Abstract: While AI has recently produced impressive systems that achieve human-like performance at challenging tasks, these systems tell us very little about how human intelligence works. In particular, they do not address the problem of composing knowledge and behaviour incrementally – a phenomenon that is pervasive in individual and collective human intelligence. We argue that achieving more human-like AI requires focusing on diversity in reasoning and behaviour among humans and artificial agents, and that developing systems capable of dealing with such diversity is key to achieving more human-like AI. In these systems intelligence should not only be measured in terms of how a system performs at a certain task, but also in terms of the properties of the process by which each component combines its knowledge and behaviour with that of others, just like humans do.

Citation: M. Rovatsos. Diversity-Awareness – The Key to Human-Like Computing? In S. Muggleton et al, editor, Twentieth Workshop on Machine Intelligence (MI 20), Windsor Park, UK, 23-25 October, 2016. In press.

Download: http://bit.ly/2jd0oMI

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

Download: http://bit.ly/2jp8rUr

Abstract: Web-based collaborative systems, where most computation is performed by human collectives, have distinctly different requirements from traditional workflow orchestration systems, as humans have to be mobilised to perform computations and the system has to adapt to their collective behaviour at runtime. In this paper, we present a social orchestration system called SmartOrch, which has been designed specifically for collective adaptive systems in which human participation is at the core of the overall distributed computation. SmartOrch provides a flexible and customisable workflow composition framework that has multi-level optimisation capabilities. These features allow us to manage the uncertainty that collective adaptive systems need to deal with in a principled way.
We demonstrate the benefits of SmartOrch with simulation experiments in a ridesharing domain. Our experiments show that SmartOrch is able to respond flexibly to variation
in collective human behaviour, and to adapt to observed behaviour at different levels. This is accomplished by learning how to propose and route human-based tasks, how to allocate computational resources when managing these tasks, and how to adapt the overall interaction model of the platform based on past performance. By proposing novel, solid engineering principles for these kinds of systems, SmartOrch addresses shortcomings of previous work that mostly focused on application-specific, non-adaptive solutions.

Citation: M. Rovatsos, D. Diochnos, Z. Wen, S. Ceppi, and P. Andreadis. SmartOrch: An Adaptive Orchestration System for Human-Machine Collectives. In Proceedings of the Special Track on Collective Adaptive Systems of the 32nd ACM Symposium on Applied Computing (SAC2017), Marrakech, Morocco, 2017. In Press

Download: http://bit.ly/2jBDYlu

Abstract: Large-scale IoT services such as healthcare, smart cities and marine monitoring are pervasive in Cyber-physical environments strongly supported by Internet technologies and Fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within Fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, how to efficiently deal with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within Fog environments and increased computational complexity further dramatically aggravate this challenge. This article provides an overview of the core issues, challenges and future research directions in Fog-enabled orchestration for IoT services. Additionally, we present early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.

Citation: Z. Wen, R. Yang, P. Garraghan, T. Lin, J. Xu, and M. Rovatsos. Fog Orchestration for IoT Services: Issues, Challenges and Directions. IEEE Internet Computing, 2017. In press

Download: http://bit.ly/2iiyhfZ

Abstract: Despite the fact that social computation systems involve interaction mechanisms that closely resemble well-known models of agent coordination, current applications in this area make little or no use of the techniques the agent-based systems literature has to offer. In order to bridge this gap, this paper proposes a data-driven method for defining and deploying agent interaction protocols that is entirely based on using the standard architecture of the World Wide Web. This obviates the need of bespoke message passing mechanisms and agent platforms, thereby facilitating the use of agent coordination principles in standard Web-based applications. We describe a prototypical implementation of the architecture and experimental results that prove it can deliver the scalability and robustness required of modern social computation applications while maintaining the expressiveness and versatility of agent interaction protocols.

Citation: M. Rovatsos, D. Diochnos, and M. Craciun. Agent Protocols for Social Computation. Advances in Social Computing and Multiagent Systems, CCIS 541, Springer, 2015.

Download: http://bit.ly/2jcIfiG

Abstract: Ridesharing, that is, the problem of finding parts of routes that can be shared by several travelers with different points of departure and destinations, is a complex, multiagent decision-making problem. The problem has been widely studied but only for the case of ridesharing using freely moving vehicles not bound to fixed routes and/or schedules—ridesharing on timetabled public transport services has not been previously considered. In this article, we address this problem and propose a solution employing strategic multiagent planning that guarantees that for any shared journey plan found, each individual is better off taking the shared ride rather than traveling alone, thus providing a clear incentive to participate in it. We evaluate the proposed solution on real-world scenarios in terms of the algorithm’s scalability and the ability to address the inherent trade-off between cost savings and the prolongation of journey duration. The results show that under a wide range of circumstances our algorithm finds attractive shared journey plans. In addition to serving as a basis for traveler-oriented ridesharing service, our system allows stakeholders to determine appropriate pricing policies to incentivize group travel and to predict the effects of potential service changes.

Citation: J. Hrncir, M. Rovatsos, and M. Jakob. Ridesharing on Timetabled Transport Services: A Multiagent Planning Approach, Journal of Intelligent Transportation Systems, 19(1):89-105, 2015.

