Coordinated constraint relaxation using a distributed agent protocol

The interactions among agents in a multi-agent system for coordinating a distributed, problem solving task can be complex, as the distinct sub-problems of the individual agents are interdependent. A distributed protocol provides the necessary framework for specifying these interactions. In a model of interactions where the agents' social norms are expressed as the message passing behaviours associated with roles, the dependencies among agents can be specified as constraints. The constraints are associated with roles to be adopted by agents as dictated by the protocol. These constraints are commonly handled using a conventional constraint solving system that only allows two satisfactory states to be achieved - completely satisfied or failed. Agent interactions then become brittle as the occurrence of an over-constrained state can cause the interaction between agents to break prematurely, even though the interacting agents could, in principle, reach an agreement. Assuming that the agents are capable of relaxing their individual constraints to reach a common goal, the main issue addressed by this thesis is how the agents could communicate and coordinate the constraint relaxation process. The interaction mechanism for this is obtained by reinterpreting a technique borrowed from the constraint satisfaction field, deployed and computed at the protocol level.The foundations of this work are the Lightweight Coordination Calculus (LCC) and the distributed partial Constraint Satisfaction Problem (CSP). LCC is a distributed interaction protocol language, based on process calculus, for specifying and executing agents' social norms in a multi-agent system. Distributed partial CSP is an extension of partial CSP, a means for managing the relaxation of distributed, over-constrained, CSPs. The research presented in this thesis concerns how distributed partial CSP technique, used to address over-constrained problems in the constraint satisfaction field, could be adopted and integrated within the LCC to obtain a more flexible means for constraint handling during agent interactions. The approach is evaluated against a set of overconstrained Multi-agent Agreement Problems (MAPs) with different levels of hardness. Not only does this thesis explore a flexible and novel approach for handling constraints during the interactions of heterogeneous and autonomous agents participating in a problem solving task, but it is also grounded in a practical implementation

Artificial Intelligence, social changes and impact on the world of education

The way in which humans acquire and share knowledge has been under constant evolution throughout times. Since the appearance of the first computers, education has changed dramatically. Now, as disruptive technologies are in full development, new opportunities arise for taking education to levels that have never been seen before. Ever since the coronavirus pandemic, the use of online teaching modalities has become widespread all over the world and the situation has caused the development of robust digital learning solutions an urgent need. At present, primary, secondary, third-level teaching and all sorts of courses may be delivered online, either in real-time or recorded for later viewing. Classes can be complemented with videos, documents or even interactive exercises. However, the institutions that used little or no technology prior to Covid-19 have found this situation overwhelming. The lack of knowledge regarding the digital teaching/learning tools available on the market and/or lack of knowledge regarding their use, means that educational institutions will not be able to take full advantage of the opportunities offered; poor use of technology in online classrooms may hinder the students’ progress

From distributed coordination to field calculus and aggregate computing

open6siThis work has been partially supported by: EU Horizon 2020 project HyVar (www.hyvar-project .eu), GA No. 644298; ICT COST Action IC1402 ARVI (www.cost -arvi .eu); Ateneo/CSP D16D15000360005 project RunVar (runvar-project.di.unito.it).Aggregate computing is an emerging approach to the engineering of complex coordination for distributed systems, based on viewing system interactions in terms of information propagating through collectives of devices, rather than in terms of individual devices and their interaction with their peers and environment. The foundation of this approach is the distillation of a number of prior approaches, both formal and pragmatic, proposed under the umbrella of field-based coordination, and culminating into the field calculus, a universal functional programming model for the specification and composition of collective behaviours with equivalent local and aggregate semantics. This foundation has been elaborated into a layered approach to engineering coordination of complex distributed systems, building up to pragmatic applications through intermediate layers encompassing reusable libraries of program components. Furthermore, some of these components are formally shown to satisfy formal properties like self-stabilisation, which transfer to whole application services by functional composition. In this survey, we trace the development and antecedents of field calculus, review the field calculus itself and the current state of aggregate computing theory and practice, and discuss a roadmap of current research directions with implications for the development of a broad range of distributed systems.embargoed_20210910Viroli, Mirko; Beal, Jacob; Damiani, Ferruccio; Audrito, Giorgio; Casadei, Roberto; Pianini, DaniloViroli, Mirko; Beal, Jacob; Damiani, Ferruccio; Audrito, Giorgio; Casadei, Roberto; Pianini, Danil

