*** Daily Agenda is now out ***

Organisers

Michael Rovatsos, The University of Edinburgh, mrovatso@inf.ed.ac.uk
Ronald Chenu-Abente, University of Trento, chenu@disi.unitn.it

Background

Diversity is pervasive in human nature and culture, and is deeply rooted in the variation of natural traits and experience among individuals, the collectives they form, and the environments they inhabit. When humans reason individually, they maintain different representations, conceptualisations, and theories, and apply different rules of inference, learning, and decision making. When they interact with each other to combine their skills or resources, to coordinate their activities, and to resolve conflicts between their individual objectives, they exchange information and knowledge, negotiate and align their individual views, and adapt to each other’s behaviour dynamically. Arguably, diversity is not only a phenomenon that humans have to deal with, but it is also the vehicle for achieving some of the most impressive products of human intelligence.

Artificial Intelligence, on the other hand, has so far largely relied on a certain degree of homogeneity, not necessarily in terms of the components involved in a method or system, but in terms of the process that combines them. While various areas within AI have already developed methods that can cope with and/or exploit diversity to some extent, for example

• electronic markets where individual agents have different goals and aim to maximise their own profit,
• hybrid robot architectures that involve different layers of representation and reasoning,
• knowledge sharing infrastructures where different agents use different domain ontologies, and
• machine learning systems that combine different sources of data and/or learning units,

more often than not, these systems still involve a “monolithic”, global approach to integration. This usually derives from a global task context, a common intermediate representation layer, or a global output to be produced by the integrated system.

We believe that there is a huge potential in bringing the insights from work on problems that involve diversity – like those listed in the examples above – together to gain a deeper understanding of the phenomenon of diversity, as well as to develop principled methodological approaches that will enable us to better utilise diversity in future AI systems.

Workshop Description

The workshop seeks to explore diversity as a phenomenon that both poses a challenge for AI in terms of dealing with and managing diversity in an intelligent system (or ecosystem of intelligent human and/or artificial agents) and presents an opportunity in terms of leveraging diversity (for example through processes like crowdsourcing and collaborative knowledge production) to achieve human-like (and human-friendly) capabilities in more open-ended, incrementally evolving, and interactive AI systems.

We aim to bring together researchers from different communities that have each addressed diversity in different ways, such as

• hierarchical and hybrid inference systems (combining representation and reasoning mechanisms),
• semantic web and ontologies (interoperability of information sources, ontology alignment),
• non-monotonic and defeasible reasoning (reasoning about conflicting and changing information),
• mechanism design and social choice (reaching agreement in the presence of conflict of interest),
• language evolution and emergent semantics (evolving shared symbol and concept spaces),
• cross-lingual approaches to natural language understanding (integrating different natural languages),
• teamwork and collaborative multiagent systems (integrating heterogeneous knowledge/behaviours),
• human-AI/human-robot collaboration (aligning agents’ views and objectives with those of humans),
• crowdsourcing and human computation (managing diverse contributions of large human collectives).

The workshop will provide an open forum for researchers from these (and other) areas to contribute their insights on diversity in order to develop a shared agenda for the future study of diversity in AI. We welcome submissions on all aspects of diversity, ranging from theoretical foundations to practical applications, case studies, and surveys. The workshop will be heavily discussion-based, with relatively short paper presentations and a focus on formulating key research questions and a longer-term research agenda for the area. To enable high-quality discussion and debate, a key evaluation criterion will be the focus of papers contributed to the workshop on the diversity “angle“ of the research reported. Specifically, papers should clearly identify

• what type of diversity or aspects of diversity the reported research investigates or accommodates,
• the methods the paper proposes to deal with and/or exploit diversity,
• how the proposed method combines and/or exceeds existing diversity-oriented capabilities, and
• what key challenges in terms of diversity it leaves open for future research.

Beyond this key requirement, we deliberately impose no restrictions on methodological approach, or maturity of the research. In particular, the workshop aims to be inclusive with regard to the types of diversity considered, including (but not limited to) diversity of representations, algorithms, systems infrastructures, datasets, agent behaviours, skills and capabilities, preferences and objectives, but also users, user populations, cultures, contexts of use, application domains, user interfaces, etc.

Also, in keeping with the Special Topic of ECAI 2016 Artificial Intelligence for Human Values, we particularly invite papers that address the ethics and social impact of AI applications related to diversity, for example addressing issues related to the social dynamics of diversity in systems comprising of humans and artificial agents, the emergence of “digital divides“ and the implications of diversity on the cohesiveness of these systems, diversity-aware accountability and privacy methods, or the potential risks and benefits of diversity-aware AI in terms of promoting human diversity in various domains.

Paper submission

We invite full (8-12 pages) and short (4-6 pages) papers for presentation at the workshop, to be submitted through the workshop’s Easychair web site using the ECAI format (which can be downloaded together with instructions from this page). Each paper will be peer-reviewed by at least two Programme Committee members, and authors will be expected to produce final versions of their papers in good time before the workshop.

