Adaptation strategies for self-organising electronic institutions

For large-scale systems and networks embedded in highly dynamic, volatile, and unpredictable environments, self-adaptive and self-organising (SASO) algorithms have been proposed as solutions to the problems introduced by this dynamism, volatility, and unpredictability. In open systems it cannot be guaranteed that an adaptive mechanism that works well in isolation will work well — or at all — in combination with others. In complexity science the emergence of systemic, or macro-level, properties from individual, or micro-level, interactions is addressed through mathematical modelling and simulation. Intermediate meso-level structuration has been proposed as a method for controlling the macro-level system outcomes, through the study of how the application of certain policies, or norms, can affect adaptation and organisation at various levels of the system. In this context, this thesis describes the specification and implementation of an adaptive affective anticipatory agent model for the individual micro level, and a self-organising distributed institutional consensus algorithm for the group meso level. Situated in an intelligent transportation system, the agent model represents an adaptive decision-making system for safe driving, and the consensus algorithm allows the vehicles to self-organise agreement on values necessary for the maintenance of “platoons” of vehicles travelling down a motorway. Experiments were performed using each mechanism in isolation to demonstrate its effectiveness. A computational testbed has been built on a multi-agent simulator to examine the interaction between the two given adaptation mechanisms. Experiments involving various differing combinations of the mechanisms are performed, and the effect of these combinations on the macro-level system properties is measured. Both beneficial and pernicious interactions are observed; the experimental results are analysed in an attempt to understand these interactions. The analysis is performed through a formalism which enables the causes for the various interactions to be understood. The formalism takes into account the methods by which the SASO mechanisms are composed, at what level of the system they operate, on which parts of the system they operate, and how they interact with the population of the system. It is suggested that this formalism could serve as the starting point for an analytic method and experimental tools for a future systems theory of adaptation.Open Acces

Towards cooperative urban traffic management: Investigating voting for travel groups

