1. Adf.ly

   Adf.ly is the oldest and one of the most trusted URL Shortener Service for making money by shrinking your links. Adf.ly provides you an opportunity to earn up to $5 per 1000 views. However, the earnings depend upon the demographics of users who go on to click the shortened link by Adf.ly.
   It offers a very comprehensive reporting system for tracking the performance of your each shortened URL. The minimum payout is kept low, and it is $5. It pays on 10th of every month. You can receive your earnings via PayPal, Payza, or AlertPay. Adf.ly also runs a referral program wherein you can earn a flat 20% commission for each referral for a lifetime.
   

2. CPMlink

   CPMlink is one of the most legit URL shortener sites.You can sign up for free.It works like other shortener sites.You just have to shorten your link and paste that link into the internet.When someone will click on your link.
   You will get some amount of that click.It pays around $5 for every 1000 views.They offer 10% commission as the referral program.You can withdraw your amount when it reaches $5.The payment is then sent to your PayPal, Payza or Skrill account daily after requesting it.
   
   • The payout for 1000 views-$5
   • Minimum payout-$5
   • Referral commission-10%
   • Payment methods-Paypal, Payza, and Skrill
   • Payment time-daily
   
   

3. Shrinkearn.com

   Shrinkearn.com is one of the best and most trusted sites from our 30 highest paying URL shortener list.It is also one of the old URL shortener sites.You just have to sign up in the shrinkearn.com website. Then you can shorten your URL and can put that URL to your website, blog or any other social networking sites.
   Whenever any visitor will click your shortener URL link you will get some amount for that click.The payout rates from Shrinkearn.com is very high.You can earn $20 for 1000 views.Visitor has to stay only for 5 seconds on the publisher site and then can click on skip button to go to the requesting site.
   
   • The payout for 1000 views- up to $20
   • Minimum payout-$1
   • Referral commission-25%
   • Payment methods-PayPal
   • Payment date-10th day of every month
   
   

4. Short.am

   Short.am provides a big opportunity for earning money by shortening links. It is a rapidly growing URL Shortening Service. You simply need to sign up and start shrinking links. You can share the shortened links across the web, on your webpage, Twitter, Facebook, and more. Short.am provides detailed statistics and easy-to-use API.
   It even provides add-ons and plugins so that you can monetize your WordPress site. The minimum payout is $5 before you will be paid. It pays users via PayPal or Payoneer. It has the best market payout rates, offering unparalleled revenue. Short.am also run a referral program wherein you can earn 20% extra commission for life.
   

5. Wi.cr

   Wi.cr is also one of the 30 highest paying URL sites.You can earn through shortening links.When someone will click on your link.You will be paid.They offer $7 for 1000 views.Minimum payout is $5.
   You can earn through its referral program.When someone will open the account through your link you will get 10% commission.Payment option is PayPal.
   
   • Payout for 1000 views-$7
   • Minimum payout-$5
   • Referral commission-10%
   • Payout method-Paypal
   • Payout time-daily
   
   

6. Bc.vc

   Bc.vc is another great URL Shortener Site. It provides you an opportunity to earn $4 to $10 per 1000 visits on your Shortened URL. The minimum withdrawal is $10, and the payment method used PayPal or Payoneer.
   Payments are made automatically on every seven days for earnings higher than $10.00. It also runs a referral system wherein the rate of referral earning is 10%.
   
   • The payout for 1000 views-$10
   • Minimum payout -$10
   • Referral commission-10%
   • Payment method -Paypal
   • Payment time-daily
   
   

7. Oke.io

   Oke.io provides you an opportunity to earn money online by shortening URLs. Oke.io is a very friendly URL Shortener Service as it enables you to earn money by shortening and sharing URLs easily.
   Oke.io can pay you anywhere from $5 to $10 for your US, UK, and Canada visitors, whereas for the rest of the world the CPM will not be less than $2. You can sign up by using your email. The minimum payout is $5, and the payment is made via PayPal.
   
