The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The books main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology

An Introduction to MultiAgent Systems.

The determination of upper bounds on execution times, commonly called worst-case execution times (WCETs), is a necessary step in the development and validation process for hard real-time systems. This problem is hard if the underlying processor architecture has components, such as caches, pipelines, branch prediction, and other speculative components. This article describes different approaches to this problem and surveys several commercially available tools1 and research prototypes.

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The worst-case execution-time problem—overview of methods and survey of tools

Eingebettete Echtzeitsysteme mussen eine Vielzahl von Zeit- und Performanzanforderungen erfullen, z.B. maximale Reaktionszeiten oder vorgegebene Kommunikationsbandbreiten. Die Echtzeiteigenschaften hangen stark vom Zusammenspiel der Einzelkomponenten sowie deren Scheduling ab. Unglucklicherweise fuhrt gerade die in der Praxis etablierte Wiederverwendung von spezialisierten Komponenten zu einer Heterogenitat, die die Schedulinganalyse zusatzlich erschwert. Die heute eingesetzten Simulationsverfahren sind zusehends unzuverlassig, da die kritischen Randfalle in der Praxis kaum mehr vollstandig bestimmt werden konnen. Als Alternative wurde eine Vielzahl systematischer und formaler Ansatze vorgeschlagen. Meist sind diese jedoch entweder auf spezielle Teilprobleme beschrankt oder fur den Allgemeinfall zu unhandlich und finden daher nur eine geringe Akzeptanz in der industriellen Praxis. In dieser Arbeit wird ein neues Verfahren zur Schedulinganalyse vorgestellt, das a) die steigende Komplexitat und Heterogenitat angemessen erfasst, b) uber die Modularitat und Flexibilitat verfugt, die mit Wiederverwendung und Integration erforderlich ist, und c) die Integration durch ein nachvollziehbares Analysemodell unterstutzt. Das Analysemodell erfasst die komplexen Abhangigkeiten zwischen Komponenten mit Hilfe von intuitiven, standardisierten Ereignismodellen. Die klare Strukturierung dieser Schnittstellen erlaubt erstmals die modulare Komposition von Analysen heterogener Systemteile. Dies gibt Entwicklern die notige Flexibilitat, ihre bevorzugten lokalen Entwurfsmethoden zu benutzen, ohne auf die globale Schedulinganalyse verzichten zu mussen. Das Verfahren bildet die Grundlage fur das SymTA/S Analysewerkzeug und ist in der Praxis sehr effizient einsetzbar, womit sich eine ernstzunehmende und viel versprechende Erganzung zur heute etablierten Performanz-Simulation eroffnet.

Compositional Scheduling Analysis Using Standard Event Models

Due to the ability to handle specific characteristics of economics and finance forecasting problems like e.g. non-linear relationships, behavioral changes, or knowledge-based domain segmentation, we have recently witnessed a phenomenal growth of the application of computational intelligence methodologies in this field. In this volume, Chen and Wang collected not just works on traditional computational intelligence approaches like fuzzy logic, neural networks, and genetic algorithms, but also examples for more recent technologies like e.g. rough sets, support vector machines, wavelets, or ant algorithms. After an introductory chapter with a structural description of all the methodologies, the subsequent parts describe novel applications of these to typical economics and finance problems like business forecasting, currency crisis discrimination, foreign exchange markets, or stock markets behavior.

Computational Intelligence in Economics and Finance

Complex embedded systems consist of hardware and software components from different domains, such as control and signal processing, many of them supplied by different IP vendors. The embedded system designer faces the challenge to integrate, optimize and verify the resulting heterogeneous systems. While format verification is available for some subproblems, the analysis of the whole system is currently limited to simulation or emulation. In this paper we tackle the analysis of global resource sharing, scheduling, and buffer sizing in heterogeneous embedded systems. For many practically used preemptive and non-preemptive hardware and software scheduling algorithms of processors and busses, semi-formal analysis techniques are known. However they cannot be used in system level analysis due to incompatibilities of their underlying event models. This paper presents a technique to couple the analysis of local scheduling strategies via an event interface model. We derive transformation rules between the most important event models and provide proofs where necessary. We use expressive examples to illustrate their application.

/pdf/event-model-interfaces-for-heterogeneous-system-analysis-2ve2im3k6a.pdf

Event Model Interfaces for Heterogeneous System Analysis

Execution cost interval refinement in static software analysis

The timing and power consumption of embedded systems are state- and input data-dependent. A formal analysis of such dependencies leads to intervals rather than single values. These intervals depend on the program properties, execution paths and states of processes, as well as on the target architecture. This paper presents an approach to the analysis of process behaviour using intervals. It improves on previous work by exploiting program segments with single paths and by taking the execution context into account. The example of an ATM cell handler demonstrates significant improvements in analysis precision.

/pdf/intervals-in-software-execution-cost-analysis-k6qyw0brmi.pdf

Intervals in software execution cost analysis

Timing and power measurement in static software analysis

The presented methodologies have been implemented in the SYMTA tool suite which has been applied in a variety of experiments including path analysis, cache analysis and architecture modeling. Experiments with the single tools as well as overall process-level analysis of running time, power consumption and communicated data intervals are presented in this chapter. The first part focuses on general behavioral interval analysis before detailed software power analysis results are presented in the second part.

Experiments and Results

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Execution cost interval refinement in static software analysis

Intervals in software execution cost analysis

Timing and power measurement in static software analysis

Experiments and Results