The Impact of John McCarthy on the Field of Artificial Intelligence

1. The Impact of John McCarthy on the Field ofArtificial Intelligence Peter Tonner

2. Outline • Introduction • Context and Other Important Researchers • Programs with Common Sense • Recursive Functions • Lisp • Philosophical Problems from the Standpoint of Artificial Intelligence • Epistemological Problems of Artificial Intelligence • Epistemology and Situation Calculus • Circumscription

3. John McCarthy • 1955 Develop the phrase “Artificial Intelligence” • 1960 First LISP implementation • 1971 Turing Award Recipient • 2001 Professor Emeritus of Computer Science at Stanford University

4. Turing Award History • Prize value • Original Prize: $25,000 • Intel Sponsorship (2002): $100,000 • Google Sponsorship (2007): $250,000

5. Context of AI Research circa 1970 • Turing Test - Alan Turing • General Problem Solver - Allen Newell and Herbert Simon

6. Computing Machinery and Intelligence- Alan Turing • Turing Test • Define Intelligence as: • Actions that are indistinguishable from known intelligent beings, i.e. humans • Side steps philosophical arguments • Turing predicts intelligent computing in 50 years (1950)

7. A Guide to the General Problem Solver -Newell and Simon (1957) • General mechanisms utilized in problem solving • Built off the Logic Theory Machine • Designed to cover • theorem proving • geometric problems • chess playing • Suffered from combinatorial explosion • Led to the program SOAR

8. McCarthy’s Perspective “I think the best hope for human-level AI is logical AI, based on the formalizing of commonsense knowledge and reasoning in mathematical logic.” – John McCarthy • Problems in AI • Knowledge Representation • Problem Solving • Solutions from Philosophy • Hierarchy of data • Generalization of solution finding

9. Programs with Common Sense (1959) • “ 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” – John McCarthy • Outlines the Advice Taker • Undertake and solve problems on level of a human • First major AI paper for McCarthy • Introduction to Predicate (Situational) Calculus

10. What is an Intelligent System? • Core Features of Intelligent Systems: • All behaviors represented by the system, can create arbitrary automata or program in a language • Interesting changes in behavior must be expressible in a simple way • Nearly all aspects of behavior must be improvable, even the improving mechanism itself • There must be a concept of partial success and failure rather than a binary representation • Create subroutines which form new base units

11. Giving Instructions • Computer • Imperative sentences • Human or the Advice Taker • Declarative and Imperative Sentences • Imperative sentences • “Punch the keys for god’s sake!” • Declarative sentences • “You’re the man now dog.”

12. Advantages of Instruction Types • Imperative Sentences: • A procedure of imperatives is carried out faster • No previous knowledge of the machine needs to be analyzed • Declarative Sentences: • Use of previous knowledge is acceptable • Order is not as important as in imperative sentences, can have afterthoughts • Previous state is less important so allows for less need of instructor to know previous state

13. Immediate Deduction Routine • Given a set of premises and finds the set of immediate conclusions • Not heuristic based, entirely logical • Intelligent behavior -> proper use of the IDR • EX: • at(I, desk) • at(desk,universe) • at(x,y), at(y,z) -> at(x,y) • -------------------------------- • at(I, universe)

14. Example want(have(I, icecream)) have(I,price_of(icecream)) ->can(buy(I, icecream)) at(I,home) -> can(go(home, store, walking) ------- do(go(home, store, walking)) do(buy(I,icecream)) do(go(store, home, walking)) do(devour(I, icecream))

15. Sources of Propositions • Actions are guided by goals and desires • EX: want(at(I,x)) • want(have(I, icecream)) • System starts with known general solutions • EX: want(at(I,x)) -> do(observe_location(I)) • Machine memory stores past solution strategies • Must be reasonably general to allow for wide problem solving and constitute common sense want(have(I,x)) -> do(go(current_location(),store,walking)

16. Problem Generalization • want(at(I,x)) • general goal proposition • this should point to previously successful solutions • determine possible transportation solutions • walking • driving • flying • new generalizations must be formed after completing tasks • these would then be stored for later usage

17. Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I (1960) • LISP programming language • LISt Processor • List structure for data • Garbage Collection

18. Goals of LISP • Writing a compiler to compile LISP programs into machine language. • Writing a program to check proofs in a class of formal logical systems. • Writing programs for formal differentiation and integration. • Writing programs to realize various algorithms for generating proofs in predicate calculus. • Making certain engineering calculations whose results are formulas rather than numbers. • Programming the Advice Taker system.