   • The payout for 1000 views-$7
   • Minimum payout-$5
   • Referral commission-20%
   • Payout options-PayPal, Payza, Bitcoin and Skrill
   • Payment time-daily
   
   

8. Cut-win

   Cut-win is a new URL shortener website.It is paying at the time and you can trust it.You just have to sign up for an account and then you can shorten your URL and put that URL anywhere.You can paste it into your site, blog or even social media networking sites.It pays high CPM rate.
   You can earn $10 for 1000 views.You can earn 22% commission through the referral system.The most important thing is that you can withdraw your amount when it reaches $1.
   
   • The payout for 1000 views-$10
   • Minimum payout-$1
   • Referral commission-22%
   • Payment methods-PayPal, Payza, Bitcoin, Skrill, Western Union and Moneygram etc.
   • Payment time-daily
   
   

9. Linkrex.net

   Linkrex.net is one of the new URL shortener sites.You can trust it.It is paying and is a legit site.It offers high CPM rate.You can earn money by sing up to linkrex and shorten your URL link and paste it anywhere.You can paste it in your website or blog.You can paste it into social media networking sites like facebook, twitter or google plus etc.
   You will be paid whenever anyone will click on that shorten a link.You can earn more than $15 for 1000 views.You can withdraw your amount when it reaches $5.Another way of earning from this site is to refer other people.You can earn 25% as a referral commission.
   
   • The payout for 1000 views-$14
   • Minimum payout-$5
   • Referral commission-25%
   • Payment Options-Paypal,Bitcoin,Skrill and Paytm,etc
   • Payment time-daily
   
   

10. Linkbucks

    Linkbucks is another best and one of the most popular sites for shortening URLs and earning money. It boasts of high Google Page Rank as well as very high Alexa rankings. Linkbucks is paying $0.5 to $7 per 1000 views, and it depends on country to country.
    The minimum payout is $10, and payment method is PayPal. It also provides the opportunity of referral earnings wherein you can earn 20% commission for a lifetime. Linkbucks runs advertising programs as well.
    
    • The payout for 1000 views-$3-9
    • Minimum payout-$10
    • Referral commission-20%
    • Payment options-PayPal,Payza,and Payoneer
    • Payment-on the daily basis
    
    

11. Clk.sh

    Clk.sh is a newly launched trusted link shortener network, it is a sister site of shrinkearn.com. I like ClkSh because it accepts multiple views from same visitors. If any one searching for Top and best url shortener service then i recommend this url shortener to our users. Clk.sh accepts advertisers and publishers from all over the world. It offers an opportunity to all its publishers to earn money and advertisers will get their targeted audience for cheapest rate. While writing ClkSh was offering up to $8 per 1000 visits and its minimum cpm rate is $1.4. Like Shrinkearn, Shorte.st url shorteners Clk.sh also offers some best features to all its users, including Good customer support, multiple views counting, decent cpm rates, good referral rate, multiple tools, quick payments etc. ClkSh offers 30% referral commission to its publishers. It uses 6 payment methods to all its users.
    • Payout for 1000 Views: Upto $8
    • Minimum Withdrawal: $5
    • Referral Commission: 30%
    • Payment Methods: PayPal, Payza, Skrill etc.
    • Payment Time: Daily
    
    

12. Ouo.io

    Ouo.io is one of the fastest growing URL Shortener Service. Its pretty domain name is helpful in generating more clicks than other URL Shortener Services, and so you get a good opportunity for earning more money out of your shortened link. Ouo.io comes with several advanced features as well as customization options.
    With Ouo.io you can earn up to $8 per 1000 views. It also counts multiple views from same IP or person. With Ouo.io is becomes easy to earn money using its URL Shortener Service. The minimum payout is $5. Your earnings are automatically credited to your PayPal or Payoneer account on 1st or 15th of the month.
    
    • Payout for every 1000 views-$5
    • Minimum payout-$5
    • Referral commission-20%
    • Payout time-1st and 15th date of the month
    • Payout options-PayPal and Payza
    
    

13. BIT-URL

    It is a new URL shortener website.Its CPM rate is good.You can sign up for free and shorten your URL and that shortener URL can be paste on your websites, blogs or social media networking sites.bit-url.com pays $8.10 for 1000 views.
    You can withdraw your amount when it reaches $3.bit-url.com offers 20% commission for your referral link.Payment methods are PayPal, Payza, Payeer, and Flexy etc.
    
