ShanghAI Lectures: Samia Nefti-Mezziani “Non-rational Particle Swarm Optimization”

09 January 2014

share this:

Nefti-MezianiSamiaGuest talk in the ShanghAI Lectures, 2009-12-03

Among the evolutionary algorithms, Particle Swarm Optimization (PSO) represents an optimization method where individuals, called particles, collaborate as a swarm to reach a collective goal. However, the usual logical decision processes used in the literature to model individual agent behaviour are generally found to be inadequate when the phenomena of uncertainty and risk are factored into the evolutionary process; and also incapable of fully emulating actual human decision-making behaviours under risk and uncertainty.

In this talk, I will propose a significant modification to agent reasoning processes employed so far in conventional Swarm Intelligence Techniques. I will show that endowing each particle with a non-rational behaviour, in the sense of Prospect Theory, can improve considerably the efficiency of global searching procedures. The results of this proposed technique is illustrated by numerical results obtained from applications to classical problems quoted in the literature.

The ShanghAI Lectures are a videoconference-based lecture series on Embodied Intelligence, run and organized by Rolf Pfeifer (from 2009 till 2012), Fabio Bonsignorio (since 2013), and me with partners around the world.

Dr Samia Nefti is Associate Professor Reader in Computational Intelligence at University of Salford and head of Computational Intelligence and Robotics Research Group. She is a leading expert internationally in artificial intelligence and her research interests over the last 19 years are concerned with the development of cognitive models for information processing and decision support systems for complex and non-linear systems and fuzzy optimasation and clustering. Dr Nefti has published and edited extensively in the above areas, which appeared in leading academic journals. She has also organized many international and national conference and workshop. She has been involved and led national (EPSRC, TSB) and European (FP5, FP6) multi-disciplinary research projects. She is a Chartered member of BCS and active member of the European Network for the Advancement of Artificial Cognitive Systems and Vice-President of IEEE UKRI Chapter in Robotics and Automation.

The ShanghAI lectures have brought us a treasure trove of guest lectures by experts in robotics. You can find the whole series from 2012 here. Now, we’re bringing you the guest lectures you haven’t yet seen from previous years, starting with the first lectures from 2009 and releasing a new guest lecture every Thursday until all the series are complete. Enjoy!

tags: , , , ,

Nathan Labhart Co-organizing the ShanghAI Lectures since 2009.
Nathan Labhart Co-organizing the ShanghAI Lectures since 2009.

Related posts :

Estimating manipulation intentions to ease teleoperation

Introducing an intention estimation model that relies on both gaze and motion features.
06 December 2022, by and

Countering Luddite politicians with life (and cost) saving machines

Beyond aerial tricks, drones are now being deployed in novel ways to fill the labor gap of menial jobs that have not returned since the pandemic.
04 December 2022, by

Call for robot holiday videos 2022

That’s right! You better not run, you better not hide, you better watch out for brand new robot holiday videos on Robohub!
02 December 2022, by

The Utah Bionic Leg: A motorized prosthetic for lower-limb amputees

Lenzi’s Utah Bionic Leg uses motors, processors, and advanced artificial intelligence that all work together to give amputees more power to walk, stand-up, sit-down, and ascend and descend stairs and ramps.

Touch sensing: An important tool for mobile robot navigation

Proximal sensing often is a blind spot for most long range sensors such as cameras and lidars for which touch sensors could serve as a complementary modality.
29 November 2022, by

Study: Automation drives income inequality

New data suggest most of the growth in the wage gap since 1980 comes from automation displacing less-educated workers.
27 November 2022, by

©2021 - ROBOTS Association


©2021 - ROBOTS Association