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11 Simple Steps to Develop an Entertaining and Engaging Mobile Game App

Finally, you have decided to develop a mobile game app for your startup! Why not choose the easiest way to ensure its flawless development? We […]

The post 11 Simple Steps to Develop an Entertaining and Engaging Mobile Game App appeared first on Quytech Blog.

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Finally, you have decided to develop a mobile game app for your startup! Why not choose the easiest way to ensure its flawless development? We are really not exaggerating as we have taken these mobile app development steps straight from an expert’s diary. You will have to follow them one by one to turn your game app development idea into reality. So, why wait? Let’s check them out:

  1. Choose a game idea 
  2. Create an attractive game concept
  3. Select a mobile app platform and game engine
  4. Prepare a game design document
  5. Make the game structure
  6. Plan for wireframing 
  7. Write code
  8. Test your game
  9. Select the right monetization strategy
  10. Release the game app
  11. Take care of post-release support and maintenance

Now, let’s read all these steps in detail so that you can start the mobile game app development process:

Choose A Game Idea 

choose a game idea

The game idea is like the foundation of the game; it should be strong to increase the chances of your app’s success after the launch. Do thorough market research and check out on other mobile game apps that are ruling the App Stores and Play Stores. 

If you don’t come up with an innovative idea, then pick up an existing mobile idea and build a new app with unique and useful features that your targeted audiences are looking for. 

Create An Attractive Game Story

A story of a game gives its players a purpose to play. Moreover, it also helps to keep them engaged and hooked for hours. Think of such a game concept and then work hard to figure out how it will entertain the players. 

Select A Mobile App Platform And Game Engine

After choosing a unique game idea and preparing the right story, the next step is to decide on the platform you want to launch your app on. Considering the trends not just in the game industry but in the entire mobile app development world, it would be no wrong to say that you should develop your game app for multiple platforms. The reason is people these days people use different devices and hence, they prefer to play games which are accessible on all of them. 

Prepare A Game Design Document

This document will contain a detailed description of all the game elements. It will also include information about the game mechanics, tools, and technologies you will use for the development, game characters, and how it will use the screen space. Prepare it carefully. 

Make The Game Structure

make a game structure

Designing the game structure impacts heavily on its sales; therefore, it needs a great deal of attention to prepare the same. While designing the structure, you have to pay attention to the graphics, environment, texture, and other objects of the game. Decide if you are going to use 2D or 3D designating. 

Plan For Wireframing 

Wireframing gives you a clear view of the functioning, feel, and look of your mobile game application. You can consider it as a prototype or visual guide to your app. It will help you to understand a lot about mobile game development

Write Code

write code

Now, begin the coding part. Choose a suitable programming language and write flawless codes to ensure the game is being developed as per your expectations. 

Test Your Game

Testing each module right after it is developed can save a lot of cost. Therefore, don’t skip the step at any cost. 

Select The Right Monetization Strategy

Choosing the right monetization strategy beforehand will help you to get the maximum ROI. Therefore, pick the right one. The three most common strategies are- in-app advertisement, in-app purchases, and subscription/membership plans. If you are sure that players will definitely love your game and would be willing to pay for the download, then you can also make it paid. 

Release The Game App

Now, read the complete guidelines of the platform where you want to launch your app. Once your mobile game app fulfills all of them, hit the launch button. 

Take Care Of Post-Release Support And Maintenance

Releasing the app on the Play Store or App Store is not the last step of the mobile game app development. You need to provide regular support and maintenance services to ensure it works flawlessly all the time. 

How Much Does It Cost To Develop A Mobile Game App?

Now, when you know how to create a gaming app, let’s know how much it would cost you. To be honest, you first need to decide on the following factors:

  1. Type of game– Developing a casual game would cost much less than a multiplayer game. It means selecting the game genre is the first thing you would need to decide to calculate the estimated cost of the game app development. 
  2. Tools and technologies– Depending on the tools, game engines, and technologies, the cost of game app development might increase or decrease. 
  3. Platforms– The cost of developing an Android game app is different from an iOS game app; therefore, the cost can be determined only after you decide one from them. You can also choose to develop a mobile game for both these platforms; however, this may cost you a bit high. 
  4. Features and complexity- A mobile game app with highly advanced features (built using top technologies like AR, AI, ML, and more) will cost you higher than an app with basic features. 
  5. UI/UX design- The more interactive the design of a game is, the higher would be its price. That’s why it is one of the important factors to keep in mind while estimating the cost of game app development. 
  6. Time duration of the project- If a mobile game app takes a year to develop, then it would obviously cost much higher than the one that can be developed in three to four months.  
  7. Size and location of the game app development company– Hiring a mobile game app development company in The USA, UK, and other similar countries is costlier as compared to the companies in India, Bangladesh, and other such countries. 
  8. Post-launch support and maintenance- Depending on how much maintenance and support your app requires after its release, you can estimate the total cost of game app development. 
  9. Marketing and promotion- It is one of the significant factors that cannot be ignored while determining how much you would have to pay for game app development services. 

