Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance

Methods of general applicability are searched for in swarm intelligence with the aim of gaining new insights about natural swarms and to develop design methodologies for artificial swarms. An ideal solution could be a `swarm calculus' that allows to calculate key features of swarms such as expected swarm performance and robustness based on only a few parameters. To work towards this ideal, one needs to find methods and models with high degrees of generality. In this paper, we report two models that might be examples of exceptional generality. First, an abstract model is presented that describes swarm performance depending on swarm density based on the dichotomy between cooperation and interference. Typical swarm experiments are given as examples to show how the model fits to several different results. Second, we give an abstract model of collective decision making that is inspired by urn models. The effects of positive feedback probability, that is increasing over time in a decision making system, are understood by the help of a parameter that controls the feedback based on the swarm's current consensus. Several applicable methods, such as the description as Markov process, calculation of splitting probabilities, mean first passage times, and measurements of positive feedback, are discussed and applications to artificial and natural swarms are reported

Enhancing Group Social Perceptiveness through a Swarm-based Decision-Making Platform

Swarm Intelligence is natural phenomenon that enables social animals to make group decisions in real-time systems. This process has been deeply studied in fish schools, bird flocks, and bee swarms, where collective intelligence has been observed to emerge. The present paper describes swarm.ai—a collaborative technology that enables swarms of humans to collectively converge upon a decision as a real-time system. Then we present the results of a study investigating if groups working as “human swarms” can amplify their social perceptiveness, a key predictor of collective intelligence. Results showed that groups reduced their social perceptiveness errors by more than half when operating as a swarm. A statistical analysis revealed with 99.9% confidence that groups working as swarms had significantly higher social perceptiveness than either individuals working alone or through plurality vote

Amplifying the Social Intelligence of Teams Through Human Swarming

Artificial Swarm Intelligence (ASI) is a method for amplifying the collective intelligence of human groups by connecting networked participants into real-time systems modeled after natural swarms and moderated by AI algorithms. ASI has been shown to amplify performance in a wide range of tasks, from forecasting financial markets to prioritizing conflicting objectives. This study explores the ability of ASI systems to amplify the social intelligence of small teams. A set of 61 teams, each of 3 to 6 members, was administered a standard social sensitivity test — Reading the Mind in the Eyes” or RME. Subjects took the test both as individuals and as ASI systems (i.e. “swarms”). The average individual scored 24 of 35 correct (32% error) on the RME test, while the average ASI swarm scored 30 of 35 correct (15% error). Statistical analysis found that the groups working as ASI swarms had significantly higher social sensitivity than individuals working alone or groups working together by plurality vote (p\u3c0.001). This suggests that when groups reach decisions as real-time ASI swarms, they make better use of their social intelligence than when working alone or by traditional group vote

Modeling and Mathematical Analysis of Swarms of Microscopic Robots

The biologically-inspired swarm paradigm is being used to design self-organizing systems of locally interacting artificial agents. A major difficulty in designing swarms with desired characteristics is understanding the causal relation between individual agent and collective behaviors. Mathematical analysis of swarm dynamics can address this difficulty to gain insight into system design. This paper proposes a framework for mathematical modeling of swarms of microscopic robots that may one day be useful in medical applications. While such devices do not yet exist, the modeling approach can be helpful in identifying various design trade-offs for the robots and be a useful guide for their eventual fabrication. Specifically, we examine microscopic robots that reside in a fluid, for example, a bloodstream, and are able to detect and respond to different chemicals. We present the general mathematical model of a scenario in which robots locate a chemical source. We solve the scenario in one-dimension and show how results can be used to evaluate certain design decisions.Comment: 2005 IEEE Swarm Intelligence Symposium, Pasadena, CA June 200

Creativity and Autonomy in Swarm Intelligence Systems

This work introduces two swarm intelligence algorithms -- one mimicking the behaviour of one species of ants (\emph{Leptothorax acervorum}) foraging (a `Stochastic Diffusion Search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a `Particle Swarm Optimiser', PSO) -- and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploliting an artistic tension between the local behaviour of the `birds flocking' - as they seek to follow the input sketch - and the global behaviour of the `ants foraging' - as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putative `creativity' of this hybrid swarm system in the philosophical light of the `rhizome' and Deleuze's well known `Orchid and Wasp' metaphor

Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’

On the origin of satellite swarms

For a species to develop in nature, two basically two things are needed: an enabling technology and a "niche". In spacecraft design the story is basically the same. Both a suitable technology and a niche application need to be there before a new generation of spacecraft can be developed. Last century two technologies have emerged that had and still have a huge impact on the development of technical systems: Micro-Electronics (ME) and Micro-Systems Technology (MST). Both are ruled by Moore's Law that indicates that considerable technology updates appear at the pace of years or even months instead of decades. Systems that need a development time of more than a few years will inevitably be based on "out-dated" and thereby difficult to maintain and repair technology unless during the development constant redesigns are made. This makes the development of the system at least very expensive. Although expenses do not seem to be a frequent show stopper in the design of spacecraft, it is still very interesting to investigate what system architectures might evolve when the specific properties of the new technologies ME and MST are fully exploited. ME presently offers more than 2 billion transistors on a chip and MST offers mechanical systems like resonators, mechanical switches, propulsions units, gyroscopes and many other sensors that _t in a volume of a few square millimeters to a few centimeters. So it is possible to fit a lot of signal processing power together with the necessary sensors and actuators in a volume that is really very small compared to any know space system. Of course state-of-the art spacecraft will immediately outperform these units in all aspects apart from cost and quantity. For the _rst time it makes sense to envisage the operation of formations of tens to hundreds of satellites that are cheap because they are based on standard commercial COTS technology and system designs. These satellite swarms will not be the systems that replace all other space systems. But, like in nature, there is a niche where swarms are the optimal solution. It's time to start occupying this niche. Typical properties of a swarm in nature are robustness, redundancy, large area coverage, the lack a hierarchical command structure, limited processing power per unit and self-organization ("swarm-intelligence"). This paper discusses the technological trends that lead to satellite swarms, where they can go and what new science they can create