Symbolic Programming. Symbolic Programming. Symbolic Programs for Playing Chess. AI in Practice: Playing Chess. Artificial Intelligence Chapter 2

Transcription

1 Symbolic Programming Symbolic Programming Artificial Intelligence Chapter 2 z Create symbols and symbolic expressions: ˆ NB Ž SNB Ž NBJŠ Š Bˆ KN BBBJ B Ž SB Ž K z Pass them around as arguments to procedures. z Manipulate them to infer new expressions: J B Ž SB Ž KNBJ B Ž B Ž UK BBB BJ ƒ B Ž SB Ž B Ž UK Symbolic Programs for Playing Chess AI in Practice: Playing Chess z Symbols: chess pieces z Expressions: legal moves eg.: Žƒ O Š SNB Žƒ O Š J BJˆ ƒ BšBKBJ Š BšBSKK

2 2.1 Rule-Based Reactive System Example Obstacle Detection Sensors for a Mobile Robot z Symbols: sensors, controls z Sensors names ˆ ƒ NB Š NBŽ ˆ NBŒ Š NBŒŽ ˆ NB ƒ B ƒ NBƒ ƒ NBˆƒ Ÿ z Control parameters: speed, direction speed B{œ NB Ž NBˆƒ } turn B {Ž ˆ NB ƒ Š NB Š } 2.2 Introduction to Lisp z Programs and data are represented as lists, i.e. ( expressions ) z An expression is a Lisp object or a list of zero or more expressions. z Examples: JKNBJSKNBJSBJKK z Symbols: SVNB O NB NB ˆ NBhqqNBh NB NB Ž z A Lisp program consists of a sequence of Lisp expressions. e.g.: J ˆ B ƒ BJšKBJLBšBšBKK 2.3 Interacting with Lisp z Lisp interpreter: read-eval-print loop z ƒž and ƒ Ž `BD D D D `BJMBUBVBWK S `BJ ƒ BK V

3 2.4 Functions in Lisp z ˆ : function definition J ˆ BŠ BJƒB K BBB BJ BJMBJ ƒ BƒK BBBBBBBBB BBBBBJ ƒ B KKKK J ˆ B ƒ BJšK BBB BJLBšBšKK `BJ ƒ BUK [ `BJŠ BUBVK W Eval and Apply z Eval takes the list and extracts the first element of the list and the list of arguments. z Eval passes the function associated with the symbol and the list of argument to apply. z Apply uses eval to evaluate the list of arguments. z Apply extracts the function definition and check the number of arguments. z Apply sends the evaluated arguments to eval to be evaluated. Conditional Statements z J ˆBtest conditional alternativek J ˆ B š O O BJ K BB BBJ ˆBJ B K BBBB BBBBBJMB BSK BBBBBB BJMB BKKK `BJ š O O BSK U Conditional Statements: z J BclausesK, clauses := Jtest bodyk* J ˆ B BJ K BBBJ BJJ`_B BV[KBJ BDeƒ I B B Šƒ Š Š PDKB ŽK BBBBBBBBBBBBBJJ BJ_B BKBJ_B BUKBJ_B BWKKB K BBBBBBBBBBBBBJJ BJ_BJ B BKBRK BBBBBBBBBBBBBBBBBBBBJ_BJ B BUKBRK BBBBBBBBBBBBBBBBBBBBJ_BJ B BWKBRKKB ŽK BBBBBBBBBBBBBJ B KKK

4 Using Defined Functions `BJ BUK `BJ BUYK v `BJ BYXK eƒ I B B Šƒ BŠ ŠP Recursive Functions z Reduce the original problem to simpler problems, and then apply itself to solve the simpler problems. z Break the problem down again, and again, until the pieces of the problem can be solved easily. z Problem reduction z Divide-and-conquer Recursive Functions: An Example z Lisp Example J ˆ B ƒ BJšB K BBBBJ ˆBJ_B BRK BBBBBBBBBS BBBBBBBBJLBšBJ ƒ BšBJOB BSKKKKK `BJ ƒ BUBUK Y z Evaluating Functions in Files BBBB`BJŽ ƒ BD ƒ PŽ D) 2.5 Environments, Symbols, and Scope z Assigning values to symbols `BJ B BK `B J B BUK U `B U `BJ B B K BBBBU `B U

