How to avoid micromanagement with swarm intelligence (step-by-step guide )

How to avoid micromanagement with swarm intelligence (step-by-step guide )

To participate in investment opportunity token sales on the Swarm platform, you’re first going to need some crypto in your wallet. This is a step-by-step guide to depositing or sending SWM into your Swarm platform wallet. You can also use this guide to transfer Ether (ETH) or DAI Stablecoin (DAI). In this guide we will make the transfer using MyEtherWallet.

How to avoid micromanagement with swarm intelligence (step-by-step guide )

  1. Go to the Swarm website and click Get Started.
  2. Sign in to your account, or set up a new one.
  3. Navigate to your wallet:

How to avoid micromanagement with swarm intelligence (step-by-step guide )

4. Click Deposit and from the dropdown, select the Asset type as SWM.

5. Request a new address by clicking the Request new address button

6. You will be shown a QR code and below it a long string of numbers. This is the SWM address you will be sending to. Each time you want to deposit, confirm the address is still valid or request a new one. Copy the address.

  1. Go to MyEtherWallet (MEW): https://www.myetherwallet.com/#send-transaction
  2. Load your wallet using one of the methods listed.

If the token you want to send is already displayed in Token Balances, you can skip this part. These instructions are for adding SWM. To add other tokens, you will need their token contract address, symbol, and number of decimals.

  1. On the right of the page you will see Token Balances.
  2. Click Add Custom Token

3. For the field titled Token Contract Address, enter:
0x9e88613418cf03dca54d6a2cf6ad934a78c7a17a

4. For the field titled Token Symbol, enter: SWM

5. For the field titled Decimals, enter: 18

7. You should now see your SWM Token Balance displayed.

How to avoid micromanagement with swarm intelligence (step-by-step guide )

You will need some Ether in your wallet to pay the fees (gas) for the transfer.

  1. In the To Address field, enter the SWM address you copied from the Swarm deposit screen
  2. In the Amount to Send field, enter the amount of SWM tokens you will like to deposit.
  3. Select SWM from the dropdown to the right of Amount to Send.
  4. MEW will display a recommended Gas Limit based on current network conditions. If it doesn’t or you want to be sure the transaction will not run out of gas, enter 300,000 or more. Note: You will only pay enough gas to make sure the transaction goes through. Avoid frustration and set the Gas Limit high from the start.
  5. Click Generate Transaction.
  6. Click Send Transaction.
  7. Confirm your transaction by following the next steps in your sending wallet.

Give your transaction some time to be processed, and then check the balance of your wallet on the Swarm platform. If you didn’t run out of gas and nothing else went wrong, you will now see an updated balance of SWM tokens:

In the next guide in this series, we will show you how to use the funds you have deposited to buy tokens in the investment opportunities on Swarm.

Add to Mendeley

Abstract

In swarm robotics, it is necessary to develop methods and strategies that guide the collective execution of tasks by the robots. The design of such tasks can be done considering it as a collection of simpler behaviors, called subtasks. In this paper, the Wave Swarm is presented as a general strategy to manage the sequence of subtasks that compose the collective navigation, which is an important task in swarm robotics. The proposed strategy is based mainly on the execution of wave algorithms. The swarm is viewed as a distributed system, wherein the communication is achieved by message passing among robot’s neighborhood. Message propagation delimits the start and end of each subtask. Simulations are performed to demonstrate that controlled navigation of robot swarms/clusters is achieved with three subtasks, which are recruitment, alignment and movement.

Previous article in issue
Next article in issue

Keywords

Selection and peer-review under responsibility of the Scientific Programme Committee of ICCS 2016.

Recommended articles

Citing articles

Article Metrics

  • About ScienceDirect
  • Remote access
  • Shopping cart
  • Advertise
  • Contact and support
  • Terms and conditions
  • Privacy policy

We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies .

Add to Mendeley

Abstract

This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.

Previous article in issue
Next article in issue

Keywords

Peer review under responsibility of China Ordnance Society

Recommended articles

Citing articles

Article Metrics

  • About ScienceDirect
  • Remote access
  • Shopping cart
  • Advertise
  • Contact and support
  • Terms and conditions
  • Privacy policy

We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies .

Whilst it’s true that the rise Artificial Intelligence threatens industries and jobs alike, it also presents an opportunity for humans and teams to embrace the new paradigm by staying one step ahead and making themselves smarter and more capable in harnessing collective intelligence. The term collective intelligence refers to the resulting knowledge or wisdom that ensues when many agents or individuals are involved in a group and where this type of ‘intelligence’ cannot exist through an individual endeavour. It is therefore important that in the face of tectonic shifts in technology and the rise of intelligent machines coupled with the threat of automation; teams and humans embrace a form of ‘swarming’ in order to not only future proof themselves but create the right type of environment to achieve outcomes that could not be reached through individual pursuits. In this article, I refer to various examples of how Nature’s team achieve this ‘swarm intelligence’ and appropriate how these can be achieved in the organisational setting through Bioteaming.