All accepted papers will be made available online prior to the workshop, and distributed to all participants in hardcopy. If a sufficiently high number of high-quality papers is received, we will aim to produce a special issue in a high-quality journal where revised versions of the papers will be published alongside invited papers.

Important dates

The following is a (tentative) timeline of key dates:

• Paper submission deadline – 14th June 2016 extended to 17th June 2016
• Author notification – 28th June 2016 30th June 2016
• Camera-ready versions – 15th July 2016
• Workshop – 29th or 30th August 2016

Agenda

The following is the workshop agenda for the 29th of August:

09:15-09:30 Welcome
09:30-10:00 Towards Building Ontologies with the Wisdom of the Crowd. Paula Chocron, Dagmar Gromann and Francisco José Quesada Real
10:00-10:30 A Methodology to Take Account of Diversity in Collective Adaptive Systems. Heather S. Packer and Luc Moreau
10:30-11:00 Coffee break
11:00-11:30 Diversity-Aware Recommendation for Human Collectives. Pavlos Andreadis, Sofia Ceppi, Michael Rovatsos and Subramanian Ramamoorthy
11:30-12:00 Industry talk: Democracy by Design. Marcel van Hest
12:00-13:00 Invited talk by Antonella de Angeli
13:00-14:00 Lunch
14:00-14:20 A Semantic Distance based Architecture for a Guesser Agent in ESSENCE’s Location Taboo Challenge. Kemo Adrian, Aysenur Bilgin and Paul Van Eecke
14:20-14:40 Interdisciplinarity as an Indicator of Diversity in a Corpus of Artificial Intelligence Research Articles. Bilge Say
14:40-15:00 Managing human diversity in diverse multi-agent collaborative intelligence systems. Mark Hartswood, Kevin Page, Avi Segal, Kobi Gal and Marina Jirotka
15:00-15:20 Analysing communicative diversity via the Stag Hunt. Robert van Rooij and Katrin Schulz
15:20-15:40 Domain-Based Sense Disambiguation in Multilingual Structured Data. Gabor Bella, Alessio Zamboni and Fausto Giunchiglia
15:40-16:10 Coffee break
16:15-17:15 Panel discussion
17:15-17:30 Wrap-up

Financial Support

The workshop is sponsored by the ESSENCE (www.essence-network.com) and SmartSociety (www.smart-society-project.eu) projects, which will provide extensive financial support to participants, in particular PhD students and junior researchers who wish to participate. To be eligible for such support, interested individuals should submit a short or full paper, and email Michael Rovatsos (mrovatso@inf.ed.ac.uk) with a one-page case for support, providing a short bio, describing their interest in the workshop, and specifying the requested amount together with a justification of the anticipated expenses.

Committees

Workshop Organisers

Michael Rovatsos, The University of Edinburgh, mrovatso@inf.ed.ac.uk
Ronald Chenu-Abente, University of Trento, chenu@disi.unitn.it

Steering Committee

Alan Bundy, University of Edinburgh, United Kingdom
Peter Gardenfors, University of Lund, Sweden
Fausto Giunchiglia, University of Trento, Italy
Asuncion Gomez Perez, Universidad Politecnica de Madrid, Spain
Ben Kuipers, University of Michigan, USA
Ariel Procaccia, Carnegie-Mellon University, USA
Carles Sierra, IIIA-CSIC Barcelona, Spain
Luc Steels, Vrije Universiteit Brussels, Belgium
Michael Wooldridge, University of Oxford, United Kingdom
Gerhard Weiss, University of Maastricht, The Netherlands

Programme Committee

Yoram Bachrach, Microsoft Research Cambridge, United Kingdom
Gabor Bella, University of Trento, Italy
Sofia Ceppi, University of Edinburgh, United Kingdom
Jerome Euzenat, INRIA Grenoble, France
Kobi Gal, Ben-Gurion University of the Negev, Israel
Fabien Gandon, INRIA Sophia-Antipolis, France
Mark Hartswood, University of Oxford, United Kingdom
Nick Hawes, University of Birmingham, United Kingdom
Catholijn Jonker, Technical University of Delft, The Netherlands
Ian Kash, Microsoft Research Cambridge, United Kingdom
Oliver Lemon, Heriot-Watt University Edinburgh, United Kingdom
Nicolas Maudet, Universite Pierre et Marie Curie Paris, France
Fiona McNeill, Heriot-Watt University Edinburgh, United Kingdom
Roberto Navigli, University of Rome “La Sapienza”, Italy
Luc Moreau, University of Southampton, United Kingdom
Iyad Rahwan, MIT, USA
Subramanian Ramamoorthy, University of Edinburgh, United Kingdom
Katharina Reinecke, University of Washington, USA
Robert van Rooij, ILLC University of Amsterdam, The Netherlands
Carlos Ruiz, TAIGER S.A., Spain
Marco Schorlemmer, IIIA-CSIC Barcelona, Spain
Onn Shehory, IBM Haifa Labs, Israel
Pavel Shvaiko, Informatica Trentina, Italy
Remi van Trijp, Sony Computer Science Labs Paris, France

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: Society is moving towards a socio-technical ecosystem in which physical and virtual dimensions of life are intertwined and where people interactions ever more take place with or are mediated by machines. Hybrid Diversity-aware Collective Adaptive Systems (HDA-CAS) is a new generation of sociotechnical systems where humans and machines synergetically complement each other and operate collectively to achieve their goals. HDA-CAS introduce the fundamental properties of hybridity and collectiveness, hiding from the users the complexities associated with managing the collaboration and coordination of machine and human computing elements. In this paper we present an HDA-CAS system called SmartSociety, supporting computations with hybrid human/machine collectives. We describe the platform’s architecture and functionality, validate it on two real-world scenarios involving human and machine elements and present a performance evaluation.

Citation: O. Scekic, D. Miorandi, T. Schiavinotto, D. I. Diochnos, A. Hume, R. Chenu-Abente, H.-L. Truong, M. Rovatsos, I. Carreras, S. Dustdar, F. Giunchiglia, SmartSociety — A Platform for Collaborative People-Machine Computation, The 8th IEEE International Conference on Service Oriented Computing & Applications (SOCA’15), 19-21 October 2015, Rome, Italy.

Download: http://bit.ly/1Wz4eN5

Dr. Rovatsos, begins by introducing us to his research, especially in what relates to systems with interacting intelligent agents, and technologies that support humans in designing such systems. He then proceeds to detail how this relates to the work done in SmartSociety, focussing mainly on the human aspect of this. Continuing, he explains how efficient task recommendation enables greater uptake and support of users. To accomplish this, we require systems that can adapt to the users’ needs and successfully integrate their individual contributions. These points are illustrated through a number of useful examples, leading to the concept of collectives. Finally, he discusses what SmartSociety means to him and the impact he expects the project will have.

You can watch the complete 10 minute interview below, or directly on Youtube, here.

Filmed at TEDxUniversityofEdinburgh at The Pleasance, Edinburgh on 21st February 2014.

Michael Rovatsos is a Senior Lecturer at the School of Informatics of the University of Edinburgh where he leads the Agents Group at the Centre for Intelligent Systems and their Applications. His past research has been in multi-agent systems. Part of the SmartSociety project, his research now involves social computation systems where humans and artificial agents work together to solve hard societal problems.

Executive Summary. This report first situates and outlines the potential of social computation to provide the basis for Social Collective Intelligence (SCI) in future systems. This involves the close interaction of social groups and machines together with systems of incentives and social structures to perform tasks that would otherwise be difficult to achieve either using entirely human or entirely machine solutions. The deliverable considers the challenges both from a technical and from a social science standpoint, identifying the potential for aligning them in order to provide an interdisciplinary perspective on the development of SCI systems. The paper then describes some of the challenges in developing an engineering approach to the development of such systems. Finally the paper outlines some of the “big questions” that arise from the framework for SCI research developed in the white paper.

Citation: Robertson, D., Anderson, S., Carreras, I., Miorandi, D., “D2.1 White Paper on Research Challenges in Social Collective Intelligence WP2 – Research Challenges and Strategic Analysis”, Social-IST (2013).

Download: http://bit.ly/1qzcWOx

Executive Summary. This document includes the outcomes of the activities carried out by the Social-IST Consortium on the identification and analysis of the application areas for which R&D&I initiatives on Social Collective Intelligence (SCI) can have a major impact. This serves as the basis for defining a set of recommendations and a possible roadmap to be taken into consideration when drafting future FET initiatives in the field of Social Collective Intelligence.

In a preliminary phase the Consortium identified, through desktop search, six relevant application areas for SCI, namely the Future of Work, the Future of Learning, Mobility and Transport in Cities of the Future, Healthcare and Well Being, Smart Energy and the Future of Science and Innovation. These areas have been analysed and discussed in details, in particular by means of (i) the two workshops held with the Social-IST Scientific Panel experts (ii) a Web survey open to the research community at large (iii) the final project event held in Oct. 2013. For each area, a number of scenarios were elaborated, leading to the identification of impacts on science, technology and society and of emerging research challenges.

The results of this analysis have been used for defining a roadmap for future EU initiatives in the field of SCI. This included (i) a proposal in terms of research methodology for running SCI projects and initiatives, (ii) a taxonomy of the most relevant research communities (iii) a mapping to the Horizon2020 Work programme and related calls.

This document is expected to provide some key insights on how to potentially exploit a Social Collective Intelligence approach in future calls and EU initiatives.

Citation: Carreras, I., Anderson, S., Robertson, D., Miorandi, D., “D3.1 Roadmap for FET Initiatives in Social Collective Intelligence, WP3 – High Impact Application Areas and Roadmapping”, Social-IST (2013).

Download: http://bit.ly/1p8Tmb1