5 Eval 1. If x is a number or string, return x. 2. If x is a symbol, then look up its value in the environment. 3. If x a special form, handle it accordingly 4. If x a list, send the function (first item) and arguments (the rest of items) to ƒ Ž. Apply 1. Look up the definition of the function 2. Use ƒž to evaluate each argument in the environment. 3. Create a new environment. 4. Use ƒž to evaluate the definition in the new environment. Structured Environments z An environment allocates storage for symbolic values. z Global environments as a large table. z A new environment points to the existing parent environment. z o determine the value of a symbol, ƒž first looks in the table pointed by the environment. If no entry exists, then ƒž looks in the parent environment. Scope `BJ BšBK ` J ˆ BŽ ƒžbjšk B BJ BšBJMBšBSKK B B JLBšBšKK nqecn `BJŽ ƒžbjmbšbskk SX `Bš `BJŽ BJJšBSK BBBBBJŽ BJJšBKK BBBBBBBBJŽ BJJšBUKK BBBBBBBBBBBJ BšKK BBBBBBBBJ BšKK BBBBBJ BšKK US S

6 n : Introducing Local Variables Environments Created during Function Invocation J ˆ B BJK BBBJ BDi Bƒ B Bˆ BRB B[\BDK BBBJŽ BJJ BJ ƒ KKBJ BJ ƒ BSRKKK BBBBBJ BJJ`B B KBJ BDv Š ŠCDKK BBBBBBBBBBBBBBBJJ^B B KBJ BDv BŽ CDKK BBBBBBBBBBBBBBBJ BJ BDn B CDKKKKK iwguu `BJ K i Bƒ B Bˆ BRB B[\BU v BŽ C 2.6 More on Functions z Functions with Local State `BJŽ BJšBJ BSKK BBB BBBBJ ˆ B ƒ Žƒ BJœK BBBBBBBBB BBJ BšB KBJ B BœKBJLBšBšKKK uswctgncuv `BJ ƒ Žƒ BK S `BJ ƒ Žƒ BUK V Lambda Functions z ˆ : define named functions. `BJ ˆ B ƒ BJšKBJLBšBšKK z Žƒ ƒ: creates unnamed functions. `BJŽƒ ƒbjškbjlbšbškk z ˆ : evaluates lambda functions or a symbol defined as a function. `BJˆ BJŽƒ ƒbarguments bodykk `BJˆ B#'Žƒ ƒbarguments bodykk `BJˆ B ƒ K

7 Functions as Arguments and ˆ ƒžž 2.7 List Processing `BJˆ ƒžžbeijžƒ ƒbjškbjlbšbškkbuk [ ` J ˆ B ƒ BJšB BˆK BBBBBBJ ˆBJ`BJˆ ƒžžbˆbškbjˆ ƒžžbˆb KKB B ŽKK fgetgcukpir ` J ƒ BSBBEIJŽƒ ƒbjškbjlbšbšbkkk `BJ B ƒžbeijžƒ ƒbjškbjqbsbškkk #^k Oh BJncodfcBJzKBJQBSBzKK SRVddYW` `BJ ƒ BSBB ƒžk v J BexpressionK `BJ B K u{o `BI u{o IJˆ B B Š K JhktuvBugeqpfBvjktfK `BJŽ BSBBUBVK `BJŽ BSBJŽ BBJŽ BUKKK JSBJBJUKKK Building and Accessing Elements in Lists Lists in Memory `BJ Bˆ BJŽ BSBBU VKK `BJŽ BJˆ Bˆ K J Bˆ KBJ ŠB ˆ KK JSBBUK `BJ Bˆ K JBUBVK `BJ Bˆ BSK S `BJ B BJŽ BBU VKK JBUBVK `BJ Bˆ B K `BJ BšBJ BSBKK JSBPBBK ` J B BJ BS J BBJKKKK JSBK `BJ ƒ BšK S `BJ B K JK `BJ ƒ BJ B KK `BJ ƒ B K

