Using Process Technology to Control and Coordinate Software Adaptation

We have developed an infrastructure for end-to-end run-time monitoring, behavior/performance analysis, and dynamic adaptation of distributed software. This infrastructure is primarily targeted to pre-existing systems and thus operates outside the target application, without making assumptions about the target's implementation, internal communication/computation mechanisms, source code availability, etc. This paper assumes the existence of the monitoring and analysis components, presented elsewhere, and focuses on the mechanisms used to control and coordinate possibly complex repairs/reconfigurations to the target system. These mechanisms require lower level effectors somehow attached to the target system, so we briefly sketch one such facility (elaborated elsewhere). Our main contribution is the model, architecture, and implementation of Workflakes, the decentralized process engine we use to tailor, control, coordinate, etc. a cohort of such effectors. We have validated the Workflakes approach with case studies in several application domains. Due to space restrictions we concentrate primarily on one case study, briefly discuss a second, and only sketch others

Orchestrating the Dynamic Adaptation of Distributed Software with Process Technology

Software systems are becoming increasingly complex to develop, understand, analyze, validate, deploy, configure, manage and maintain. Much of that complexity is related to ensuring adequate quality levels to services provided by software systems after they are deployed in the field, in particular when those systems are built from and operated as a mix of proprietary and non-proprietary components. That translates to increasing costs and difficulties when trying to operate large-scale distributed software ensembles in a way that continuously guarantees satisfactory levels of service. A solution can be to exert some form of dynamic adaptation upon running software systems: dynamic adaptation can be defined as a set of automated and coordinated actions that aim at modifying the structure, behavior and performance of a target software system, at run time and without service interruption, typically in response to the occurrence of some condition(s). To achieve dynamic adaptation upon a given target software system, a set of capabilities, including monitoring, diagnostics, decision, actuation and coordination, must be put in place. This research addresses the automation of decision and coordination in the context of an end-to-end and externalized approach to dynamic adaptation, which allows to address as its targets legacy and component-based systems, as well as new systems developed from scratch. In this approach, adaptation provisions are superimposed by a separate software platform, which operates from the outside of and orthogonally to the target application as a whole; furthermore, a single adaptation possibly spans concerted interventions on a multiplicity of target components. To properly orchestrate those interventions, decentralized process technology is employed for describing, activating and coordinating the work of a cohort of software actuators, towards the intended end-to-end dynamic adaptation. The approach outlined above, has been implemented in a prototype, code-named Workflakes, within the Kinesthetics eXtreme project investigating externalized dynamic adaptation, carried out by the Programming Systems Laboratory of Columbia University, and has been employed in a set of diverse case studies. This dissertation discusses and evaluates the concept of process-based orchestration of dynamic adaptation and the Workflakes prototype on the basis of the results of those case studies

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Ein verteilter und agentenbasierter Ansatz für gekoppelte Probleme der rechnergestützten Ingenieurwissenschaften

Challenging questions in science and engineering often require to decouple a complex problem and to focus on isolated sub-problems first. The knowledge of those individual solutions can later be combined to obtain the result for the full question. A similar technique is applied in numerical modeling. Here, the software solver for subsets of the coupled problem might already exist and can directly be used. This thesis describes a software environment capable of combining multiple software solvers, the result being a new, combined model. Two important design decisions were crucial at the beginning: First, every sub-model keeps full control of its execution. Second, the source code of the sub-model requires only minimal adaptation. The sub-models choose themselves when to issue communication calls, with no outer synchronisation mechanism required. The coupling of heterogeneous hardware is supported as well as the use of homogeneous compute clusters. Furthermore, the coupling framework allows sub-solvers to be written in different programming languages. Also, each of the sub-models may operate on its own spatial and temporal scales. The next challenge was to allow the potential coupling of thousands software agents, being able to utilise today's petascale hardware. For this purpose, a specific coupling framework was designed and implemented, combining the experiences from the previous work with additions required to cope with the targeted number of coupled sub-models. The large number of interacting models required a much more dynamic approach, where the agents automatically detect their communication partners at runtime. This eliminates the need to explicitly specify the coupling graph a~priori. Agents are allowed to enter (and leave) the simulation at any time, with the coupling graph changing accordingly.Da viele Problemstellungen im Ingenieurwesen sehr komplex sind, ist es oft sinnvoll, sie in einzelne Teilprobleme aufzugliedern. Diese Teilbereiche können nun einzeln angegangen und dann zur Gesamtlösung kombiniert werden. Ein ähnlicher Ansatz wird bei der numerischen Modellierung verfolgt: Komplexe Software wird schrittweise erstellt, indem Software-Löser für einzelne Bereiche zuerst separat erarbeitet werden. In dieser Arbeit wird eine Software beschrieben, die eine Vielzahl von unabhängigen Software-Lösern kombinieren kann. Jedes Teilmodell verhält sich weiterhin wie ein selbständiges Programm. Hierfür wird es in einen Software-Agenten gehüllt. Zur Kopplung sind lediglich minimale Ergänzungen am Quellcode des Teilmodells nötig. Möglich wird dies durch die Struktur der Kommunikation zwischen den Teilmodellen. Sie lässt den Modellen die Kontrolle über die Kommunikationsaufrufe und benötigt zur Synchronisation keine Einflussnahme einer übergeordneten Instanz. Manche Teilmodelle sind für den Gebrauch mit einer speziellen Hardware optimiert. Daher musste das Zusammenspiel unterschiedlicher Hardware ebenso berücksichtigt werden wie homogene Rechencluster. Weiterhin ermöglicht das Kopplungs-Framework, dass unterschiedliche Programmiersprachen verbunden werden können. Wie schon der Programmablauf, so können auch die Modellparameter, etwa die räumliche und zeitliche Skala, von Teilmodell zu Teilmodell unterschiedlich bleiben. Weiter behandelt diese Arbeit eine Vorgehensweise um tausende von Software-Agenten zu einem Groß-Modell zu koppeln. Dies ist erforderlich, wenn die Ressourcen heutiger Petascale Rechencluster benutzt werden sollen. Hierzu wurde das bisherige Framework neu aufgelegt, da die große Anzahl von zu koppelnden Modellen einer wesentlich dynamischeren Kommunikationsstruktur bedarf. Die Agenten der Teilmodelle können einer laufenden Simulation hinzugefügt werden (oder diese verlassen) und die globalen Kopplungsbeziehungen passen sich dementsprechend an