Engineering Self-Adaptive Collective Processes for Cyber-Physical Ecosystems

The pervasiveness of computing and networking is creating significant opportunities for building valuable socio-technical systems. However, the scale, density, heterogeneity, interdependence, and QoS constraints of many target systems pose severe operational and engineering challenges. Beyond individual smart devices, cyber-physical collectives can provide services or solve complex problems by leveraging a “system effect” while coordinating and adapting to context or environment change. Understanding and building systems exhibiting collective intelligence and autonomic capabilities represent a prominent research goal, partly covered, e.g., by the field of collective adaptive systems. Therefore, drawing inspiration from and building on the long-time research activity on coordination, multi-agent systems, autonomic/self-* systems, spatial computing, and especially on the recent aggregate computing paradigm, this thesis investigates concepts, methods, and tools for the engineering of possibly large-scale, heterogeneous ensembles of situated components that should be able to operate, adapt and self-organise in a decentralised fashion. The primary contribution of this thesis consists of four main parts. First, we define and implement an aggregate programming language (ScaFi), internal to the mainstream Scala programming language, for describing collective adaptive behaviour, based on field calculi. Second, we conceive of a “dynamic collective computation” abstraction, also called aggregate process, formalised by an extension to the field calculus, and implemented in ScaFi. Third, we characterise and provide a proof-of-concept implementation of a middleware for aggregate computing that enables the development of aggregate systems according to multiple architectural styles. Fourth, we apply and evaluate aggregate computing techniques to edge computing scenarios, and characterise a design pattern, called Self-organising Coordination Regions (SCR), that supports adjustable, decentralised decision-making and activity in dynamic environments.Con lo sviluppo di informatica e intelligenza artificiale, la diffusione pervasiva di device computazionali e la crescente interconnessione tra elementi fisici e digitali, emergono innumerevoli opportunità per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessità—si pensi ai livelli, scale, eterogeneità, e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un “effetto sistema” che emerge dalla coordinazione e l'adattamento di componenti fra loro, l'ambiente e il contesto. Comprendere e costruire sistemi in grado di esibire intelligenza collettiva e capacità autonomiche è un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. Perciò, traendo ispirazione e partendo dall'attività di ricerca su coordinazione, sistemi multiagente e self-*, modelli di computazione spazio-temporali e, specialmente, sul recente paradigma di programmazione aggregata, questa tesi tratta concetti, metodi, e strumenti per l'ingegneria di ensemble di elementi situati eterogenei che devono essere in grado di lavorare, adattarsi, e auto-organizzarsi in modo decentralizzato. Il contributo di questa tesi consiste in quattro parti principali. In primo luogo, viene definito e implementato un linguaggio di programmazione aggregata (ScaFi), interno al linguaggio Scala, per descrivere comportamenti collettivi e adattativi secondo l'approccio dei campi computazionali. In secondo luogo, si propone e caratterizza l'astrazione di processo aggregato per rappresentare computazioni collettive dinamiche concorrenti, formalizzata come estensione al field calculus e implementata in ScaFi. Inoltre, si analizza e implementa un prototipo di middleware per sistemi aggregati, in grado di supportare più stili architetturali. Infine, si applicano e valutano tecniche di programmazione aggregata in scenari di edge computing, e si propone un pattern, Self-Organising Coordination Regions, per supportare, in modo decentralizzato, attività decisionali e di regolazione in ambienti dinamici

Viral systems : a new bio-inspired optimisation approach

The paper presents a new approach to deal with combinatorial problems. It makes use of a biological analogy inspired by the performance of viruses. The replication mechanism, as well as the hosts’ infection processes is used to generate a metaheuristic that allows the obtention of valuable results. The viral system (VS) theoretical context is described and it is applied to a library of medium-to-large-sized cases of the Steiner problem for which the optimal solution is known. The method is compared with the metaheuristics that have provided the best results for the Steiner problem. The VS provides better solutions than genetic algorithms and certain tabu search approaches. For the most sophisticated tabu search approaches (the best metaheuristic approximations to the Steiner problem solution) VS provides solutions of similar quality