How to avoid micromanagement with swarm intelligence (step-by-step guide )

For billions of years, collective intelligence and swarm behaviour has been evident in Nature where social species like Bees work together to make better decisions such as when they want to setup a new colony. In the context of Bees, a few hundred scout bees will journey into different directions to look for potential nest locations. Once identified, the scouts perform a waggle dance which functions like a one way broadcast to share the direction and distance to the new nest site locations with other members of the colony. Different scouts may have attempted to pull the swarm towards or away from their preferred direction and eventually the colony decides as a group which scout to follow, making a decision no individual bee could have ever made on their own. This use of an effective ‘collective brain’ is also evident in ant colonies where millions of them come together to create what is perceived as a complex two way highway buzzing with traffic. Dr. Couzin a mathematical biologist at Princeton University and the University of Oxford notes that Army Ants, in particular, “build the bridges with their living bodies….”they build them up if they’re required, and they dissolve if they’re not being used”. This shows how ants are able to optimise to its environment, another fsacinating display of scaling and adaptability.

The examples of the Bees and Ants herewith are great examples of stigmergy as a mechanism for indirect coordinator between agents or actions, something that human teams must embrace to nurture collective intelligence. To recap, stigmergy is a form of self organistion where it produces complex, seemingly intelligent structures without the need for any planning, control or even direct communication between the agents. As such, it supports efficient collaboration between extremely simple agents, who lack any memory, intelligence or even individual awareness of each other.

So how do humans and teams achieve the same degree of super intelligence that is exhibited by group interaction from the social insects? How do teams achieve a level of amplification that enables them to make better decisions, predictions, estimations and forecasts and as a result – achieve outcomes quicker and in a more effective manner? Ultimately, the ability to adapt and solve problems are all ascribed to intelligence as a collective capacity and it must be nurtured and deployed strategically.

How to avoid micromanagement with swarm intelligence (step-by-step guide )

To contextualise and formulate this for humans and teams, its worthy to note that the interactions that nurture collective intelligence in Nature’s teams are governed by fundamentally simple rules. The culmination and adherence of these rules lead to the emergence of complex and subsequent autonomous and intelligent behaviour. This then brings on identification of the optimal solution or a resulting type of ‘swarm intelligence‘. Ants for example use antennas to asses if it makes contact with another ant and turns around and slows down if so to avoid collisions.

A study into Morman crickets found that their collective movement causes the crickets to form vast swarms. Dr Couzin observed that “they’re trying to attack the crickets who are ahead, and they’re trying to avoid being eaten from behind” which gives insight into agility and the ability to act like a collective mind. Therefore, for humans and teams, it’s important to understand that in settings where you are working with project members that span different departments, geographies and cultural varieties – the best way to leverage everyone’s capabilities is to implement a framework that nurtures shared decision making in combination with collaborative style effective communication.

Bioteams exhibit elements of swarm intelligence in group settings by defining themselves in terms of transformations, not outputs. This is a contrast to the traditional model of teamwork wherein activities, tasks or outputs are explicitly defined. If a team wishes to embrace collective intelligence and swarm behaviours, they should define themselves in terms of the transformations they wish to make on their network components. Network components are the sub of internal and external stakeholders as well as processes, systems and people capabilities intrinsic to the enterprise. By doing this, it will be possible to enable humans to think together as super intelligent systems that connect groups of other individuals from all areas of expertise together over computer networks and virtual technologies, thereby enabling everyone to think together as a system that emulates the intelligence that emerges from swarms in Nature.

A critical factor in creating the right technological and process mix for collective and swarm intelligence in enterprises and human teams is to traverse the use of polls and surveys (which have been the most common method for harnessing the intelligence of human groups for several decades) and focusing on moving towards real time communication, innovation frameworks and methodologies that inspire teams under the tenets of distributed leadership. These practices should be underpinned by a few set of rules that support a collaborative orientation.

Ultimately, as technologies, people capabilities and process continually coalesce; its quite clear that Bioteaming like models of distributed, collaborative leadership and teamwork will ultimately define the evolution of teams within organisations. Dr R Meredith Belbin, regarded as the father of team-role theory, prophesises that teams will take more biological forms as they learn from a “diminuitive masterclass” of sical insects such as bees, ants and terminates.

How to avoid micromanagement with swarm intelligence (step-by-step guide )

Max is a Bioteaming Practitioner, Author, Strategic Innovation and Change Management Consultant.

Add to Mendeley

Abstract

Multipopulation methods, which can enhance the population diversity, are well suited for dynamic optimization. However, there are still some challenges need to be tackled when multipopulation methods are employed, namely, how to avoid sensitive parameters when creating sub-populations, and how to effectively adapt to the changing optima continuously during the search process. Therefore, a novel multipopulation algorithm based on the affinity propagation clustering is proposed to address the above challenges. In the proposed method, affinity propagation clustering is applied for automatically creating sub-populations by message-passing process, which can avoid some extra parameters. Moreover, a simple but effective strategy, denoted as optimal particles relocation, is proposed for responding to environmental changes. In this strategy, the best particles in each sub-population are first stored in a memory. Then, local search is applied for helping the memory to quickly locate new peaks, if the environmental change has occurred. To validate the performance of the proposed algorithm, a variety of experiments have been conducted. The experimental results have demonstrated that the proposed algorithm performs robustly and competitively under different environments.

Previous article in issue
Next article in issue

Keywords

No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.knosys.2020.105711.

Recommended articles

Citing articles

Article Metrics

  • About ScienceDirect
  • Remote access
  • Shopping cart
  • Advertise
  • Contact and support
  • Terms and conditions
  • Privacy policy

We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies .