Programs with Common Sense

1. Programs with Common Sense Mingzhe Du and Hongying Du April, 2011 • This paper “Programs With Common Sense” was written by John McCarthy in 1959, Stanford University

2. Outline • Programs with common sense • Introduction • Basic Concept • The Construction of Advice Taker: an example • Common sense database • Common sense problem

3. Why common sense: Software Is BRITTLE • Example • Google: “Is the Eiffel tower taller than the Dubai Burj Al Arab?” • VS • Eliza: a computer therapist in 1965, MIT http://www.manifestation.com/neurotoys/eliza.php3

4. Eliza: a computer therapist in 1965, MIT http://www.manifestation.com/neurotoys/eliza.php3

5. Eliza: a computer therapist in 1965, MIT http://www.manifestation.com/neurotoys/eliza.php3

6. John McCarthy • born in Boston in 1927 • Invention of Lisp • one of the founders of artificial intelligence and knowledge representation. • Programs with CommonSense – the birth of knowledge representation

7. Programs with common sense • Introduction and Concept • The Construction of Advice Taker: an example

8. Programs with common sense • Advice Taker: is a proposed program for solving problems by manipulating sentences in formal languages. • The basic program will draw immediate conclusions from a list of premises. These conclusions will be either Declarative or Imperative sentences.

9. Programs with common sense Imperative Sentence When an imperative sentence is deduced the program takes a corresponding action. These actions may include printing sentences, moving sentences on lists, and reinitiating the basic deduction process on these lists. Declarative Sentence Describing the situation in which action is required together with a few imperatives that say what is wanted.

10. Programs with common sense • Main Advantages: we expect the advice taker’s behavior will be improvable merely by making statements to it, telling it about its symbolic environment and what is wanted from it.

11. Programs with common sense • A program has common sense if it automatically deduces for itself a sufficiently wide class of immediate consequences of anything it is told and what it already knows.

12. Programs with common sense • “We base ourselves on the idea that: In order for a program to be capable of learning something it must first be capable of being told it.” -- John McCarthy

13. Programs with common sense Features an intelligent system should have: • All behaviors must be representable in the system. • Interesting changes in behavior must be expressible in a simple way. • All aspects of behavior except the most routine must be improvable. • The machine must have or evolve concepts of partial success. • The system must be able to create subroutines which can be included in procedures as units.

14. Programs with common sense • Our Ultimate Objective Make programs that learn from their experience as effectively as humans do.

15. Programs with common sense • Introduction • The Construction of Advice Taker: an example

16. Programs with common sense • An example: • Assume that I am seated at my desk at home and I wish to go to the airport. • My car is at my home also. • The solution of the problem is to walk to the car and drive the car to the airport.

17. Programs with common sense • 17 Premises • a predicate ``at''. ``at(x,y)'' is a formalization of ``x is at y''.

18. Programs with common sense

19. Programs with common sense

20. Programs with common sense Here prog(y,u) is the program of first carrying out y and then u.

21. Programs with common sense

22. Programs with common sense • Deduction Process • According to • x: home, y: desk, z: car • We can deduce

23. Programs with common sense • Similarly, we can get • Then According to We can deduce

24. Programs with common sense • According to • We can deduce

25. Programs with common sense • According to • We can deduce

26. Programs with common sense • The need for a computer to be able to perform commonsense reasoning • The need for a computer to have a formal representation of the commonsense • The representation of such knowledge, along with an inference method to reason with this knowledge, was an essential part of any artificial intelligence

27. Controversy • Philosopher and linguist Yehoshua Bar-Hillel: • The example of formal commonsense reasoning given in the paper was oversimplified and that any proper formalization of an example would necessarily be much longer and more complex.

28. Common sense database: open mind common sense • In 1999, Common Sense Computing Initiative began at the MIT Media Lab to collect common sense from volunteers on the internet. • Now, the English site has over a million sentences from over 15,000 contributors.

29. A planning problem:The Wolf and the Bush  • A small girl is walking through a forest to visit her grandmother, and she passes a bush behind which a Wolf is hiding, planning to pounce out and eat her. Just as she gets close, however, the Wolf hears the singing of the woodcutters as they start work nearby. The Wolf therefore decides to stay hidden and not pounce on the little girl after all. • The problem is to explain why the Wolf decides to stay behind the bush. • Contributed by Pat Hayes and Lokendra Shastri  (9th July, 1997) • More examples at http://www-formal.stanford.edu/leora/commonsense/

30. Common sense problem • If one encounters no difficulty along the way, one should be suspicious of the adequacy of the solution. • It’s more difficult than they appear at first glance. • Few of the problems listed in that website are currently solvable without considerable simplification.

31. References • John McCarthy, Programs With Common Sense, 1959, Mechanisation of Thought Processes, Proceedings of the Teddington Conference on the Mechanization of Thought Processes, 77–84 • Patrick Hayes and Leora Morgenstern, On John McCarthy’s 80th Birthday, in Honor of His Contributions, 2007, AI Magazine 28(4): 93-102 • http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/CommonSense • http://openmind.media.mit.edu/ • http://www-formal.stanford.edu/leora/commonsense/