In den letzten Jahrzehnten haben intelligente Verkehrssysteme an Bedeutung gewonnen. Wir betrachten einen Teilbereich des kooperativen Verkehrsmanagements, nämlich kollektive Entscheidungsfindung in Gruppen von Verkehrsteilnehmern. In dem uns interessierenden Szenario werden Touristen, die eine Stadt besuchen, gebeten, Reisegruppen zu bilden und sich auf gemeinsame Besuchsziele (Points of Interest) zu einigen. Wir konzentrieren uns auf Wählen als Gruppenentscheidungsverfahren. Unsere Fragestellung ist, wie sich verschiedene Algorithmen zur Bildung von Reisegruppen und zur Bestimmung gemeinsamer Reiseziele hinsichtlich der System- und Benutzerziele unterscheiden, wobei wir als Systemziel große Gruppen und als Benutzerziele hohe präferenzbasierte Zufriedenheit und geringen organisatorischen Aufwand definieren. Wir streben an, einen Kompromiss zwischen System- und Benutzerzielen zu erreichen. Neu ist, dass wir die inhärenten Auswirkungen verschiedener Wahlregeln, Wahlprotokolle und Gruppenbildungsalgorithmen auf Benutzer- und Systemziele untersuchen. Altere Arbeiten zur kollektiven Entscheidungsfindung im Verkehr konzentrieren sich auf andere Zielgrößen, betrachten nicht die Gruppenbildung, vergleichen nicht die Auswirkungen mehrerer Wahlalgorithmen, benutzen andere Wahlalgorithmen, berücksichtigen nicht klar definierte Gruppen von Verkehrsteilnehmern, verwenden Wahlen für andere Anwendungen oder betrachten andere Algorithmen zur kollektiven Entscheidungsfindung als Wahlen. Wir untersuchen in der Hauptsimulationsreihe verschiedene Gruppenbildungsalgorithmen, Wahlprotokolle und Komiteewahlregeln. Wir betrachten sequentielle Gruppenbildung vs. koordinierte Gruppenbildung, Basisprotokoll vs. iteratives Protokoll und die Komiteewahlregeln Minisum-Approval, Minimax-Approval und Minisum-Ranksum. Die Simulationen wurden mit dem neu entwickelten Simulationswerkzeug LightVoting durchgef¨uhrt, das auf dem Multi-Agenten-Framework LightJason basiert. Die Experimente der Hauptsimulationsreihe zeigen, dass die Komiteewahlregel Minisum-Ranksum in den meisten Fällen bessere oder ebenso gute Ergebnisse erzielt wie die Komiteewahlregeln Minisum-Approval und Minimax-Approval. Das iterative Protokoll tendiert dazu, eine Verbesserung hinsichtlich der präferenzbasierten Zufriedenheit zu erbringen, auf Kosten einer deutlichen Verschlechterung hinsichtlich der Gruppengröße. Die koordinierte Gruppenbildung tendiert dazu, eine Verbesserung hinsichtlich der präferenzbasierten Zufriedenheit zu erbringen bei relativ geringen Kosten in Bezug auf die Gruppengröße. Dies führt uns dazu, die Komiteewahlregel Minisum-Ranksum, das Basisprotokoll und die koordinierte Gruppenbildung zu empfehlen, um einen Kompromiss zwischen System- und Benutzerzielen zu erreichen. Wir demonstrieren auch die Auswirkungen verschiedener Kombinationen von Gruppenbildungsalgorithmen und Wahlprotokollen auf die Reisekosten. Hier bietet die Kombination aus Basisprotokoll und koordinierter Gruppenbildung einen Kompromiss zwischen der präferenzbasierten Zufriedenheit und den Reisekosten. Zusätzlich zur Hauptsimulationsreihe bieten wir ein erweitertes Modell an, das die Präferenzen der Reisenden generiert, indem es die Attraktivität der möglichen Ziele und Distanzkosten, basierend auf den Entfernungen zwischen den möglichen Zielen, kombiniert. Als weiteren Anwendungsfall von Wahlverfahren betrachten wir ein Verfahren zur Treffpunktempfehlung, bei dem eine Bewertungs-Wahlregel und eine Minimax-Wahlregel zur Bestimmung von Treffpunkten verwendet werden. Bei kleineren Gruppen ist die durchschnittliche maximale Reisezeit unter der Bewertungs-Wahlregel deutlich höher. Bei größeren Gruppen nimmt der Unterschied ab. Bei kleineren Gruppen ist die durchschnittliche Verspätung für die Gruppe unter der Minimax-Wahlregel hoch, bei größeren Gruppen nimmt sie ab. Es ist also sinnvoll für kleinere Gruppen, die Minimax-Wahlregel zu verwenden, wenn man eine fairere Verteilung der Reisezeiten anstrebt, und die Bewertungs-Wahlregel zu verwenden, wenn das Ziel stattdessen ist, Verzögerungen für die Gruppe zu vermeiden. Für zukünftige Arbeiten wäre es sinnvoll, das Simulationskonzept anzupassen, um reale Bedingungen und Anforderungen berücksichtigen zu können. Weitere Möglichkeiten für zukünftige Arbeiten wären die Betrachtung zusätzlicher Algorithmen und Modelle, wie zum Beispiel die Betrachtung kombinatorischer Wahlen oder die Durchführung von Simulationen auf der Grundlage des erweiterten Modells, die Berücksichtigung der Rolle finanzieller Anreize zur Förderung von Ridesharing oder Platooning und die Nutzung des LightVoting-Tools für weitere Forschungsanwendungen.In the last decades, intelligent transport systems have gained importance. We consider a subarea of cooperative traffic management, namely collective decision-making in groups of traffic participants. In the scenario we are studying, tourists visiting a city are asked to form travel groups and to agree on common points of interest. We focus on voting as a collective decision-making process. Our question is how different algorithms for the formation of travel groups and for determining common travel destinations differ with respect to system and user goals, where we define as system goal large groups and as user goals high preference satisfaction and low organisational effort. We aim at achieving a compromise between system and user goals. What is new is that we investigate the inherent effects of different voting rules, voting protocols and grouping algorithms on user and system goals. Older works on collective decision-making in traffic focus on other target quantities, do not consider group formation, do not compare the effects of several voting algorithms, use other voting algorithms, do not consider clearly defined groups of vehicles, use voting for other applications or use other collective decision-making algorithms than voting. In the main simulation series, we examine different grouping algorithms, voting protocols and committee voting rules. We consider sequential grouping vs. coordinated grouping, basic protocol vs. iterative protocol and the committee voting rules Minisum-Approval, Minimax-Approval and Minisum-Ranksum. The simulations were conducted using the newly developed simulation tool LightVoting, which is based on the multi-agent framework LightJason. The experiments of the main simulation series show that the committee voting rule Minisum-Ranksum in most cases yields better than or as good results as the committee voting rules Minisum-Approval and Minimax-Approval. The iterative protocol tends to yield an improvement regarding preference satisfaction, at the cost of strong deterioriation regarding the group size. The coordinated grouping tends to yield an improvement regarding the preference satisfaction at relative small cost regarding the group size. This leads us to recommend the committee voting rule Minisum-Ranksum, the basic protocol and coordinated grouping in order to achieve a compromise between system and user goals. We also demonstrate the effect of different combinations of grouping algorithms and voting protocols on travel costs. Here, the combination of the basic protocol and coordinated grouping yields a compromise between preference satisfaction and traveller costs. Additionally to the main simulation series, we provide an extended model which generates traveller preferences by combining attractiveness of the points of interest and distance costs based on the distances between the points of interest. As further application of voting, we consider a meeting-point scenario where a range voting rule and a minimax voting rule are used to agree on meeting points. For smaller groups, the average maximum travel time is clearly higher for range voting. For larger groups, the difference decreases. For smaller groups, the average lateness for the group using minimax voting is high, for larger groups it decreases. Hence, it makes sense for smaller groups to use the minimax voting rule if one aims at fairer distribution of travel times, and to use the range voting rule if the goal is instead to avoid delay for the group. For future work, it would be useful to adapt the simulation concept to take real-world conditions and requirements into account. Further possibilities for future work would be considering additional algorithms and models, such as considering combinatorial voting or running simulations based on the extended model, considering the role of financial incentives to encourage ridesharing or platooning and using the LightVoting tool for further research applications