Distributed Planning for Self-Organizing Production Systems

Für automatisierte Produktionsanlagen gibt es einen fundamentalen Tradeoff zwischen Effizienz und Flexibilität. In den meisten Fällen sind die Abläufe nicht nur durch den physischen Aufbau der Produktionsanlage, sondern auch durch die spezielle zugeschnittene Programmierung der Anlagensteuerung fest vorgegeben. Änderungen müssen aufwändig in einer Vielzahl von Systemen nachgezogen werden. Das macht die Herstellung kleiner Stückzahlen unrentabel. In dieser Dissertation wird ein Ansatz entwickelt, um eine automatische Anpassung des Verhaltens von Produktionsanlagen an wechselnde Aufträge und Rahmenbedingungen zu erreichen. Dabei kommt das Prinzip der Selbstorganisation durch verteilte Planung zum Einsatz. Die aufeinander aufbauenden Ergebnisse der Dissertation sind wie folgt: 1. Es wird ein Modell von Produktionsanlagen entwickelt, dass nahtlos von der detaillierten Betrachtung physikalischer Produktionsprozesse bis hin zu Lieferbeziehungen zwischen Unternehmen skaliert. Im Vergleich zu existierenden Modellen von Produktionsanlagen werden weniger limitierende Annahmen gestellt. In diesem Sinne ist der Modellierungsansatz ein Kandidat für eine häufig geforderte "Theorie der Produktion". 2. Für die so modellierten Szenarien wird ein Algorithmus zur Optimierung der nebenläufigen Abläufe entwickelt. Der Algorithmus verbindet Techniken für die kombinatorische und die kontinuierliche Optimierung: Je nach Detailgrad und Ausgestaltung des modellierten Szenarios kann der identische Algorithmus kombinatorische Fertigungsfeinplanung (Scheduling) vornehmen, weltweite Lieferbeziehungen unter Einbezug von Unsicherheiten und Risiko optimieren und physikalische Prozesse prädiktiv regeln. Dafür werden Techniken der Monte-Carlo Baumsuche (die auch bei Deepminds Alpha Go zum Einsatz kommen) weiterentwickelt. Durch Ausnutzung zusätzlicher Struktur in den Modellen skaliert der Ansatz auch auf große Szenarien. 3. Der Planungsalgorithmus wird auf die verteilte Optimierung durch unabhängige Agenten übertragen. Dafür wird die sogenannte "Nutzen-Propagation" als Koordinations-Mechanismus entwickelt. Diese ist von der Belief-Propagation zur Inferenz in Probabilistischen Graphischen Modellen inspiriert. Jeder teilnehmende Agent hat einen lokalen Handlungsraum, in dem er den Systemzustand beobachten und handelnd eingreifen kann. Die Agenten sind an der Maximierung der Gesamtwohlfahrt über alle Agenten hinweg interessiert. Die dafür notwendige Kooperation entsteht über den Austausch von Nachrichten zwischen benachbarten Agenten. Die Nachrichten beschreiben den erwarteten Nutzen für ein angenommenes Verhalten im Handlungsraum beider Agenten. 4. Es wird eine Beschreibung der wiederverwendbaren Fähigkeiten von Maschinen und Anlagen auf Basis formaler Beschreibungslogiken entwickelt. Ausgehend von den beschriebenen Fähigkeiten, sowie der vorliegenden Aufträge mit ihren notwendigen Produktionsschritten, werden ausführbare Aktionen abgeleitet. Die ausführbaren Aktionen, mit wohldefinierten Vorbedingungen und Effekten, kapseln benötigte Parametrierungen, programmierte Abläufe und die Synchronisation von Maschinen zur Laufzeit. Die Ergebnisse zusammenfassend werden Grundlagen für flexible automatisierte Produktionssysteme geschaffen -- in einer Werkshalle, aber auch über Standorte und Organisationen verteilt -- welche die ihnen innewohnenden Freiheitsgrade durch Planung zur Laufzeit und agentenbasierte Koordination gezielt einsetzen können. Der Bezug zur Praxis wird durch Anwendungsbeispiele hergestellt. Die Machbarkeit des Ansatzes wurde mit realen Maschinen im Rahmen des EU-Projekts SkillPro und in einer Simulationsumgebung mit weiteren Szenarien demonstriert

Peak reduction in decentralised electricity systems : markets and prices for flexible planning

In contemporary societies, industrial processes as well as domestic activities rely to a large degree on a well-functioning electricity system. This reliance exists both structurally (the system should always be available) and economically (the prices for electricity affect the costs of operating a business and the costs of living). After many decades of stability in engineering principles and related economic paradigms, new developments require us to reconsider how electricity is distributed and paid for.Twowell-known examples of important technological developments in this regard are decentralised renewable energy generation (e.g. solar and wind power) and electric vehicles. They promise to be highly useful, for instance because they allow us to decrease our CO2 emissions and our dependence on energy imports. However, a widespread introduction of these (and related) technologies requires significant engineering efforts. In particular, two challenges to themanagement of electricity systems are of interest to the scope of this dissertation. First, the usage of these technologies has significant effects on howwell (part of) supply and demand can be planned ahead of time and balanced in real time. Planning and balancing are important activities in electricity distribution for keeping the number of peaks low (peaks can damage network hardware and lead to high prices). It can become more difficult to plan and balance in future electricity systems, because supply will partly depend on intermittent sunshine and wind patterns, and demand will partly depend on dynamic mobility patterns of electric vehicle drivers. Second, these technologies are often placed in the lower voltage (LV) tiers of the grid in a decentralised manner, as opposed to conventional energy sources, which are located in higher voltage (HV) tiers in central positions. This is introducing bi-directional power flows on the grid, and it significantly increases the number of actors in the electricity systems whose day-to-day decisionmaking about consumption and generation (e.g. electric vehicles supplying electricity back to the network) has significant impacts on the electricity system.In this dissertation, we look into dynamic pricing and markets in order to achieve allocations (of electricity and money) which are acceptable in future electricity systems. Dynamic pricing and markets are concepts that are highly useful to enable efficient allocations of goods between producers and consumers. Currently, they are being used to allocate electricity between wholesale traders. In recent years, the roles of the wholesale producer and the retailer have been unbundled in many countries of the world, which is often referred to as “market liberalisation”. This is supposed to increase competition and give end consumers more choice in contracts. Market liberalisation creates opportunities to design markets and dynamic pricing approaches that can tackle the aforementioned challenges in future electricity systems. However, they also introduce new challenges themselves, such as the acceptance of price fluctuations by consumers.The research objective of this dissertation is to develop market mechanisms and dynamic pricing strategies which can deal with the challenges mentioned above and achieve acceptable outcomes. To this end, we formulate three major research questions:First, can we design pricing mechanisms for electricity systems that support two necessary featureswell, which are not complementary—namely to encourage adaptations in electricity consumption and generation on short notice (by participants who have this flexibility), but also to enable planning ahead of electricity consumption and generation (for participants who can make use of planning)?Second, the smart grid vision (among others) posits that in future electricity systems, outcomeswill be jointly determined by a large number of (possibly) small actors and allocations will be mademore frequently than today. Which pricing mechanisms do not require high computational capabilities from the participants, limit the exposure of small participants to risk and are able to find allocations fast?Third, automated grid protection against peaks is a crucial innovation step for network operators, but a costly infrastructure program. Is it possible for smart devices to combine the objective of protecting network assets (e.g. cables) from overloading with applying buying and selling strategies in a dynamic pricing environment, such that the devices can earn back parts of their own costs?In order to answer the research questions, our methods are as follows: We consider four problems which are likely to occur in future electricity systems and are of relevance to our research objective. For each problem, we develop an agent-based model and propose a novel solution. Then, we evaluate our proposed solution using stochastic computational simulations in parameterised scenarios. We thus make the following four contributions:In Chapter 3,we design a market mechanism in which both binding commitments and optional reserve capacity are explicitly represented in the bid format, which can facilitate price finding and planning in future electricity systems (and therefore gives answers to our first research question). We also show that in this mechanism, flexible consumers are incentivised to offer reserve capacity ahead of time, whichwe prove for the case of perfect competition and showin simulations for the case of imperfect competition. We are able to show in a broad range of scenarios that our proposed mechanism has no economic drawbacks for participants. Furthermore (giving answers to our second research question), the mechanism requires less computational capabilities in order to participate in it than a contemporary wholesale electricitymarket with comparable features for planning ahead.In Chapter 4, we consider the complexity of dynamic pricing strategies that retailers could use in future electricity systems (this gives answers to our first, but foremost to our second research question). We argue that two important features of pricing strategies are not complementary—namely power peak reduction and comprehensibility of prices—and we propose indicators for the comprehensibility of a pricing strategy from the perspective of consumers. We thereby add a novel perspective for the design and evaluation of pricing strategies.In Chapter 5, we consider dynamic pricing mechanisms where the price is set by a single seller. In particular, we develop pricing strategies for a seller (a retailer) who commits to respect an upper limit on its unit prices (this gives answers to both our first and second research question). Upper price limits reduce exposure of market participants to price fluctuations. We show that employing the proposed dynamic pricing strategies reduces consumption peaks, although their parameters are being simultaneously optimised for themaximisation of retailer profits.In Chapter 6, we develop control algorithms for a small storage device which is connected to a low voltage cable. These algorithms can be used to reach decisions about when to charge and when to discharge the storage device, in order to protect the cable from overloading as well as to maximise revenue from buying and selling (this gives answers to our third research question). We are able to show in computational simulations that our proposed strategies perform well when compared to an approximated theoretical lower cost bound. We also demonstrate the positive effects of one of our proposed strategies in a laboratory setupwith real-world cable hardware.The results obtained in this dissertation advance the state of the art in designing pricing mechanisms and strategies which are useful for many use cases in future decentralised electricity systems. The contributions made can provide two positive effects: First, they are able to avoid or reduce unwanted extreme situations, often related to consumption or production peaks. Second, they are suitable for small actors who do not have much computation power but still need to participate in future electricity systems where fast decision making is needed