1. The Impact of John McCarthy on the Field of Artificial 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 Symbolics and 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 = h 0 + v 0 * (t – t 0 ) -.5g * (t – t 0 ) 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