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Rules Rule – GOFAI<\/b><\/p>\n
In this series of articles, I would like to discuss the basic nature of categories of AI\/ML algorithms and develop a common-sense perspective of the algorithms and their applications consciously not getting deeply into technical aspects as well as deal with those applications explicitly which touch our business and personal lives. In this first part I will bring out perspectives on so called \u2018Good Old Fashioned Artificial intelligence (GOFAI)\u2019<\/span><\/p>\n The thumb of Old-fashioned<\/b><\/p>\n We will start from where it all started and tell you what is now referred to as \u2018Good Old Fashioned Artificial intelligence (GOFAI) which was prominent during mid-1950s to mid-1990s is still relevant. Not that all young people ignore elderly old-fashioned people. Trying to mimic experts was the earliest approach to install intelligence in machines. Rules and thumb rules of experts get processed through their knowledge base to come out with answers for questions which often surprised the ordinary souls.\u00a0<\/span><\/p>\n How Rules rule?<\/b><\/p>\n In this approach knowledge is represented symbolically, and logical rules framed to simulate human intelligence. Knowledge can also be coded into rules and represented as trees which can be searched. Inverse deduction is one of the methods used to arrive at a result based on available conclusions. To get a feel of how GOFAI algorithms work lets take up a simple rule set and its implementation in prolog.<\/span><\/p>\n From the conclusions or data such as <\/span>\u2018Cow eats grass\u2019, \u2018Sheep eats grass\u2019, \u2018Horse eats horse grams\u2019, \u2018Grass is plant material\u2019, \u2018Horse grams is plant material\u2019, \u2018Herbivores eat plant material<\/span><\/i>\u2019, a general concept or result that <\/span>\u2018Horse is herbivores\u2019<\/span><\/i> can be arrived at. The below prolog program can answer whether cow is a herbivore and lion is a carnivore.<\/span><\/p>\n % Facts Prolog program<\/span><\/p>\n We define the facts about what each animal eats and that grass and horse grams are plant material. The rule herbivore(X) states that an animal X is a herbivore if it eats something (Y) that is plant material. Finally, we query whether \u201chorse\u201d satisfies the condition of being a herbivore.<\/span><\/p>\n When we run this Prolog program, it will deduce that \u201cHorse is Herbivores\u201d based on the given data.\u00a0 Unlike the imperative programming languages such as python or Java where we explicitly specify control flow using <\/span>if-then-else<\/span><\/i> statements,\u00a0 Prolog is a declarative language where we specify\u00a0<\/span>what<\/b>\u00a0we want to achieve rather than\u00a0<\/span>how<\/b>\u00a0to achieve it. Programs in Prolog consist of a set of\u00a0<\/span>facts<\/b>\u00a0(statements about the world) and\u00a0<\/span>rules<\/b>\u00a0(logical relationships). Prolog\u2019s execution model is based on\u00a0<\/span>resolution<\/b>, which involves searching for solutions by unifying facts and rules.<\/span><\/p>\n Did GOFAI leave its mark?<\/b><\/p>\n GOFAI is well-suited for building expert systems that emulate human expertise in specific domains. Examples include medical diagnosis, legal reasoning, financial advice, and troubleshooting complex machinery. MYCIN was an early expert system developed in the 1970s by Edward Shortliffe at Stanford University. Its primary purpose was to assist doctors in diagnosing and recommending treatments for patients with blood diseases, particularly bacterial infections.<\/span><\/p>\n Limitations of GOFAI<\/b><\/p>\n There are several notable limitations that have influenced the shift towards other AI approaches like machine learning and neural networks. Here are some key limitations:<\/span><\/p>\n Why should we get along with this old-fashioned guy?<\/b><\/p>\n GOFAI provides a foundational understanding of AI principles and techniques. It is still taught in AI courses to help students grasp the basics of symbolic reasoning and logic. You may wonder the relevance of this old fashioned fellow in the current AI world where we hear a lot about LLMs like ChatGPT.\u00a0<\/span><\/p>\n
\neats(cow, grass).
\neats(sheep, grass).
\neats(horse, horse_grams).
\neats(deer, grass).
\neats(lion, deer).
\nis_plant_material(grass).
\nis_plant_material(horse_grams).
\nis_animal_material(deer).
\n% Rule: Herbivores eat plant material
\nherbivore(X) :- eats(X, Y), is_plant_material(Y).
\n% Rule: Carnivores eat animal material
\ncarnivore(X) :- eats(X, Y), is_animal_material(Y).
\n% Query: Is Horse a herbivore?
\n?- herbivore(horse).
\n% Query: Is Lion a carnivore?
\n?- carnivore(lion).
\n% Query: Is Horse a herbivore?
\n?- herbivore(horse).<\/p>\n\n
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