Plan Acquisition Through Intentional Learning in BDI Multi-Agent Systems

Multi-Agent Systems (MAS), a technique emanating from Distributed Artificial Intelligence, is a suitable technique to study complex systems. They make it possible to represent and simulate both elements and interrelations of systems in a variety of domains. The most commonly used approach to develop the individual components (agents) within MAS is reactive agency. However, other architectures, like cognitive agents, enable richer behaviours and interactions to be captured and modelled. The well-known Belief-Desire-Intentions architecture (BDI) is a robust approach to develop cognitive agents and it can emulate aspects of autonomous behaviour and is thus a promising tool to simulate social systems. Machine Learning has been applied to improve the behaviour of agents both individually or collectively. However, the original BDI model of agency, is lacking learning as part of its core functionalities. To cope with learning, the BDI agency has been extended by Intentional Learning (IL) operating at three levels: belief adjustment, plan selection, and plan acquisition. The latter makes it possible to increase the agent’s catalogue of skills by generating new procedural knowledge to be used onwards. The main contributions of this thesis are: a) the development of IL in a fully-fledged BDI framework at the plan acquisition level, b) extending IL from the single-agent case to the collective perspective; and c) a novel framework that melts reactive and BDI agents through integrating both MAS and Agent-Based Modelling approaches, it allows the configuration of diverse domains and environments. Learning is demonstrated in a test-bed environment to acquire a set of plans that drive the agent to exhibit behaviours such as target-searching and left-handed wall-following. Learning in both decision strata, single and collective, is tested in a more challenging and socially relevant environment: the Disaster-Rescue problem

A trust based approach to mobile multi-agent systems.

This thesis undertakes to provide an architecture and understanding of the incorporation of trust into the paradigm of mobile multi-agent systems. Trust deliberation is a soft security approach to the problem of mobile agent security whereby an agent is protected from the malicious behaviour of others within the system. Using a trust approach capitalises on observing malicious behaviour rather than preventing it. We adopt an architectural approach to trust such than we do not provide a model in itself, numerous mathematical models for the calculation of trust based on a history of observations already exist. Rather we look to provide the framework enabling such models to be utilised by mobile agents. As trust is subjective we envisage a system whereby individual agents will use different trust models or different weighting mechanisms. Three architectures are provided. Centralised whereby the platform itself provides all of the services needed by an agent to make observations and calculate trust. Decentralised in which each individual agent is responsible for making observations, communicating trust and the calculation of its own trust in others. A hybrid architecture such that trust mechanisms are provided by the platform and additionally are embedded within the agents themselves. As an optimisation of the architectures proposed in this thesis, we introduce the notion of trust communities. A community is used as a means to represent the trust information in categorisations dependant upon various properties. Optimisation occurs in two ways; firstly with subjective communities and secondly with system communities. A customised implementation framework of the architectures is introduced in the form of our TEMPLE (Trust Enabled Mobile-agent PLatform Environment) and stands as the underpinning of a case-study implementation in order to provide empirical evidence in the form of scenario test-bed data as to the effectiveness of each architecture. The case study chosen for use in a trust based system is that of a fish market' as given the number of interactions, entities, and migration of agents involved in the system thus, providing substantial output data based upon the trust decisions made by agents. Hence, a good indicator of the effectiveness of equipping agents with trust ability using our architectures

Ninth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools, Aarhus, Denmark, October 20-22, 2008

This booklet contains the proceedings of the Ninth Workshop on Practical Use of Coloured Petri Nets and the CPN Tools, October 20-22, 2008. The workshop is organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark. The papers are also available in electronic form via the web pages: http://www.daimi.au.dk/CPnets/workshop0

Logic-based Technologies for Multi-agent Systems: A Systematic Literature Review

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computerscientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI” – in particular, logic-based ones will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones