The Impact of John McCarthy on the Field of Artificial Intelligence

The Impact of John McCarthy on the Field ofArtificial Intelligence

Peter Tonner

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

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

Turing Award History

• Prize value– Original Prize: $25,000– Intel Sponsorship (2002): $100,000– Google Sponsorship (2007): $250,000

Context of AI Research circa 1970

• Turing Test - Alan Turing

• General Problem Solver - Allen Newell and Herbert Simon

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)

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

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

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

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

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.”

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

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)

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))

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 sensewant(have(I,x)) -> do(go(current_location(),store,walking)

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

Recursive Functions of Symbolic Expressions and Their Computation by Machine, Part I (1960)

• LISP programming language• LISt Processor• List structure for data• Garbage Collection

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.

Data Representation

• Word := atomic data– address and

decrement• Cycles not permitted– value CAN be linked

multiple times

More on Data Representation

• Figure A) ((A.B)(C(E.F))• Figure B) (A,(B,C),D)• Ends of list contain NIL

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

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

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

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

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

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

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 + …

Epistemology and Situation Calculus

• Situations• Fluents• Causality• Actions• Strategies• Knowledge and Ability

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

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)

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 )

Actions and Strategies

• Actions : unit of change that creates a new situation

• Strategy– combination of actions– introduces general programming techniques• loops• flow control

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

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

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

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

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

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?

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

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)

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