Abstract: Researchers that study Human-Like computing mainly aim to understand how systems can emulate human cognitive performance. However, when Human-Like systems are designed for sharing economy applications in which humans have to collaborate in order to achieve a desired task, there are several problems that needs to be addressed before asking how cognitive performance of a single human can be emulated. In this paper, we highlight these problems, provide examples related to the ridesharing scenario, and introduce how we approach these problems and which techniques we are using to tackle them.

Citation: S.Ceppi. The Dilemma of Human-Like Collective Systems. In S. Muggleton et al, editor, Twentieth Workshop on Machine Intelligence (MI 20), Windsor Park, UK, 23-25 October, 2016. In press.

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Abstract: In this paper, we introduce a privacy-enhanced Peer Manager, which is a fundamental building block for the implementation of a privacy-preserving collective adaptive systems computing platform. The Peer Manager is a user-centered identity management platform that keeps information owned by a user private and is built upon an attribute based privacy policy. Furthermore, this paper explores the ethical, privacy and social values aspects of collective adaptive systems and their extensive capacity to transform lives. We discuss the privacy, social and ethical issues around profiles and present their legal privacy requirements from the European legislation perspective. © IFIP International Federation for Information Processing 2015.

Citation: Mark Hartswood, Marina Jirotka, Ronald Chenu-Abente, Alethia Hume, Fausto Giunchiglia, Leonardo A. Martucci, Simone Fischer-Hübner. “Privacy for Peer Profiling in Collective Adaptive Systems.” Privacy and Identity Management for the Future Internet in the Age of Globalisation. Springer International Publishing, 2014. 237-252.

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Abstract: Hybrid and Diversity-Aware Collective Adaptive Systems(HDA-CAS) form a broad class of highly distributed systems comprising a number of heterogeneous human-based and machine-based computing(service) units. These units collaborate in ad-hoc formed, dynamically-adaptive collectives. The flexibility of these collectives makes them suit-able for processing elaborate tasks, but at the same time, building a system to support diverse communication types in such collectives is challenging. In this paper, we address the fundamental communication challenges for HDA-CAS. We present the design of a middleware for virtualizing communication within and among collectives of diverse types of service units. The middleware is able to handle numerous, intermittently available, human and software-based service units, and manages the notion of collectivity transparently to the programmer. A prototype implementation for validation purpose is also provided.

Citation: Philipp Zeppezauer, Ognjen Scekic, Hong-Linh Truong, and Schahram Dustdar. Virtualizing Communication for Hybrid and Diversity-Aware Collective Adaptive Systems. 10th International Workshop on Engineering Service-Oriented Applications (WESOA’14), 3 Nov, Paris, France, 2014.

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Abstract: We report work in progress on the role of models in the formation and maintenance of collectives in Hybrid Diversity-Aware Collective Adaptive Systems (HDA-CASs). HDA-CASs utilize hybrid computations involving machines and humans operating in collectives in a way that manages and leverages the diversity of collectives and machine-based computation. Here we explore the role of models in helping to constitute particular collectives and how models help shape the response of the collective. It appears that models are a potentially critical resource in collecting, sharing and acting on data gathered from the operation of CASs. This points to the potential role for models in the design of HDA-CASs. In particular we are interested in how models provide a sense of identity for a collective and can provide resources that shape the potential for collective action.

Citation: Stuart Anderson, Mark Hartswood and Marina Jirotka (2014). Reflection, collectives and adaptation: the role of models in the design of Collective Adaptive Systems. In the 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASOW 2014).

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Objective

Collective intelligence platforms, where large numbers of people contribute their skills, resources, and knowledge in a collaborative way, have emerged in recent years as a new type of socio-technical systems that use the creativity of their users to solve complex problems. However, digital infrastructures that support such collective intelligence are only successful and sustainable in the long term if they manage to adapt to the evolution of the ways in which they are used, and co-evolve meaningfully with their user base. The overall objective of this atelier is to build a prototypical Web-based collective intelligence system that has such adaptive capabilities. Participants will be trained in the fundamental theoretical and technological building blocks of such systems using the SmartSociety software platform, which contains many of the key components to engineering collective adaptive systems successfully.

Format

The atelier will involve daily lectures on each of the key technologies used in the SmartSociety platform, and the rest of the day group work implementation of an actual case study. The theoretical introductions will focus only very briefly on general material, and then go into aspects specific to collective adaptive systems for the most part.

Prerequisites

Participants are expected to have general mathematical literacy (background in mathematical notation, discrete mathematics, probability theory), and good programming skills. Experience with web programming (REST, Javascript, etc) is strongly recommended, as this will be extensively used in the practical part of the atelier.