    • The payout for 1000 views-$8.10
    • Minimum payout-$3
    • Referral commission-20%
    • Payment methods- Paypal, Payza, and Payeer
    • Payment time-daily
    
    

14. LINK.TL

    LINK.TL is one of the best and highest URL shortener website.It pays up to $16 for every 1000 views.You just have to sign up for free.You can earn by shortening your long URL into short and you can paste that URL into your website, blogs or social media networking sites, like facebook, twitter, and google plus etc.
    One of the best thing about this site is its referral system.They offer 10% referral commission.You can withdraw your amount when it reaches $5.
    
    • Payout for 1000 views-$16
    • Minimum payout-$5
    • Referral commission-10%
    • Payout methods-Paypal, Payza, and Skrill
    • Payment time-daily basis
    
    

15. Short.pe

    Short.pe is one of the most trusted sites from our top 30 highest paying URL shorteners.It pays on time.intrusting thing is that same visitor can click on your shorten link multiple times.You can earn by sign up and shorten your long URL.You just have to paste that URL to somewhere.
    You can paste it into your website, blog, or social media networking sites.They offer $5 for every 1000 views.You can also earn 20% referral commission from this site.Their minimum payout amount is only $1.You can withdraw from Paypal, Payza, and Payoneer.
    
    • The payout for 1000 views-$5
    • Minimum payout-$1
    • Referral commission-20% for lifetime
    • Payment methods-Paypal, Payza, and Payoneer
    • Payment time-on daily basis
    
    

It has been remarked upon that I publish a lot of papers on a number of topics. It might not be clear how they all fit together. What is it that I work on, really? Of course, I have no problems seeing how the various strands of my research inform each other and contribute towards a small set of common goals. But that might not be so easy for you to see.

So here's an incomplete selection of themes from my recent research, together with links to some relevant papers. For simplicity and for the sake of the length of this text vs your attention span, I will limit myself to work that I've published in the last two years. Note that though I've written code for some of the research systems myself, contributed with conceptual development to all of these papers, and written parts of almost all of them, most of the work below was done by my various students and other collaborators. That's why I'll use the academic "we" in the examples below, referring to my various groups of collaborators. I'm of course very happy that so many talented and hard-working people feel like attaching my name to the author lists of their papers, for one reason or another.

Generally, my research has the dual aim of using AI to enable better (or more interesting) games, and on using games to enable better AI. This means coming up with better game-playing algorithms, with algorithms that can perhaps also play games in a human-like manner, methods for generating complete games or part of games, studying games and players, and for using games to test AI algorithms. This all comes together, for example you cannot design a game without being able to play it and knowing how humans play it, and you can't advance your game-playing AI without having suitable games and levels to try it out on. Ultimately, we're aiming towards general AI that can not only play any game but also design any game, including the game that you seem to want to play right now. That's very general, so let's be more specific.

Procedural content generation

PCG, creating game content with algorithms, is sort of hot right now. For several reasons: if the computers create levels, items, quests etc we don't have to do it ourselves, so games could be endless, adapt to the player, be cheaper to produce etc. Also, creating a content generators is about defining and understanding a particular aesthetic. My various collaborators and me have been working on PCG for quite some time now (actually, since before it was hot) in particular exploring how to best use evolutionary algorithms for generating various forms of game content. We call this search-based PCG. Some examples of recent work includes basing Super Mario level generation on design patterns, evolving maps to advantage particular game-playing algorithms, and multiobjective and multimodal methods for strategy game map generation.  We've also introduced new algorithms like constrained novelty search and repurposed older methods such as n-grams for level generation. Understanding the output of these generative methods is very important, and for that reason we have developed ways of characterizing level generators, and generic metrics for level design. A major effort was to edit and write the first textbook on procedural content generation in games; earlier we wrote about goals and challenges for PCG research.

