Collective decision-making

Collective decision-making is the subfield of collective behaviour concerned with how groups reach decisions. Almost all aspects of behaviour can be considered in a decision-making context, but here we focus primarily on how groups should optimally reach consensus, what criteria decision-makers should optimise, and how individuals and groups should forage to optimise their nutrition. We argue for deep parallels between understanding decisions made by individuals and by groups, such as the decision-guiding principle of value-sensitivity. We also review relevant theory and empirical development for the study of collective decision making, including the use of robots

Collective decision-making

Collective decision-making is the subfield of collective behaviour concerned with how groups reach decisions. Almost all aspects of behaviour can be considered in a decision-making context, but here we focus primarily on how groups should optimally reach consensus, what criteria decision-makers should optimise, and how individuals and groups should forage to optimise their nutrition. We argue for deep parallels between understanding decisions made by individuals and by groups, such as the decision-guiding principle of value-sensitivity. We also review relevant theory and empirical development for the study of collective decision making, including the use of robots

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

Control and coordination of robotic fish

Het verbazingwekkende dynamische gedrag van scholen vissen en andere groepen sociale dieren in de natuur zijn in de afgelopen jaren in de belangstelling komen te staan van multidisciplinair onderzoek. In dit proefschrift passen we fundamentele gereedschappen uit de regeltechniek toe op biologische systemen om de regeling en coördinatie van robot multi-agent systemen bestuderen. We maken daarbij gebruik van robotvis teams die de natuur nabootsen. Als eerste onderzoeken we de motoriek van een individuele robotvis met als doel de uitstekende motorische vaardigheden van echte vissen na te bootsen. Vervolgens ontwerpen we gedistribueerde regelingen voor formaties van zwemmende robotvissen, die sinusoïde lichaamsgolven genereren in antifase. Deze regeling is geïnspireerd door de observatie dat formaties van gesynchroniseerde vissen mogelijkerwijs met een hogere energie efficiëntie zwemmen. Als derde presenteren we een evolutionair spel model om groepen robotvissen aan te sturen, dat gebaseerd is op het gecoördineerde gedrag van vissen in scholen en andere collectieve bewegingen van sociale dieren. Daarbij bestuderen we de opkomst en evolutie van samenwerking tussen de vissen in een multi-robotvis water polo wedstrijd. Gebruik makend van deze gereedschappen en evolutionaire speltheorie, ontwikkelen we tot slot een multi-robotvis setup om een nieuw kader te construeren voor de studie van diversificatie van persoonlijkheden en de opkomst van leiderschap, die cruciaal zijn voor de voltooiing van groepstaken

Antimicrobial resistance and Neisseria gonorrhoeae multiantigen sequence typing (NG-MAST) genotypes in N. gonorrhoeae during 2012-2014 in Karachi, Pakistan

Background: Globally, increasing antimicrobial resistance (AMR) in Neisseria gonorrhoea has led to decreased treatment options for gonorrhoea. Continuous monitoring of resistance is crucial to determine evolving resistance trends in Neisseria gonorrhoea and to suggest treatment recommendations. Quality assured gonococcal AMR data from Pakistan are mainly lacking. This study was performed to determine prevalence and trends of gonococcal AMR and molecular epidemiology of local strains during 2012-2014 in Karachi, Pakistan. Methods: Gonococcal isolates (n = 100) were obtained from urogenital specimens submitted to the Aga Khan University Laboratory, Karachi, Pakistan. Antimicrobial susceptibility was determined using Etest and molecular epidemiology was assessed by N. gonorrhoeae multiantigen sequence typing (NG-MAST). Quality control was performed using N. gonorrhoeae WHO reference strains C, F, G, K, L, M, N, O, and P, and ATCC 49226. Results: Susceptibility to spectinomycin, ceftriaxone and cefixime was 100 % and to azithromycin was 99 %. All isolates had low ceftriaxone MICs, i.e., ≤0.032 mg/L. Resistance to ciprofloxacin, tetracycline and penicillin G were 86 %, 51 % and 43 %, respectively. NG-MAST analysis identified 74 different sequence types (STs). Conclusions: A highly diversified gonococcal population, 74 NG-MAST STs (62 novel STs) with an increased resistance to penicillin G, ciprofloxacin and tetracycline circulated in Karachi, Pakistan. Fortunately, no resistance to ceftriaxone was detected. Accordingly, ceftriaxone can continuously be recommended as the treatment of choice. However it is recommended to increase the dose of ceftriaxone from 125 mg intramuscularly to 250 mg intramuscularly due to ceftriaxone MIC creep and emerging resistance reported in the region. Furthermore, due to the high level of resistance to ciprofloxacin (86 %) it is essential to exclude ciprofloxacin from the recommended first-line therapy. It is imperative to significantly broaden the gonococcal AMR monitoring with participation from other laboratories and cities in Pakistan