Programme

Monday 5th September
15:30-17:00 Lecture: Introduction to Collective Adaptive Systems
Michael Rovatsos, University of Edinburgh

Tuesday 6th September
11:00-13:00 Lecture: Overview of SmartSociety Platform
Tommaso Schiavinotto, U-Hopper Srl
14:30-17:00 Lab: Experimentation with Orchestration Manager

Wednesday 7th September
11:00-13:00 Lecture: Introduction to semantic privacy technologies
Ronald Chenu-Abente, University of Trento
14:30-17:00 Lab: Experimentation with Peer Manager

Thursday 8th September
11:00-13:00 Lecture: Introduction to incentive mechanisms
Michael Rovatsos, University of Edinburgh
14:30-17:00 Lab: Experimentation with Incentives Manager

Friday 9th September
11:00-13:00 Lecture: Value-sensitive design
Mark Hartswood, University of Oxford
14:30-17:00 Lab: Completion of implementation
17:30-19:30 Final presentation

Abstract: Hybrid Diversity-aware Collective Adaptive Systems (HDA-CAS) is a new generation of socio-technical systems where both humans and machine peers complement each other and operate collectively to achieve their goals. These systems are characterized by the fundamental properties of hybridity and collectiveness, hiding from users the complexities associated with managing the collaboration and coordination of hybrid human/machine teams. In this paper we present the key programming elements of the SmartSociety HDA-CAS platform. We first describe the overall platform’s architecture and functionality and then present concrete programming model elements – Collective-based Tasks (CBTs) and Collectives, describe their properties and show how they meet the hybridity and collectiveness requirements. We also describe the associated Java language constructs, and show how concrete use-cases can be encoded with the introduced constructs.

Citation: O. Scekic, T. Schiavinotto, D. I. Diochnos, M. Rovatsos, H.-L. Truong, I. Carreras, S. Dustdar, Programming Model Elements for Hybrid Collaborative Adaptive Systems, 1st IEEE International Conference on Collaboration and Internet Computing (CIC’15), 27-30 October 2015, Hangzhou, China.

Citation: http://bit.ly/1p8SJOP

Abstract. Advances in human computation bring the feasibility of utilizing human capabilities as services. On the other hand, we have witnessed emerging collective adaptive systems which are formed from heterogeneous types of compute units to solve complex problems. The recently introduced Social Compute Units (SCUs) present one type of these systems, which have human-based services as their core fundamental compute units. While, there is related work on forming SCUs and optimizing their performance with adaptation techniques, most of it is focused on static structures of SCUs. To provide better runtime performance and exibility management for SCUs, we present an elasticity model for SCUs and mechanisms for their elastic management which allow for certain uctuations in size, structure, performance and quality. We model states of elastic SCUs, present APIs for managing SCUs as well as metrics for controlling their elasticity with which it is possible to tailor their performance parameters at runtime within the customer-set constraints. We illustrate our contribution with an example algorithm.

Keywords: social compute units, elasticity, adaptation, collective adaptive systems

doi: http://link.springer.com/chapter/10.1007%2F978-3-319-07881-6_25

Citation: Mirela Riveni, Hong-Linh Truong, Schahram Dustdar. On the Elasticity of Social Compute Units, Springer-Verlag, 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014), 16-20 June 2014, Thessaloniki, Greece. Accepted.

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Abstract. The physical and virtual dimensions of life are becoming more and more deeply interwoven. Society is merging with technology, giving rise to a global socio-technical ecosystem. In a society comprising people and machines as actors we often see people-to-people interactions mediated by machine and machine-to-machine interaction mediated by people. The speed and scale of this change and the differences in culture, language and interests make the problem of establishing effective means of communication and coordination increasingly challenging. Our vision, embodied in the SmartSociety project1, is that a new generation of systems, hybrid (i.e., including humans and artificial peers, as well as social groups), distributed, open and large-scale, is needed to tackle these issues. In such systems, multitudes of heterogeneous peers will produce and handle massive amounts of data; peers will join/leave the system following unpredictable patterns with no central coordination and will interoperate at different spatial and temporal scales. Aware of the ethical issues, and by identifying the right incentive schemes and privacy levels, these systems will assist individuals and collectives in their everyday activities, coping with the diversity of the world and working in the presence of incomplete and incorrect information.

http://eprints.biblio.unitn.it/4214/

Citation: F. Giunchiglia, V. Maltese, S. Anderson, D. Miorandi (2013). Towards Hybrid and Diversity-Aware Collective Adaptive Systems. First FOCAS Workshop on Fundamentals of Collective Systems @ECAL 2013.

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Abstract. Conventional incentive mechanisms were designed for business environments involving static business processes and a limited number of actors. They are not easily applicable to crowdsourcing and other social computing platforms, characterized by dynamic collaboration patterns and high numbers of actors, because the effects of incentives in these environments are often unforeseen and more costly than in a well-controlled environment of a traditional company.

In this paper we investigate how to design and calibrate incentive schemes for crowdsourcing processes by simulating joint effects of a combination of different participation and incentive mechanisms applied to a working crowd. More specifically, we present a simulation model of incentive schemes and evaluate it on a relevant real-world scenario. We show how the model is used to simulate different compositions of incentive mechanisms and model parameters, and how these choices influence the costs on the system provider side and the number of malicious workers.

Keywords: rewards, incentives, crowdsourcing, social computing, collective adaptive systems.

doi: http://dx.doi.org/10.1007/978-3-642-41030-7_11

Citation: Ognjen Scekic, Christoph Dorn, Schahram Dustdar, “Simulation-Based Modeling and Evaluation of Incentive Schemes in Crowdsourcing Environments”, 21st International Conference on Cooperative Information Systems (CoopIS’13), September 11-13, 2013, Graz, Austria.

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