Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Ontological analysis of means-end links

The i* community has raised several main dialects and dozens of variations in the definition of the i* language. Differences may be found related not just to the representation of new concepts but to the very core of the i* language. In previous work we have tackled this issue mainly from a syntactic point of view, using metamodels and syntactic-based model interoperability frameworks. In this paper, we go one step beyond and consider the use of foundational ontologies in general, and UFO in particular, as a way to clarify the meaning of core i* constructs and as the basis to propose a normative definition. We focus here on one of the most characteristics i* constructs, namely means-end links.Postprint (published version

Evaluation of a Legally Binding Smart-Contract Language for Blockchain Applications

Blockchain governs inter-organizational business processes and enables decentralized autonomous organizations (DAO) with governance capabilities via smart contracts (SC). Due to the programmer’s lack of prior knowledge of the contract domain, SCs are ambiguous and error-prone. Several works, i.e., SPESC, Symboleo, and SmaCoNat, exist to support the legally-binding SCs. The aforementioned SCLs present intriguing approaches to building legally-binding SCs but either lack domain completeness, or are intended for non-collaborative business processes. In our previous work, we address the above-mentioned shortcomings of the XML-based smart-legal-contract markup language (SLCML), in which blockchain developers focus on the contractual workflow rather than the syntax specifics. However, SLCML, as a blockchain-independent formal specification language, is not evaluated to determine its applicability, usefulness, and usability for establishing legally-binding SCs for workflow enactment services (WES) to automate and streamline the business processes within connected organizations. In accordance with this, we formally implement the SLCML and propose evaluation approaches, such as running case and lab experiments, to demonstrate the SLCML’s generality and applicability for developing legally-binding SCs. Overall, the results of this work ascertain the applicability, usefulness, and usability of the proposed SLCML for establishing legally-binding SCs for WES

Maps of Lessons Learnt in Requirements Engineering

Both researchers and practitioners have emphasized the importance of learning from past experiences and its consequential impact on project time, cost, and quality. However, from the survey we conducted of requirements engineering (RE) practitioners, over 70\% of the respondents stated that they seldom use RE lessons in the RE process, though 85\% of these would use such lessons if readily available. Our observation, however, is that RE lessons are scattered, mainly implicitly, in the literature and practice, which obviously, does not help the situation. We, therefore, present ``maps” of RE lessons which would highlight weak (dark) and strong (bright) areas of RE (and hence RE theories). Such maps would thus be: (a) a driver for research to ``light up” the darker areas of RE and (b) a guide for practice to benefit from the brighter areas. To achieve this goal, we populated the maps with over 200 RE lessons elicited from literature and practice using a systematic literature review and survey. The results show that approximately 80\% of the elicited lessons are implicit and that approximately 70\% of the lessons deal with the elicitation, analysis, and specification RE phases only. The RE Lesson Maps, elicited lessons, and the results from populating the maps provide novel scientific groundings for lessons learnt in RE as this topic has not yet been systematically studied in the field

Exploration of biological neural wiring using self-organizing agents

Cette thèse présente un nouveau modèle computationnel capable de détecter les configurations temporelles d'une voie neuronale donnée afin d'en construire sa copie artificielle. Cette construction représente un véritable défi puisqu'il est impossible de faire des mesures directes sur des neurones individuels dans le système nerveux central humain et que la voie neuronale sous-jacente doit être considérée comme une boîte noire. La théorie des Systèmes Multi-Agents Adaptatifs (AMAS) est utilisée pour relever ce défi. Dans ces systèmes auto-organisateurs, un grand nombre d'agents logiciels coopératifs interagissent localement pour donner naissance à un comportement collectif ascendant. Le résultat est un modèle émergent dans lequel chaque entité logicielle représente un neurone " intègre-et-tire ". Ce modèle est appliqué aux réponses réflexes d'unités motrices isolées obtenues sur des sujets humains conscients. Les résultats expérimentaux, comparés à des données obtenues expérimentalement, montrent que le modèle découvre la fonctionnalité de voies neuronales humaines. Ce qui rend le modèle prometteur est le fait que c'est, à notre connaissance, le premier modèle réaliste capable d'auto-construire un réseau neuronal artificiel en combinant efficacement les neurosciences et des systèmes multi-agents adaptatifs. Bien qu'aucune preuve n'existe encore sur la correspondance exacte entre connectivité du modèle et connectivité du système humain, tout laisse à penser que ce modèle peut aider les neuroscientifiques à améliorer leur compréhension des réseaux neuronaux humains et qu'il peut être utilisé pour établir des hypothèses afin de conduire de futures expérimentations.In this thesis, a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication is presented. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, the Adaptive Multi-Agent Systems (AMAS) theory in which large sets of cooperative software agents interacting locally give rise to collective behavior bottom-up is used. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model uncovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with self-adaptive multi-agent systems. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments

A framework for engineering reusable self-adaptive systems

The increasing complexity and size of information systems result in an increasing effort for maintenance. Additionally, miniaturization of devices leads to mobility and the need for context-adaptation. Self-adaptive Systems (SASs) can adapt to changes in their environment or the system itself. So far, however, development of SASs is frequently tailored towards the requirements of use cases. The research for reusable elements — for implementation as well as design processes — is often neglected. Integrating reusable processes and implementation artifacts into a framework and offering a tool suite to developers would make development of SASs faster and less error-prone. This thesis presents the Framework for Engineering Self-adaptive Systems (FESAS). It offers a reusable implementation of a reference system, tools for implementation and design as well as a middleware for controlling system deployment. As a second contribution, this thesis introduces a new approach for self-improvement of SASs which complements the SAS with meta-adaptation

Empirical Evaluation of Tropos4AS Modelling

Abstract. Our work addresses the challenges arising in the development of self-adaptive software, which has to work autonomously in an unpredictable environment, fulfilling the objectives of its stakeholders, while avoiding failure. In this context we developed the Tropos4AS framework, which extends the AOSE methodology Tropos to capture and detail at design time the specific decision criteria needed for a system to guide self-adaptation at run-time, and to preserve the concepts of agent and goal model explicitly along the whole development process until run-time. In this paper, we present the design of an empirical study for the evaluation of Tropos4AS, with the aim of assessing the modeling effort, expressiveness and comprehensibility of Tropos4AS models. This experiment design can be reused for the evaluation of other modeling languages extensions

Engineering requirements for adaptive systems

none4noThe increasing demand for complex and distributed software calls for novel software engineering methods and techniques, to create systems able to autonomously adapt to dynamically changing situations. In this paper, we present a framework for engineering requirements for adaptive software systems. The approach, called Tropos4AS, combines goal-oriented concepts and highvariability design methods. The Tropos4AS requirements model can be directly mapped to software prototypes with an agent-oriented architecture which can be executed for requirements validation and refinement. We give a comprehensive description of the framework, with conceptual models, modelling guidelines, and supporting tools. The applicability of the framework to requirements validation and refinement is illustrated through a case study. Two controlled experiments with subjects provide an empirical evaluation of the proposed modelling language, with statistical evidence of the effectiveness of the modelling approach for gathering requirements of adaptive systems.noneMorandini Mirko, Penserini Loris, Perini Anna, Marchetto AlessandroMorandini, Mirko; Penserini, Loris; Perini, Anna; Marchetto, Alessandr