

In this paper, we propose a two-layer sensor fusion scheme for multiple hypotheses multisensor systems. To reflect reality in decision making, uncertain decision regions are introduced in the hypotheses testing process. The entire decision space is partitioned into distinct regions of correct, uncertain and incorrect regions. The first layer of decision is made by each sensor indepedently based on a set of optimal decision rules. The fusion process is performed by treating the fusion center as an additional virtual sensor to the system. This virtual sensor makes decision based on the decisions reached by the set of sensors in the system. The optimal decision rules are derived by minimizing the Bayes risk function. As a consequence, the performance of the system as well as individual sensors can be quantified by the probabilities of correct, incorrect and uncertain decisions. Numerical examples of three hypotheses, two and four sensor systems are presented to illustrate the proposed scheme.     

Mobile Agent-Based Management in the INSERT Project

This paper describes work carried out atUniversity College London to evaluate the use of MobileAgents (MA), written in Java, in network and systemmanagement. The work draws on experience fromconventional management systems based on ISO CommonManagement Information Protocol (CMIP) andInternet Simple Network ManagementProtocol (SNMP). These systems make use of theconcept of a Managed Object (MO). The paper describes the way in which the MOconcept has been translated into a form suitable forMA-based management and how the features of the Javalanguage may be used to express management information and to implement MOs. A platform has beenimplemented which allows MOs to be instantiated,controlled and stored. The platform also allows the easyproduction of MAs, their launching into the system and their subsequent management. Details of theplatform implementation are given and an initialassessment of performance is presented.     

Enhancing the performance of an agent-based manufacturing system through learning and forecasting

Agent-based technology has been identified as an important approach for developing next generation manufacturing systems. One of the key techniques needed for implementing such advanced systems will be learning. This paper first discusses learning issues in agent-based manufacturing systems and reviews related approaches, then describes how to enhance the performance of an agent-based manufacturing system through learning from history (based on distributed case-based learning and reasoning) and learning from the future (through system forecasting simulation). Learning from history is used to enhance coordination capabilities by minimizing communication and processing overheads. Learning from the future is used to adjust promissory schedules through forecasting simulation, by taking into account the shop floor interactions, production and transportation time. Detailed learning and reasoning mechanisms are described and partial experimental results are presented.     

Shallow circuits and concise formulae for multiple addition and multiplication

A theory is developed for the construction of carry-save networks with minimal delay, using a given collection of carry-save adders each of which may receive inputs and produce outputs using several different representation standards.The construction of some new carry-save adders is described. Using these carry-save adders optimally, as prescribed by the above theory, we get {, , }-circuits of depth 3.48 log₂ n and {, , }-circuits of depth 4.95 log₂ n for the carry-save addition ofn numbers of arbitrary length. As a consequence we get multiplication circuits of the same depth. These circuits put out two numbers whose sum is the result of the multiplication. If a single output number is required then the depth of the multiplication circuits increases respectively to 4.48 log₂ n and 5.95 log₂ n.We also get {, , }-formulae of sizeO (n ^3.13) and {, }-formulae of sizeO (n ^4.57) for all the output bits of a carry-save addition ofn numbers. As a consequence we get formulae of the same size for the majority function and many other symmetric Boolean functions.     

Phase transitions in associative memory networks

Ideas from random graph theory are used to give an heuristic argument that associative memory structure depends discontinuously on pattern recognition ability. This argument suggests that there may be a certain minimal size for intelligent systems.     

On characterizing the “knee” of the Pareto curve based on Normal-Boundary Intersection

This paper deals with the issue of generating one Pareto optimal point that is guaranteed to be in a desirable part of the Pareto set in a given multicriteria optimization problem. A parameterization of the Pareto set based on the recently developed normal-boundary intersection technique is used to formulate a subproblem, the solution of which yields the point of maximum bulge, often referred to as the knee of the Pareto curve. This enables the identification of the good region of the Pareto set by solving one nonlinear programming problem, thereby bypassing the need to generate many Pareto points. Further, this representation extends the concept of the knee for problems with more than two objectives. It is further proved that this knee is invariant with respect to the scales of the multiple objective functions.The generation of this knee however requires the value of each objective function at the minimizer of every objective function (the pay-off matrix). The paper characterizes situations when approximations to the function values comprising the pay-off matrix would suffice in generating a good approximation to the knee. Numerical results are provided to illustrate this point. Further, a weighted sum minimization problem is developed based on the information in the pay-off matrix, by solving which the knee can be obtained.     

Context-free graph languages of bounded degree are generated by apex graph grammars

The apex graph grammars generate precisely the context-free graph languages of bounded degree, independently of whether one considers hyperedge replacement systems or (boundary or confluent) NLC or edNCE graph grammars. The main feature of apex graph grammars is that nodes cannot be passed from nonterminal to nonterminal. The proof is based on a normal form result for arbitrary hyperedge replacement systems that forbids passing chains. This generalizes Greibach Normal Form.     

Networked Collaborative Learning in the Study of Modern History and Literature

Many teachers adopt networked collaborative learning strategies even though these approaches systematically increase the time needed to deal with a given subject. But who's making them do it?. Probably there has to be a return on investment, in terms of time and obviously in terms of educational results, which justifies that commitment. After surveying the particular features of two experimental projects based on networked collaborative learning, the paper will then offer a series of thoughts triggered by observation of the results and the dynamics generated by this specific approach. The purpose of these thoughts is to identify some key factors that make it possible to measure the real added value produced by network collaboration in terms of the acquisition of skills, knowledge, methods and attitudes that go beyond the mere learning of contents (however fundamental this may be). And it is precisely on the basis of these considerations that teachers usually answer the above question, explaining who (or what) made them do it!.     

Regenerative simulation of response times in networks of queues: statistical efficiency

Summary This paper deals with the statistical efficiency of estimation methods for passage times in closed, multiclass networks of queues with priorities. Informally, a passage time is the time for a job to traverse a portion of the network. Such quantities are important in computer and communication system models, and in this context, quantities other than mean values are of interest. We consider here the efficiencies of the marked job method for passage time simulation (based on the tracking of a distinguished job) and the decomposition method in which observed passage times for all of the jobs enter in the construction of point and interval estimates. We show that the decomposition method is superior in that, for simulations of equal length, it produces tighter confidence intervals. We also calculate theoretical values for variance constants entering into central limit theorems used to obtain confidence intervals for mean passage times. These results provide a means of quantifying the relative efficiency of the decomposition method.