Creating an internal teacher with natural language

 Natural language is a powerful tool for humans to describe the reality. THe existing vocabulary can be reused for robotcs applications. The only bottleneck is, that computers can't parse natural language directly but need a parser and an ontology for doing so. The following blog post explains the idea for a kitchen robot.

The starting point is an ontology which is realized as a python dictionary. The ontology stores items in a kitchen and possible actions for these items.

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items:
  apple, food
  banana, food
  plate, dishes
  pot, dishes
action
  prepate_meal, search(food)+eat(food)
  cleanup_kitchen, search(dishes)+search(trash)
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The system is realized as a command line prompt which asks the human operator to enter commands. A possible session is shown next:
$ apple
> apple is food, location is (10,1)
$ milk
> not_found
$ banana
> banana is a food, location is (20,4)
$ prepare_meal
> search(food) -> found: apple,banana
> eat(food) -> eat(apple), eat(banana)

The parser takes the human input and searches in the ontology for a definition, for location and for actions. Its some sort of text adventure game which works also with a parser and a database. If the user enters a command or object name which is not available in the database, the parser will answer the request with an error message. In other words, the intelligence of the AI doesn't depend on the parser itself but its the result of well populated database. Somebody has enter all the kitchen items into the database to make the system highly responsive.