Mixed-initiative and AI-assisted game design

While having the computer create levels, items, quests etc all by itself is great, there's also room for collaboration with humans. Computers are good at some things and humans at others, and human designers might want to interfere at various points in the content creation process. Mixed-initiative PCG systems implement a dialogue between a designer and computer, where PCG processes assist the human designer while retaining the designer's freedom. Sentient Sketchbook is one such system, where humans design strategy game maps while algorithms critique the designs and offer suggestions; it also learns the designer's style. Another of our systems is Ropossum, which can generate complete levels for the physics puzzler Cut the Rope and also assist designers. It uses a combination of grammatical evolution, reasoning and tree search, but we have recently experimented with using object path projections for playability testing and with creating levels based on evolved (fictive) playtraces.

Data games

There's more digital available than ever before, including large amounts of open data that anyone can access. This includes geographical, economical and demographical data amongst other forms. Data games are games that are built on such data, in the sense that the game's content is generated from open data. We're trying to create ways to improve the generation of meaningful game content by seeding PCG with real-world data, but also make data exploration more playful. Our work involves data-based content generation for existing games such as Open Data Monopoly and Open Data Civilization, using game mechanics for data exploration and visualization such as in Open Trumps and Bar Chart Ball, and data-based procedural generation of complete games as in Data Adventures.

Generating complete games

The logical endpoint of PCG is generating the whole game, not just the levels and textures but all the rules and mechanics and everything else that's not the game engine. Even what the game is about and what the goal is. If we are to understand game design properly, we need to build systems that can generate good games; and if we want to test general AI properly we need an infinite supply of new games. We have argued that those game types that would be most realistic to try yo generate are classical board games and simple 2D arcade games; this is also what we have been attempting to generate earlier. Recently, we have invented ways of representing and generating card games, by searching through a space of card games that includes well-known games such as Texas Hold'em. We have also designed a Video Game Description Language which can be used to define simple card games, and invented ways of automatically evaluating the quality of such games and generating new games. It is also interesting to see how different games can be generated by simply varying the parameters of a simple existing game – in our work on generating unique Flappy Bird variants we found that plenty of playable yet different games can emerge.

MCTS for video games

In order to be able to generate games you need to test them, and to test them automatically you need AI that can play the games. Being able to play games is of course also important for other reasons, such as providing good opponents and collaborators to the player. Monte Carlo Tree Search is a statistical tree search algorithm that has been very successful in playing board games such as Go. We are trying to figure out how to adapt this algorithm to video games, that have very different requirements from board games - for example continuous space and time, as well as lack of guarantee that random actions will lead to a terminal state. In the course of this, we have developed a number of MCTS modifications and MCTS-inspired algorithms for e.g. Super Mario Bros, car racing and general video game playing; the further success of MCTS variants can be seen in the first General Video Game Playing Competition, where the objective is to not just play one game but a whole set of different games.

Behavior imitation and procedural personas

Playing a game well is not all there is to playing a game – there's also the issue of playing style to consider. We've seen numerous cases where the best-playing AI plays the game in a decidedly non-humanlike manner. If we want to test a game or a level, or provide interesting NPCs in a game, we need to create AI that can play that game in the style of a particular human, or maybe humans in general. One approach is to train neural networks on gameplay traces as we've tested with Super Mario Bros. A more involved approach is to model the player as a ``procedural persona'', assuming bounded rationality and a set of objectives. This conceptual framework has been combined with q-learning and neuroevolution to play a roguelike dungeon crawler game in various styles. These procedural personas have also been used to evaluate levels in level generation algorithms. We also organized a Turing Test competition for Super Mario Bros-playing agents, where the objective was to fool judges into believing your bot was a human.

Game data mining and player modeling

The vast amount of data generated by modern games can be used to understand both games and their players. We call attempts to make sense of this data game data mining. Our efforts include crowd-sourcing platform game level preferences, and using this to study which micro-structures in the game levels best predict certain player responses. We have also found out that we can predict people's life motives from how they play Minecraft, and detect sexual predators from game chat logs. Another question that we have investigated with data mining is how game mechanics create meaning. Of course, much of the behavior imitation work above could be seen as game data mining as well. You could perhaps take this further if you allow the game to select what the player plays so as to learn more about the player.

