Collective Intelligence

Information-sharing and aggregation models for interacting minds

by Piotr Migdal

Motivations for Participation in Socially Networked Collective Intelligence Systems

by Jon Chamberlain

The open education evidence hub: a collective intelligence tool for evidence based policy

by Robert Farrow

De Liddo, Anna; Buckingham Shum, Simon; McAndrew, Patrick and Farrow, Robert (2012). The open education evidence hub: a collective intelligence tool for evidence based policy. In: Cambridge 2012: Joint OER12 and OpenCourseWare Consortium Global 2012 Conference, 16 - 18 April 2012, Cambridge, UK

This paper considers a Collective Intelligence approach to collating the evidence needed to support policy in open... more This paper considers a Collective Intelligence approach to collating the evidence needed to support policy in open education. A tool, called the OER Evidence Hub, provides an infrastructure for the OER community to collect examples and data of OER effectiveness and use and then supports the community and others such as policy makers with a community generated knowledge base to help decision making. We describe the Evidence Hub concept and features, present figures on user engagement, and discuss the results of initial user testing. We also show through examples how content can be seeded into the OER Evidence Hub, and illustrate the way in which it has captured exemplars identified by a particular community, the OER Advocacy group. Finally we discuss general issues and future strategies for building effective Collective Intelligence platforms for Open Education and other purposes.

Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks

by Mihail Mihaylov

Mihaylov, M., Le Borgne, Y-A., Tuyls, K. and Nowé, A. (2012) ‘Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks’, International Journal of Communication Networks and Distributed Systems, Vol. 9, Nos. 3/4, pp.207–224.

We present a self-organising reinforcement learning (RL) approach for scheduling the wake-up cycles of nodes in a... more We present a self-organising reinforcement learning (RL) approach for scheduling the wake-up cycles of nodes in a wireless sensor network. The approach is fully decentralised, and allows sensor nodes to schedule their active periods based only on their interactions with neighbouring nodes. Compared to standard scheduling mechanisms such as SMAC, the benefits of the proposed approach are twofold. First, the nodes do not need to synchronise explicitly, since synchronisation is achieved by the successful exchange of data messages in the data collection process. Second, the learning process allows nodes competing for the radio channel to desynchronise in such a way that radio interferences and therefore packet collisions are significantly reduced. This results in shorter communication schedules, allowing to not only reduce energy consumption by reducing the wake-up cycles of sensor nodes, but also to decrease the data retrieval latency. We implement this RL approach in the OMNET++ sensor network simulator, and illustrate how sensor nodes arranged in line, mesh and grid topologies autonomously uncover schedules that favour the successful delivery of messages along a routing tree while avoiding interferences.

Learning the Lessons of Openness

by Robert Farrow

McAndrew, P., Farrow, R., Law, P. and Elliot-Cirigotis, G. (2012). Learning the Lessons of Openness. In Proceedings of Cambridge 2012: Innovation and Impact - Openly Collaborating to Enhance Education. Cambridge, UK.

The Open Educational Resources (OER) movement has built up a record of experience and achievements since it was formed... more The Open Educational Resources (OER) movement has built up a record of experience and achievements since it was formed 10 years ago as an identifiable approach to sharing online learning materials. In its initial phase, much activity was driven by ideals and interest in finding new ways to release content, with less direct research and reflection on the process. It is now important to consider the impact of OER and the types of evidence that are being generated across initiatives, organisations and individuals. Drawing on the work of OLnet (http://olnet.org) in bringing people together through fellowships, research projects and supporting collective intelligence about OER, we discuss the key challenges facing the OER movement. We go on to consider these challenges in the context of another project, Bridge to Success (http://b2s.aacc.edu), identifying the services which can support open education in the future.

Tanja Aitamurto (2011) New ecosystem in journalism: Decentralized newsrooms empowered by self-organized crowds.

by Tanja Aitamurto

Knowledge Federation 2010: Self-Organizing Collective Mind. Second International Workshop on Knowledge Federation
Dubrovnik, Croatia, October 3-6, 2010.
Edited by Dino Karabeg and Jack Park.

Social Network Influences Decision Making During Collective Movements in Brown Lemurs (Eulemur fulvus fulvus)

by Cedric Sueur

Collective decision‐making and fission–fusion dynamics: a conceptual framework

by Cedric Sueur

Selective mimetism at departure in collective movements of Macaca tonkeana: an experimental and theoretical approach

by Cedric Sueur

From the first intention movement to the last joiner: macaques combine mimetic rules to optimize their collective decisions

by Cedric Sueur

Wisdom and Futures Studies

by Vahid V. Motlagh

Book Review: Wisdom, Consciousness, and the Future by Tom Lombardo. 461 pages. Bloomington, IN: Xlibris, 2011. ISBN13: 978-1-4628-8360-8. US$23.99 paper

In the coming decades we will witness a new collective enlightenment which many futurists describe as a “significant... more In the coming decades we will witness a new collective enlightenment which many futurists describe as a “significant jump in the collective mental functioning of humanity”. As expected by many writers, contemporary challenges and evolutionary forces will push humanity to a new level of “cosmic consciousness.” And for Lombardo, a core feature of this evolutionary transformation is “heightened future consciousness.”

