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The Field of AI (Part 04): AI APPROACHES AND METHODS

AI & Games

A History of AI Research with Games

Games (computer games and board games alike) have been used in AI research and development since the early 1950s. Scientist and engineers focus on games to measure certain stages of success in AI developments. Game settings form a closed testing environment, as if it were a lab, within a specific set of rules and steps. Games have a clear objective or a clear set of goals. Games also allow to research and understand possible applications of probability (e.g. calculate the chances of winning if certain parameters are met or followed). Since very specific and focused problems need to be solved in specific game architectures, games are ideal to test Narrow AI applications.

Narrow AI solutions, are what have been achieved by scientist, so far, as opposed to a ‘General AI’ solution. A General AI solution (or ‘Strong AI’) would be a super-intelligent construct able to solve many, if not any, humanly-thinkable problem and / or beyond. The latter is still science fiction (until it is not). The former, Narrow AI solutions, exists in many applications and can be tested in a game setting. Results in such AI designs, within game play, can following be transcoded into other areas (e.g. solutions for language translation, speech recognition, weather forecast, sales predictions, autonomously operating mechanical arms, managing efficiency in a country’s electric grid[1] or other systems).

Some Narrow AI solutions use a method of Machine Learning that is called “Reinforcement Learning.” In simple terms, it is a way of learning by rewards or scoring. For that reason too, games are an obvious environment that can be used infinitely, to test and improve an AI application. Games lead to rewards or scores; one can even win them.

Moreover, a (computer) game can be played by multiple copies or versions of an AI solution, speeding up the process to reach the best solution or strategy (to win). The latter can, for instance, be achieved by means of “evolutionary algorithms.”  These are algorithms that are improving themselves through, for instance, mutations or  a process of selection as if through a biological natural selection of the fittest (i.e. an autonomous selecting, by means of a process, of a version or offspring of an algorithm that is better at solving something, while ignoring another that is not). Though, if the AI a plays a computer game that has a bug, it might exploit the bug to win, instead of learning the game[2].

Chess has been one of the first games, besides checkers, to have been approached by the AI research community.[3] As mentioned previously, in the mid-1950s Dr. SAMUEL, Arthur wrote a checkers program. A few years earlier (circa 1951) trials were made to write applications for both chess (by Dr. PRINZ, Dietrich) and checkers (by Dr. STRACHEY, Christopher). While these earliest attempts are presently perhaps dismissed as not really being a type of AI application (since, at times, some coding tricks were used), in those days they were a modest, yet first, benchmark of what was to come in the following decades.

For instance, on May 11 1997, the computer named “Deep Blue” beat Mr. KASPAROV,[4] the chess world champion of that time. A number of such achievements have followed covering a number of games. Compared to today’s developments Deep Blue is no longer that impressive. A few years ago, in 2014, by using a form of Machine Learning, namely Deep Learning, AlphaGo defeated the world champion Mr. Lee Sedol at Wéiqí (also known as the game of Go). That AI solution was later surpassed, by AlphaGo Zero (aka AlphaZero). This system used yet another form of Machine Learning, namely Reinforcement Learning (a method mentioned here previously). This AI architecture played against itself and then against AlphaGo. AlphaZero won all of the Wéiqí games from AlphaGo.

In 2017, LěngPūDàshī, the poker-playing AI, defeated some of the world’s top players in the Texas Hold ‘Em poker game. Now scientists are trying to defeat complex real-time online strategy video games players with AI solutions. While such games might not often be taken seriously by some people, they are, technically and through the lens of AI developments, far more complex then, for instance, a chess game. Some successes have already been booked: On April 17, 2019 an AI solution defeated Dota 2 champions. Earlier that same year, human players were defeated at a game of StarCraft II.  Note, the same algorithm that was trained to play Dota 2 can also be taught to move a mechanical hand. Improvements, in benchmarking AI solutions with games, do not stop.[5]

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Hands-on Learning with AI Research through Games

Project #___ : Build your own Game
AI with TIC TAC TOE (Arduino version):
Project source: 

Project #___ : TIC TAC TOE Iteration #2 (SCRATCH implementations): 
Project Source:
Project #___ : TIC TAC TOE
Iteration #3 (Berkeley’s SNAP! + Oxford AI implementation for K12):
Project Source:

[1] Anthony, S. (March 14, 2017). DeepMind in talks with the National Grid to reduce UK energy use by 10%. Online: ars technica. Retrieved February 14, 2020 from https://arstechnica.com/information-technology/2017/03/deepmind-national-grid-machine-learning/

[2] Vincent, J. (February 28, 2018). A Video game-playing AI beat Q*bert in a way no one’s ever seen before. Online: The Verge. Retrieved February 14, 2020 from https://www.theverge.com/tldr/2018/2/28/17062338/ai-agent-atari-q-bert-cracked-bug-cheat

[3] Copeland, J. (May, 2000). What is Artificial Intelligence? Sections: Chess. Online: AlanTuring.net Retrieved February 14, 2020 from http://www.alanturing.net/turing_archive/pages/Reference%20Articles/what_is_AI/What%20is%20AI12.html

[4] Kasparov, G. (March 25, 1996). The Day I Sensed a New Kind of Intelligence. Online: Time Retrieved February 14, 2020 from http://content.time.com/time/subscriber/article/0,33009,984305-1,00.html

[5] An example of the process of continued developments is very well unfolded here: https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go ; URL last checked on March 10, 2020