8 Lists in Memory: Illustrations Modifying Existing List Structures `BJ BšBJŽ BSBJŽ BKKK JSBJKK `BJ ˆBJˆ BJˆ BJ BšKKKBSK S `Bš JSBJSKK Comparing List Structures: Band ƒž Built-In List Manipulation Functions `BJ B BIˆ K hqq `BJ B BIˆ K v `BJ B BJŽ BIˆ KK JhqqK ` J B BJŽ BIˆ KK `BJ B B K JhqqK `BJ B B K v `BJ ƒžb BJŽ BIˆ KK v `BJ BšBIJS KB BIJUBVKK JUBVK `BJƒ BšB K `Bš JSBK `B( BšB K ;; destructive `Bš `BJ BšK JVBUBBSK `BJ BWBJƒ BIJS BUKBIJVBWKBIJXBYBZKKK JWBXBYBZK

9 Optional Arguments `BJ BIJKBIJJSKBJKBJUKKK ;; eq is used `BJ BIJKBIJJSKBJKBJUKK \ B#I ƒžk JJKBJUKK `BJ BIJSBKBIJJRBVKBJSBUKK BBBBBBBBBB\ BEIŽƒ ƒbjšb KBJ BJˆ BšK BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBJˆ B KKKK JJSBUKK List-Processing Examples: Homeworks z (p. 49) z ƒ ŠB(p. 50) z ƒ O ƒ (p. 50) z and ƒ (p. 51) z Žƒ (p. 40) z Data Abstraction: vtggo (p ) 2.8 Iterative Constructs: ƒ ƒ z ƒ ƒ NB ƒ NB ƒ ƒ NB `BJ ƒ ƒ BEIMBIJSBBUKBIJVBWBXKK JWBYB[K `BJ ƒ ƒ BEIJŽƒ ƒbjšb K BBBBBBBBBBBBBBBBBBBBBBBJ ˆBJ`BšB KBšB KK BBBBBBBBBBBBBBBBBBIJBYBWKBIJSB[BVKK JB[BWK `BJ BEIMBIJSBBUKK X z ( B index-var-specs (end-test result) body) z index-var-specs := (step-var init-val step-val) `BJ BJJ BSBJMB BSKKBBBBBB ; index-var-specs BBBBBBBBBJŒBSBJLBŒB KKK BBBBBBBBJJ_B BSRKBŒKBBBBBBB B; (end-test result) BBBBBBBBJ B KKBBBBBBBBBB B; body SUVWXYZ[ UXZZR

10 Ž NB z ( Ž (var expr result) body) ; var is bound to elements of list expr z ( (var expr result) body) ; var is an integer resulting from expr `BJ Ž BJšBIJƒB B KKBJ BšKK cde `BJ BJ BSRB KBJ B KK RSUVWXYZ[ SR 2.9 Monitoring and Debugging Programs z racing the programs `BJ ƒ B ƒ B ƒ K JtckugBuswctgK `BJ ƒ BJ ƒ BBKK SBg BtckugBB žbbbbg BtckugBBS žbbppp žbbbbgš BtckugB SBBgš BtckugBV SBg BuswctgBV SBgš BuswctgBSX `BJ ƒ B ƒ K JtckugK Formatted Output `BJˆ ƒ B ŽBD fb ]B cb ŽDBSYBIˆ K DSYB ]BhqqB ŽD `BJˆ ƒ B ŽBD VNhB ƒždbspuvwk DSPUB ƒžd Jˆ ƒ B ŽBDj G BƒBŽ B ƒ PDK Dj BƒBŽ B ƒ PD 2.10 Rule-Based Reactive System Revisited z Homework `BJƒ BIJŽ ˆ Bˆƒ KBIJJJŽ ˆ Bˆƒ KBSKKK `BJƒ BIJŽ ˆ Bˆƒ KBIJJJŽ ˆ Bˆƒ KBSKKB\ BEI ƒžk JJŽ ˆ Bˆƒ KBSK `BJƒ BIŽ ˆ BIJJJŽ ˆ Bˆƒ KBSKKB\ BEIˆ K JJŽ ˆ Bˆƒ KBSK

11 Exercises z (switch '(a b)) (b a) z (switch '(a b c d) 2 4) (c b a d) z (factorial 3) 6 z (distance '(2 5) '(3 6)) 1.4 z (remove 'b '(a b c b d)) (a c d) z (intersect '(a b b c d) '(c a b b)) (a b c) Explain in detail what ƒž does when it encounters J B KBin the following Lisp session. J ˆ B BJšKBJLBšBšBšKK BBB J B BUK BBB J B K