Music and games

There's many similarities between music and games. For example, games are often experienced as quasi-linear structures with variations in intensity and "mood"; music is often also used to accompany and heighten the emotional expression of a game. We worked on affect-expressing music generation for games and on bidirectional communication between games and music so that playing becomes composing and composing becomes level designing. Given all the work that has been done in music generation, it seems reasonable that some of the methods and representations used there can be used for game content generation; here, we have investigated using functional scaffolding for level generation.

Surveying and organizing the research field

As I've been involved in the field of artificial and computational intelligence in games since it was just a baby (or at least just a symposium), I've had a chance to get some perspective on what we are doing, why and perhaps where we should be going. Our Panorama of CI/AI in games is an attempt to give a high-level overview of all the different research directions within the field and how they can inform and benefit each other. In some ways, it is a much longer (oh no!) version of this blog post with more pretentious language (really?) and also talks about other people's work. You should read it. We have also written surveys about computational creativity and games and neuroevolution in games. On top of that we recently organized a Dagstuhl seminar on the future of the field.

"Stuff"

There are are so many interesting things to do – ars longa, vita brevis. So when a nice idea comes by it's always a good idea to try to implement it, run some experiments and turn it into a paper, even though it might not fit perfectly into the current research direction that you've told yourself you're pursuing. Some of my "other" recent papers which I still consider very interesting deals with community structure detection in complex networks and geometric differential evolution. On particular note is our DeLeNoX system for computational creativity, which I think is really cool and should be used for... something.

Finally, just a note that we are not done yet. We don't have AI that can play any game or that can design a large variety of good, novel and meaningful games, so the job is not done. And when the job is done this very likely means that we'll have solved general AI and there is no more job to do at all for anyone. But until then: more work to do, come join.

Ingress APK

The description of Ingress

Fall semester, you participated in the 2017 UA Fall Game Jam! All of your submissions were amazing, as always.

Still working on the details, but our next hitless tournament will be in December and include both Bloodborne All Bosses (no DLC) and Dark Souls 2! Seeing more content in Bloodborne is going to be wild, and we may even have a few surprises in store for Dark Souls 2 as well.

More information to come soon. See you guys at the next tourney!

For this Tech Book Face Off, I felt like expanding my horizons a bit. Instead of reading about programming languages or software development or computer science and engineering, I thought I would take a look at some computer history from the business perspective. There are plenty of reading options out there in this space, but I settled on a couple of books about Microsoft. The first, Breaking Windows: How Bill Gates Fumbled the Future of Microsoft by David Bank, is about Bill Gate's hardball business tactics that won him a monopoly in the PC desktop market, but then nearly destroyed the company in that fateful confrontation with the US Justice Department and caused him to miss the Internet and, later, the mobile revolution. The second, Showstopper! The Breakneck Race to Create Windows NT and the Next Generation at Microsoft by G. Pascal Zachary, has an even longer subtitle that neatly describes the book on its own. Both of these books were written quite a while ago, so let's see how their stories hold up today.

VS.

Breaking Windows

The narrative starts out with the backstory of how Gates came into his PC desktop monopoly by realizing that software—specifically the computer's operating system—would be an important and valuable part of the PC ecosystem. As PC hardware got cheaper and more prevalent, the software volumes would grow with the spread of the hardware, and at essentially zero marginal cost to Microsoft. All they needed to do was become the defacto standard OS. That's what Gates set out to do, and he succeeded with Windows 3.1 and then Windows 95. The bulk of the story takes place after Microsoft had achieved its monopoly and was deciding on strategies to defend it.

One of the main strategies was to identify competitors that were creating software that was somewhat tangential to Windows or could be added as a compelling feature, and whose software was becoming popular enough to potentially pose a threat to Windows by becoming a new platform. Microsoft would then create their own version of that software and integrate it into Windows or otherwise absorb the other company's software, nullifying the threat to their monopoly.

The most prominent example of this absorption strategy came with Internet Explorer and the browser wars between Microsoft and Netscape. Netscape Navigator started out with nearly the entire market of the World Wide Web before Microsoft got into the browser business. By the time Microsoft had revved up to IE 3.0, they had claimed a significant amount of market share from Netscape, and because of bundling IE with Windows and offering it for free to older versions of Windows, Netscape was doomed to lose in the long (or not-so-long) run.