Group decision-making in chacma baboons: leadership, order and communication during movement.

by Cedric Sueur

From Social Network (Centralized vs. Decentralized) to Collective Decision-Making (Unshared vs. Shared Consensus)

by Cedric Sueur

On Swarm Optimality In Dynamic And Symmetric Environments

by Yaniv Altshuler

The field of multi agents and multi robotics has become increasingly popular during
the last two decades.more The field of multi agents and multi robotics has become increasingly popular during
the last two decades.

The motivation behind multi agents based systems is that many tasks can be
much efficiently completed by using multiple simple autonomous agents (robots,
software agents, etc.) instead of a single sophisticated one.

However, when examining such systems, one may be concerned of the price-tag
attached to the decentralized nature of swarm based approaches. Meaning, while
we simplify designs and control mechanisms in order to save costs and
computation resources, how far do our systems drift from optimality~?

This work examines this issue by constructing an optimal algorithm for the
\emph{Dynamic Cooperative Cleaners} problem. The performance of the \textbf{SWEEP} algorithm
is compared to this of an optimal algorithm. The results of this comparison
show that not only that the swarm algorithm produces close results to the
optimal solution, but also as the problem gets harder, the performance of the
two converge.

In addition, insightful results concerning optimal swarms in symmetric
environments are presented.

The Cooperative Hunters-Efficient Cooperative Search for Smart Targets Using Uav Swarms

by Yaniv Altshuler

Cooperative Cleaners: A Study In Ant Robotics

by Yaniv Altshuler

Was published in the International Journal of Robotics Research

In the world of living creatures, ``simple minded'' animals often cooperate to
achieve common goals with amazing... more In the world of living creatures, ``simple minded'' animals often cooperate to
achieve common goals with amazing performance. One can consider this idea in
the context of robotics, and suggest models for programming goal-oriented
behavior into the members of a group of simple robots lacking global
supervision. This can be done by controlling the local interactions between the
robot agents, to have them jointly carry out a given mission. As a test case we
analyze the problem of many simple robots cooperating to clean the dirty floor
of a non-convex region in $\mbox{\bf Z}^{2}$, using the dirt on the floor as
the main means of inter-robot communication.

Swarm Intelligence—Searchers, Cleaners and Hunters

by Yaniv Altshuler

Swarm ant robotics for a dynamic cleaning problem—upper bounds

by Yaniv Altshuler

Multi-agent Cooperative Cleaning of Expanding Domains

by Yaniv Altshuler

Published in the International Journal of Robotics Research, 2010

Several recent works considered multi-a(ge)nt robotics in static environments.
In this work we examine ways of... more Several recent works considered multi-a(ge)nt robotics in static environments.
In this work we examine ways of operating in dynamic environments, where
changes take place independently of the agents' activity. The work focuses on a
dynamic variant of the \emph{Cooperative Cleaners} problem, a problem that requires several simple agents to clean a connected region of ``dirty'' pixels in $\mbox{\bf Z}^{2}$. A number of simple agents move in this dirty region, each having the ability to ``clean''
the place it is located in. Their goal is to jointly clean the given dirty region. The dynamic variant of the problem involves a
deterministic expansion of dirt in the environment, simulating spreading of
\emph{contamination}, or \emph{fire}. Theoretical lower bounds for the problem are
presented, as well as various impossibility results.
A cleaning protocol for the problem is presented, and a wealth of experimental results
testing its performance in comparison to the lower bounds.
Several analytic upper bounds for the proposed protocol are also presented, accompanied with appropriate experimental results.

Efficient Cooperative Search of Smart Targets Using UAV Swarms

by Yaniv Altshuler

This work examines the \emph{Cooperative Hunters} problem, where a swarm of
unmanned air vehicles (\emph{UAVs})... more This work examines the \emph{Cooperative Hunters} problem, where a swarm of
unmanned air vehicles (\emph{UAVs}) is used for searching after one or more
``evading targets'', which are moving in a predefined area while trying to
avoid a detection by the swarm.

By arranging themselves into efficient geometric flight
configurations, the UAVs optimize their integrated sensing
capabilities, enabling the search of a maximal territory.