After listing down your choices for the aforementioned factors, you can have an idea of the amount you need to invest in a mobile game app development. On average, you might have to pay somewhere between USD $50,000 to USD $500,000 to build a mobile game app. 

In case, if you don’t want to develop the app on your own, then connect to a reliable mobile game development company with your particular app requirements. Choose the one carefully!

Final Words

Do you want to develop a mobile game on your own? Have a sound knowledge of programming and other development tools and technologies required for game app development? If the answer is yes, then you are in the right place. Here, in this blog, we have explained the game app development process in detail. You can follow these eleven steps to build a feature-rich, engaging, and entertaining mobile gaming app for your startup.

Source: https://www.quytech.com/blog/steps-to-develop-mobile-game-app/

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How does it know?! Some beginner chatbot tech for newbies.

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Wouter S. Sligter

Most people will know by now what a chatbot or conversational AI is. But how does one design and build an intelligent chatbot? Let’s investigate some essential concepts in bot design: intents, context, flows and pages.

I like using Google’s Dialogflow platform for my intelligent assistants. Dialogflow has a very accurate NLP engine at a cost structure that is extremely competitive. In Dialogflow there are roughly two ways to build the bot tech. One is through intents and context, the other is by means of flows and pages. Both of these design approaches have their own version of Dialogflow: “ES” and “CX”.

Dialogflow ES is the older version of the Dialogflow platform which works with intents, context and entities. Slot filling and fulfillment also help manage the conversation flow. Here are Google’s docs on these concepts: https://cloud.google.com/dialogflow/es/docs/concepts

Context is what distinguishes ES from CX. It’s a way to understand where the conversation is headed. Here’s a diagram that may help understand how context works. Each phrase that you type triggers an intent in Dialogflow. Each response by the bot happens after your message has triggered the most likely intent. It’s Dialogflow’s NLP engine that decides which intent best matches your message.

Wouter Sligter, 2020

What’s funny is that even though you typed ‘yes’ in exactly the same way twice, the bot gave you different answers. There are two intents that have been programmed to respond to ‘yes’, but only one of them is selected. This is how we control the flow of a conversation by using context in Dialogflow ES.

Unfortunately the way we program context into a bot on Dialogflow ES is not supported by any visual tools like the diagram above. Instead we need to type this context in each intent without seeing the connection to other intents. This makes the creation of complex bots quite tedious and that’s why we map out the design of our bots in other tools before we start building in ES.

The newer Dialogflow CX allows for a more advanced way of managing the conversation. By adding flows and pages as additional control tools we can now visualize and control conversations easily within the CX platform.

source: https://cloud.google.com/dialogflow/cx/docs/basics

This entire diagram is a ‘flow’ and the blue blocks are ‘pages’. This visualization shows how we create bots in Dialogflow CX. It’s immediately clear how the different pages are related and how the user will move between parts of the conversation. Visuals like this are completely absent in Dialogflow ES.

It then makes sense to use different flows for different conversation paths. A possible distinction in flows might be “ordering” (as seen here), “FAQs” and “promotions”. Structuring bots through flows and pages is a great way to handle complex bots and the visual UI in CX makes it even better.

At the time of writing (October 2020) Dialogflow CX only supports English NLP and its pricing model is surprisingly steep compared to ES. But bots are becoming critical tech for an increasing number of companies and the cost reductions and quality of conversations are enormous. Building and managing bots is in many cases an ongoing task rather than a single, rounded-off project. For these reasons it makes total sense to invest in a tool that can handle increasing complexity in an easy-to-use UI such as Dialogflow CX.

This article aims to give insight into the tech behind bot creation and Dialogflow is used merely as an example. To understand how I can help you build or manage your conversational assistant on the platform of your choice, please contact me on LinkedIn.

Source: https://chatbotslife.com/how-does-it-know-some-beginner-chatbot-tech-for-newbies-fa75ff59651f?source=rss—-a49517e4c30b—4

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Who is chatbot Eliza?

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Discover the whole story.

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Frédéric Pierron

Between 1964 and 1966 Eliza was born, one of the very first conversational agents. Its creator, Joseph Weizenbaum was a researcher at the famous Artificial Intelligence Laboratory of the MIT (Massachusetts Institute of Technology). His goal was to enable a conversation between a computer and a human user. More precisely, the program simulates a conversation with a Rogérian psychoanalyst, whose method consists in reformulating the patient’s words to let him explore his thoughts himself.

Joseph Weizenbaum (Professor emeritus of computer science at MIT). Location: Balcony of his apartment in Berlin, Germany. By Ulrich Hansen, Germany (Journalist) / Wikipedia.

The program was rather rudimentary at the time. It consists in recognizing key words or expressions and displaying in return questions constructed from these key words. When the program does not have an answer available, it displays a “I understand” that is quite effective, albeit laconic.