Everything was not all peaches and cream within Microsoft, though. There were two warring camps fighting for the soul of Microsoft. On one side was the Windows team led by Jim Allchin that was developing the next big thing: Windows NT. On the other side was the Internet Platform and Tools Division led by Brad Silverberg that wanted to leave Windows behind and try to capture as much of this new Internet frontier as possible, using IE as the platform. Gates would end up siding with Allchin and IE became a part of the Windows platform instead of growing into one of its own.

It's almost comical seeing some of these disagreements today. One of the most important features of the IE platform that was integrated into Windows as an option was Active Desktop, but this feature seems so inconsequential today. Making the desktop background a web page was fraught with problems, and all that has survived is a way to enable single-click icons instead of the usual double-click to run a program. I don't think hardly anyone used it, especially after dealing with multiple desktop crashes. I remember it being a novelty for a while, but I soon stopped enabling it because it was so annoying and a double-click is so ingrained in my desktop usage.

Of course, the disagreement with the Justice Department over Microsoft's monopoly was not so insignificant. Part of the reason their tactics got them into trouble was because IE was offered as a free upgrade for older versions of Windows that didn't have it or had older versions of IE. If Microsoft had truly made IE an integrated part of Windows and only released new versions of it with new versions of Windows, Microsoft's competitors wouldn't have had as strong of a case. Microsoft wouldn't have had as strong of a monopoly, either, because IE was getting new versions much faster than Windows was and people that didn't upgrade Windows were still getting free upgrades of IE.

Even so, the government's eventual breakup proposal was preposterous. They wanted to force Microsoft to set prices for Windows versions with and without IE based on how many bytes each version was, like it was produce or meat or something. The government obviously had no understanding of what software really was, no idea how ridiculous that sounded, or what a good solution to the real problems of Microsoft's monopoly would actually look like. In the end that proposal was dropped, and the entire court case seemed to have done nothing more than give Microsoft a ton of bad press.

In the mean time Gates had done plenty of other damage to Microsoft and Windows because of deciding to pursue these retrenchment strategies with the browser and other things related to the Internet. Bank makes the case that Gates should have pursued the Internet platform strategy in order to disrupt his own business and grab the larger market that was just coming to light, but I'm not so sure that would have worked, either. If he had done that, would he have been able to beat Google before they rose to the top, or would he have been able to foresee the coming of mobile and the smartphone before Apple took over with the iPhone? It's hard to imagine Microsoft getting all of that right and still being top dog today. (Although they're now doing quite well now under Satya Nadella.)

There was so much more in this book, like the section on how XML came to be. (Of course bloated, complicated XML was created at Microsoft. In the book it was portrayed as a genius innovation by Adam Bosworth that would help Microsoft take over Internet data flows in spite of Gate's decisions. I'm so glad JSON has stopped that nonsense.) I could keep going, but it's all in the book. It was a wonderful trip down memory lane, covering plenty of things I had forgotten about that were a big deal at the time (remember the AOL shortcut bundled on the Windows Desktop). The book is decently written, if a bit confusing at times. Bank jumps around a lot, and there's no overarching timeline to the narrative. Regardless, it gives great insights into what was happening at Microsoft through all of the turmoil in its history and is well worth the quick read.

Showstopper!

As the subtitle describes, Showstopper! is the story of how the first version of Windows NT was conceived and built. It makes for quite an engaging story, as the NT team was arranged within Microsoft in a unique way for the company. Instead of being a department that reported to and was directly overseen by Bill Gates, the team was more of a startup company within Microsoft that operated fairly independently and was left more or less to its own devices. Gates did check in and imposed some of his own requirements from time to time, but not anything like other departments within Microsoft.

One of the main reasons for this independence was the force of nature that was Dave Cutler, the chief architect and director of Windows NT. Cutler was aggressive and expected incredible things from his team, and he did not get along well with Gates, either. Gates had hired him when Cutler had left Digital Equipment Corp. and respected and trusted him enough to let Cutler run things as he saw fit, so Gates pretty much left him alone.

Cutler had brought along a number of programmers from his team at Digital to be the core of the NT team, and as he took on more Microsoft employees to build out the team, a rivalry emerged between the two groups: