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Abstract: Sharing economy applications need to coordinate humans, each of whom may have different preferences over the provided service. Traditional approaches model this as a resource allocation problem and solve it by identifying matches between users and resources. These require knowledge of user preferences and, crucially, assume that they act deterministically or, equivalently, that each of them is expected to accept the proposed match. This assumption is unrealistic for applications like ridesharing and house sharing (like airbnb), where user coordination requires handling of the diversity and uncertainty in human behaviour.
We address this shortcoming by proposing a diversity-aware recommender system that leaves the decision-power to users but still assists them in coordinating their activities. We achieve this through taxation, which indirectly modifies users’ preferences over options by imposing a penalty on them. This is applied on options that, if selected, are expected to lead to less favourable outcomes, from the perspective of the collective. The framework we used to identify the options to recommend is composed by three optimisation steps, each of which has a mixed integer linear program at its core. Using a combination of these three programs, we are also able to compute solutions that permit a good trade-off between satisfying the global goals of the collective and the individual users’ interests. We demonstrate the effectiveness of our approach with two experiments in a simulated ridesharing scenario, showing: (a) significantly better coordination results with the approach we propose, than with a set of recommendations in which taxation is not applied and each solution maximises the goal of the collective, (b) that we can propose a recommendation set to users instead of imposing them a single allocation at no loss to the collective, and (c) that our system allows for an adaptive trade-off between conflicting criteria.

Citation: P. Andreadis, S. Ceppi, M. Rovatsos, and S. Ramamoorthy. Diversity-Aware Recommendation for Human Collectives. In Proceedings of the 1st International Workshop on Diversity-Aware Artificial Intelligence (DIVERSITY 2016), The Hague, The Netherlands, 2016

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You can find the workshop proceeedings on the ECAI website, or directly download them from here.

To get a glimpse of what the day was like, you can follow #diversityAwareAI on Twitter.

Michael Rovatsos opens DIVERSITY 2016 with a quote from Marvin Minsky.

Marvin Minsky: “What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle.”

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

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.

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. For more details, please see the original announcement, here.

*** 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