Download: http://bit.ly/2ipJ8mP

Abstract: The recent media focus on Smart City services, particularly ride sharing, that provide ordinary users with the ability to advertise their resources has highlighted society’s need for transparent and accountable systems. Current systems offer little transparency behind their processes that claim to provide accountability to and for their users. To address such a concern, some applications provide a static, textual description of the automated algorithms used, with a view to promote transparency. However, this is not sufficient to inform users exactly how information is derived. These descriptions can be enhanced by explaining the actual execution of the algorithm, the data it operated on, and the parameters it was configured with. Such descriptions about a system’s execution and its information flow can be expressed using PROV, a standardised provenance data model. However, given its generic and domain-agnostic nature, PROV only provides limited information about the relationship between provenance elements. Combined with semantic information, a PROV instance becomes a rich resource, which can be exploited to provide users with understandable accounts of automated processes, thereby promoting transparency and accountability. Thus, this paper contributes, a vocabulary for Smart City resource sharing applications, an architecture for accountable systems, and a set of use cases that demonstrate and quantify how the semantics enrich an account in a ride share scenario.

Citation: Heather Packer, Dimitris Diochnos, Michael Rovatsos, Ya’akov Gal, Luc Moreau, Semantics and Provenance for Accountable Smart City Applications, The Role of Semantics in Smart Cities, Semantic Web Journal special issue, 2014.

Download: http://bit.ly/2jcTK9B

Abstract: In this paper we present a novel approach to multiagent planning in domains with concurrent actions and associated concurrent action constraints. In these domains, we associate the actions of individual agents with subsets of objects, which allows for a transformation of the problems into single-agent planning problems that are considerably easier to solve. The transformation forces agents to select joint actions associated with a single subset of objects at a time, and ensures that the concurrency constraints on this subset are satisfied. Joint actions are serialised such that each agent performs their part of the action separately. The number of actions in the resulting single-agent planning problem turns out to be manageable in many real-world domains, thus allowing the problem to be solved efficiently using a standard single-agent planner. We also describe a cost-optimal algorithm for compressing the resulting plan, i.e. merging individual actions in order to reduce the total number of joint actions. Results show that our approach can handle large problems that are impossible to solve for most multiagent planners.

Citation: M. Crosby, A. Jonsson, M. Rovatsos. A Single-Agent Approach to Multiagent Planning. Proceedings of the 21st European Conference on Artificial Intelligence (ECAI 2014), Prague, Czech Republic, August 18-22, 2014.

Download: http://bit.ly/2jcNwGT

*** 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

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: While the development of techniques that allow artificial agents to engage in dialogue with humans has received a lot of interest in the multiagent systems and natural language processing literature, most of the systems created to date have focused on specific domains and types of dialogue. This has led to agent designs that are useful for specific dialogue situations, but hard to adapt to different settings. The creation of more flexible agents that can deal with a broad range of communicative scenarios would greatly improve the interaction between agents and humans, and would eliminate the need to manually adapt the design of a conversational agent when dealing with a new task domain. In this paper, we present initial work toward creating agents that are able to generate task-oriented dialogues based on a description of a previously unknown domain. Our method is based on utilising automated planning methods, which are suitable for processing specifications both of the communication language to be used and of the domain in hand. We provide preliminary experimental results which suggest that our method has the potential to provide the flexibility required to produce a broad range of communication behaviours in different settings.

Keywords: Dialogue Planning, Conversational Agents, Human-Agent Dialogue.

Citation: Tânia Marques and Michael Rovatsos. Toward Domain-Independent Dialogue Planning.

Download: http://bit.ly/1VzKPg1

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

In a new article for FoCAS (a Coordination Action sponsored by the European Union Future and Emerging Technology Unit) Michael Rovatsos discusses the challenges, and possibilities, emerging from a new generation of users empowered as producers of services.

Read the original article for details on how SmartSociety approaches these challenges and what our plans are for the future.

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.

EASSS 2014 will be held at the Technical University of Crete, Chania, Greece , from the 14th July to the 18th July 2014. Primarily focussed on single and multi-agent systems, this year’s school will feature additional courses covering topics related to social intelligence. It will be co-organised by the European Network for Social Intelligence and serves as their official Summer School.

Please note the deadline for early registration, which is the 31st May 2014. You can view the school’s complete schedule 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.

Citation: Miorandi, D., Maltese, V., Rovatsos, M., Nijholt, A., Stewart, J., Social Collective Intelligence: Combining the Powers of Humans and Machines to Build a Smarter Society, Springer, 2014.

Url: http://www.springer.com/gb/book/9783319086804

Smart Society’s M. Rovatsos has won second prize for best challenge and vision paper in AAMAS 2014!

Abstract: This paper proposes social computation, i.e. large-scale man-machine collaboration mediated by digital interaction media, as a vision for future intelligent systems, and as a new challenge for multiagent systems research. We claim that the study of social computation suggests a re-interpretation of many traditional AI endeavours, has huge potential application benefits, and presents the field of multiagent systems with novel, exciting research questions. We introduce an abstract model of social computation that helps capture some of its core research problems more precisely. We explore the potential contribution of multiagent systems technologies to the solution of these problems by exposing the close relationship between social computation and existing methods in multiagent systems. We describe how these methods could be reused in this novel application context, what methodological implications this has, and argue that the resulting cross-fertilisation will be highly beneficial for both sides.

Keywords: social computation, human-based computation, crowdsourcing, collective intelligence.

doi: http://dl.acm.org/citation.cfm?id=2617388.2617432

Citation: M. Rovatsos. Multiagent Systems for Social Computation (challenge paper), Thirteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), May 5-9, 2014.

Download: http://bit.ly/1W9uFYU