19. Data Representation • Word := atomic data • address and decrement • Cycles not permitted • value CAN be linked multiple times

20. More on Data Representation • Figure A) ((A.B)(C(E.F)) • Figure B) (A,(B,C),D) • Ends of list contain NIL

21. Advantages of Lists • Solves the problem of dynamic memory usage • does not need fixed lengths at compile time • Registers that are not needed can be recycled to the free memory list • blocks of memory that are freed will have non uniform size and are harder to allocate • A sub-expression of many expressions need only be stored once

22. Free Storage List • contains all registers not filled with values • special word FREE points to the top of this list • adding new data • take the node stored in FREE • set FREE equal to next node • population and repopulation of this list is done automatically

23. Garbage Collection through the Free Storage List • Garbage collection routine will search the tree of accessible nodes • Nodes that are accessed have their address set to negative • Any node not set this way is collected

24. LISP Machines • Built specifically for LISP • Bypass the restrictions of existing hardware • Manufactured by Symbolicsand Lisp Machines, inc. • both spinoffs from the MIT AI lab

25. Philosophical Problems from the Standpoint of Artificial Intelligence (1969) • Analyzes the link between philosophical and AI problems • questions of knowledge representation • Provides a system for representing the known universe • Presents a formal language for task completion

26. What is an Intelligent System? Part Deux • An intelligent agent : • has an adequate model of the world • can answer questions from this model • can gather information from external sources • can perform tasks in the world as permitted physically and guided by its goals

27. Representations of the World • Metaphysically Adequate: • No contradictions of known laws • EX: the world as a collection of particles • EX: the world as a giant quantum-mechanical wave function • Epistemologically Adequate: • No contradictions to an entity’s perspective • Common Language + Mathematical Language + …

28. Epistemology and Situation Calculus • Situations • Fluents • Causality • Actions • Strategies • Knowledge and Ability

29. Situations • Complete state of the universe at an instant of time • Represented as “facts” • these can be used to deduce a cascade of more facts • Represents actual and theoretical situations • Sit: the set of all situations

30. Fluent • Domain: Sit • Range • [True, False] : Propositional Fluent • Sit: Situational Fluent • Provides a conditional to analyze state of situation • Allows a program to query the state of a situation • EX • time(s) • in(x,y,s) • has(x,y,s) • at(p,x,s) AND raining(x,s)

31. Causality • Special fluent to represent cause and effect • F(N,s) := the situation s will be followed at some point in time a situation that satisfies the fluent N • Physical Law of Gravity: • h = h0 + v0 * (t – t0 ) - .5g * (t – t0 )2 • Epistemological Law of Gravity falling(b) AND t > 0 AND t > 0 AND h = height(b) AND v = velocity(b) AND h + v*t - .5gt 2 > 0 AND time = t’ -> F(time = t + t’ AND falling(b) AND height = h + vt - .5gt 2 )

32. Actions and Strategies • Actions : unit of change that creates a new situation • Strategy • combination of actions • introduces general programming techniques • loops • flow control

33. Knowledge and Ability idea-of-combination(p,sf,s) == combination(sf)? • What does it mean to know the value of a safe? • One value, two Objects • Critical for ability reasoning

34. The Frame Problem • EX • An agent decides to use a phone to call a friend • Assumption: the phone is still present after looking up the number • After and actions • must assert that fluents of a situation have not changed • With n actions and m fluents • n*m possible assertions • Solution: Introduce a Frame • fluents are attached to frame • actions are modified to update all fluents that it changes

35. Epistemological Problems of Artificial Intelligence (1977) • Further extends the concepts of epistemology • Not concerned with the design of heuristics for search spaces • New concept: Circumscription

36. Circumscription • Problem- • “Not all human reasoning can be translated into deduction in some formal system of mathematical logic” • Circumscription • the missing mode of deduction • things are expected unless otherwise specified • creates a list of conjectures that may not be universally true • provide a framework for common sense reasoning

37. More on Circumscription • EX: Missionaries and Cannibals: • Boat that fits two people • 3 missionaries and 3 cannibals • number of cannibals must never be larger than the number of missionaries • Without Circumscription: • are there oars with the boat? • is there a bridge a few miles down? • etc • Circumscription • assumes all needed information is given • if there were significant issues with the boat, they would be given

38. Open Problems • Converting commonsense into a science • Precise specification needed to model the universe • theories of the universe are still being developed • forced to use best fit theories • Game of Life example: • design the game of life with the ability to reason • would it be able to determine it’s own physical configuration is the same as the game of life?

39. Later work of John McCarthy • Continued formalization of common sense and circumscription • Later papers cover topics in free will of robots with intelligence • The robot and the baby • science fiction short story • proposes questions about whether designed intelligent machines can have emotions

40. References • Programs with Common Sense (1959) • Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I (1960) • Philosophical Problems from the Standpoint of Artificial Intelligence (1969) • Epistemological Problems of Artificial Intelligence (1977)

41. Other Good Sources • McCarthy’s website • www-formal.stanford.edu/jmc/ • Douglas Hofstadter • Gödel, Escher, Bach: An Eternal Golden Braid • LISP • Common Lisp Open Code Collection • http://clocc.sourceforge.net/ • Practical Common Lisp