Multi-agent based simulation of self-governing knowledge commons

The potential of user-generated sensor data for participatory sensing has motivated the formation of organisations focused on the exploitation of collected information and associated knowledge. Given the power and value of both the raw data and the derived knowledge, we advocate an open approach to data and intellectual-property rights. By treating user-generated content as well as derived information and knowledge as a common-pool resource, we hypothesise that all participants can be compensated fairly for their input. To test this hypothesis, we undertake an extensive review of experimental, commercial and social participatory-sensing applications, from which we identify that a decentralised, community-oriented governance model is required to support this open approach. We show that the Institutional Analysis and Design framework as introduced by Elinor Ostrom, in conjunction with a framework for self-organising electronic institutions, can be used to give both an architectural and algorithmic base for the necessary governance model, in terms of operational and collective choice rules specified in computational logic. As a basis for understanding the effect of governance on these applications, we develop a testbed which joins our logical formulation of the knowledge commons with a generic model of the participatory-sensing problem. This requires a multi-agent platform for the simulation of autonomous and dynamic agents, and a method of executing the logical calculus in which our electronic institution is specified. To this end, firstly, we develop a general purpose, high performance platform for multi-agent based simulation, Presage2. Secondly, we propose a method for translating event-calculus axioms into rules compatible with business rule engines, and provide an implementation for JBoss Drools along with a suite of modules for electronic institutions. Through our simulations we show that, when building electronic institutions for managing participatory sensing as a knowledge commons, proper enfranchisement of agents (as outlined in Ostrom's work) is key to striking a balance between endurance, fairness and reduction of greedy behaviour. We conclude with a set of guidelines for engineering knowledge commons for the next generation of participatory-sensing applications.Open Acces