Weizenbaum explains that his primary intention was to show the superficiality of communication between a human and a machine. He was very surprised when he realized that many users were getting caught up in the game, completely forgetting that the program was without real intelligence and devoid of any feelings and emotions. He even said that his secretary would discreetly consult Eliza to solve his personal problems, forcing the researcher to unplug the program.

Conversing with a computer thinking it is a human being is one of the criteria of Turing’s famous test. Artificial intelligence is said to exist when a human cannot discern whether or not the interlocutor is human. Eliza, in this sense, passes the test brilliantly according to its users.
Eliza thus opened the way (or the voice!) to what has been called chatbots, an abbreviation of chatterbot, itself an abbreviation of chatter robot, literally “talking robot”.

Source: https://chatbotslife.com/who-is-chatbot-eliza-bfeef79df804?source=rss—-a49517e4c30b—4

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FermiNet: Quantum Physics and Chemistry from First Principles

Weve developed a new neural network architecture, the Fermionic Neural Network or FermiNet, which is well-suited to modeling the quantum state of large collections of electrons, the fundamental building blocks of chemical bonds.

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Unfortunately, 0.5% error still isn’t enough to be useful to the working chemist. The energy in molecular bonds is just a tiny fraction of the total energy of a system, and correctly predicting whether a molecule is stable can often depend on just 0.001% of the total energy of a system, or about 0.2% of the remaining “correlation” energy. For instance, while the total energy of the electrons in a butadiene molecule is almost 100,000 kilocalories per mole, the difference in energy between different possible shapes of the molecule is just 1 kilocalorie per mole. That means that if you want to correctly predict butadiene’s natural shape, then the same level of precision is needed as measuring the width of a football field down to the millimeter.

With the advent of digital computing after World War II, scientists developed a whole menagerie of computational methods that went beyond this mean field description of electrons. While these methods come in a bewildering alphabet soup of abbreviations, they all generally fall somewhere on an axis that trades off accuracy with efficiency. At one extreme, there are methods that are essentially exact, but scale worse than exponentially with the number of electrons, making them impractical for all but the smallest molecules. At the other extreme are methods that scale linearly, but are not very accurate. These computational methods have had an enormous impact on the practice of chemistry – the 1998 Nobel Prize in chemistry was awarded to the originators of many of these algorithms.

Fermionic Neural Networks

Despite the breadth of existing computational quantum mechanical tools, we felt a new method was needed to address the problem of efficient representation. There’s a reason that the largest quantum chemical calculations only run into the tens of thousands of electrons for even the most approximate methods, while classical chemical calculation techniques like molecular dynamics can handle millions of atoms. The state of a classical system can be described easily – we just have to track the position and momentum of each particle. Representing the state of a quantum system is far more challenging. A probability has to be assigned to every possible configuration of electron positions. This is encoded in the wavefunction, which assigns a positive or negative number to every configuration of electrons, and the wavefunction squared gives the probability of finding the system in that configuration. The space of all possible configurations is enormous – if you tried to represent it as a grid with 100 points along each dimension, then the number of possible electron configurations for the silicon atom would be larger than the number of atoms in the universe!

This is exactly where we thought deep neural networks could help. In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks. We now know how to train these networks efficiently and scalably. We surmised that, given these networks have already proven their mettle at fitting high-dimensional functions in artificial intelligence problems, maybe they could be used to represent quantum wavefunctions as well. We were not the first people to think of this – researchers such as Giuseppe Carleo and Matthias Troyer and others have shown how modern deep learning could be used for solving idealised quantum problems. We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

There is just one wrinkle when dealing with electrons. Electrons must obey the Pauli exclusion principle, which means that they can’t be in the same space at the same time. This is because electrons are a type of particle known as fermions, which include the building blocks of most matter – protons, neutrons, quarks, neutrinos, etc. Their wavefunction must be antisymmetric – if you swap the position of two electrons, the wavefunction gets multiplied by -1. That means that if two electrons are on top of each other, the wavefunction (and the probability of that configuration) will be zero.

This meant we had to develop a new type of neural network that was antisymmetric with respect to its inputs, which we have dubbed the Fermionic Neural Network, or FermiNet. In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant. The determinant of a matrix has the property that if you swap two rows, the output gets multiplied by -1, just like a wavefunction for fermions. So you can take a bunch of single-electron functions, evaluate them for every electron in your system, and pack all of the results into one matrix. The determinant of that matrix is then a properly antisymmetric wavefunction. The major limitation of this approach is that the resulting function – known as a Slater determinant – is not very general. Wavefunctions of real systems are usually far more complicated. The typical way to improve on this is to take a large linear combination of Slater determinants – sometimes millions or more – and add some simple corrections based on pairs of electrons. Even then, this may not be enough to accurately compute energies.

Source: https://deepmind.com/blog/article/FermiNet

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