1、Artificial Intelligence through Prolog by Neil C. Rowe Artificial Intelligence through Prolog by Neil C. RowePrentice-Hall, 1988, ISBN 0-13-048679-5Full text of book (without figures)Table of Contents Preface Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 C
2、hapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Appendix A Appendix B Appendix C Appendix D Appendix E Appendix F Appendix G Some figures in crude form Instructors Manual, containing additional answers and exercises Errata on the book as published http:/www.cs.nps.navy.mil/people/fa
3、culty/rowe/book/book.html 23/04/2002 17:38:27http:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.htmlTable of contents PrefaceAcknowledgementsTo the reader1. Introduction1.1 What artificial intelligence is about1.2 Understanding artificial intelligence1.3 Preview2. Representing facts2.1 Pr
4、edicates and predicate expressions2.2 Predicates indicating types2.3 About types2.4 Good naming2.5 Property predicates2.6 Predicates for relationships2.7 Semantic networks2.8 Getting facts from English descriptions2.9 Predicates with three or more arguments2.10 Probabilities2.11 How many facts do we
5、 need?3. Variables and querieshttp:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html (1 of 8) 23/04/2002 17:38:31http:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html3.1 Querying the facts3.2 Queries with one variable3.3 Multi-directional queries3.4 Matching alternatives3.5
6、Multi-condition queries3.6 Negative predicate expressions3.7 Some query examples3.8 Loading a database3.9 Backtracking3.10 A harder backtracking example: superbosses3.11 Backtracking with “not“s3.12 The generate-and-test scheme3.13 Backtracking with “or“s (*)3.14 Implementation of backtracking3.15 A
7、bout long examples4. Definitions and inferences4.1 Rules for definitions4.2 Rule and fact order4.3 Rules as programs4.4 Rules in natural language4.5 Rules without right sides4.6 Postponed binding4.7 Backtracking with rules4.8 Transitivity inferences4.9 Inheritance inferences4.10 Some implementation
8、problems for transitivity and inheritance4.11 A longer example: some traffic laws4.12 Running the traffic lights program4.13 Declarative programming5. Arithmetic and lists in Prolog5.1 Arithmetic comparisonshttp:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html (2 of 8) 23/04/2002 17:38:
9、31http:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html5.2 Arithmetic assignment5.3 Reversing the “is“5.4 Lists in Prolog5.5 Defining some list-processing predicates5.6 List-creating predicates5.7 Combining list predicates5.8 Redundancy in definitions5.9 An example: dejargonizing bureau
10、cratese (*)6. Control structures for rule-based systems6.1 Backward-chaining control structures6.2 Forward chaining6.3 A forward chaining example6.4 Hybrid control structures6.5 Order variants6.6 Partitioned control structures6.7 Meta-rules6.8 Decision lattices6.9 Concurrency in control structures6.
11、10 And-or-not lattices6.11 Randomness in control structures6.12 Grammars for interpreting languages (*)7. Implementation of rule-based systems7.1 Implementing backward chaining7.2 Implementing virtual facts in caching7.3 Input coding7.4 Output coding7.5 Intermediate predicates7.6 An example program7
12、.7 Running the example program7.8 Partitioned rule-based systems7.9 Implementing the rule-cycle hybridhttp:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html (3 of 8) 23/04/2002 17:38:31http:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html7.10 Implementing pure forward chaini
13、ng (*)7.11 Forward chaining with “not“s (*)7.12 General iteration with “forall“ and “doall“ (*)7.13 Input and output of forward chaining (*)7.14 Rule form conversions (*)7.15 Indexing of predicates (*)7.16 Implementing meta-rules (*)7.17 Implementing concurrency (*)7.18 Decision lattices: a compilat
14、ion of a rule-based system (*)7.19 Summary of the code described in the chapter (*)8. Representing uncertainty in rule-based systems8.1 Probabilities in rules8.2 Some rules with probabilities8.3 Combining evidence assuming statistical independence8.4 Prolog implementation of independence-assumption
15、“and-combination“8.5 Prolog implementation of independence-assumption “or-combination“8.6 The conservative approach8.7 The liberal approach and others8.8 Negation and probabilities8.9 An example: fixing televisions8.10 Graphical representation of probabilities in rule-based systems8.11 Getting proba
16、bilities from statistics8.12 Probabilities derived from others8.13 Subjective probabilities8.14 Maximum-entropy probabilities (*)8.15 Consistency (*)9. Search9.1 Changing worlds9.2 States9.3 Three examples9.4 Operatorshttp:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html (4 of 8) 23/04/
17、2002 17:38:31http:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html9.5 Search as graph traversal9.6 The simplest search strategies: depth-first and breadth-first9.7 Heuristics9.8 Evaluation functions9.9 Cost functions9.10 Optimal-path search9.11 A route-finding example9.12 Special cases
18、of search9.13 How hard is a search problem?9.14 Backward chaining versus forward chaining (*)9.15 Using probabilities in search (*)9.16 Another example: visual edge-finding as search (*)10. Implementing search10.1 Defining a simple search problem10.2 Defining a search problem with fact-list states10
19、.3 Implementing depth-first search10.4 A depth-first example10.5 Implementing breadth-first search10.6 Collecting all items that satisfy a predicate expression10.7 The cut predicate10.8 Iteration with the cut predicate (*)10.9 Implementing best-first search (*)10.10 Implementing A* search (*)10.11 I
20、mplementing search with heuristics (*)10.12 Compilation of search (*)11. Abstraction in search11.1 Means-ends analysis11.2 A simple example11.3 Partial state description11.4 Implementation of means-ends analysis11.5 A harder example: flashlight repairhttp:/www.cs.nps.navy.mil/people/faculty/rowe/boo
21、k/tableconts.html (5 of 8) 23/04/2002 17:38:31http:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html11.6 Running the flashlight program11.7 Means-ends versus other search methods11.8 Modeling real-word uncertainty (*)11.9 Procedural nets (*)12. Abstraction of facts12.1 Partitioning facts
22、12.2 Frames and slots12.3 Slots qualifying other slots12.4 Frames with components12.5 Frames as forms: memos12.6 Slot inheritance12.7 Part-kind inheritance12.8 Extensions versus intensions12.9 Procedural attachment12.10 Frames in Prolog12.11 Example of a frame lattice12.12 Expectations from slots12.
23、13 Frames for natural language understanding (*)12.14 Multiple inheritance (*)12.15 A multiple inheritance example: custom operating systems (*)13. Problems with many constraints13.1 Two examples13.2 Rearranging long queries without local variables13.3 Some mathematics13.4 Rearranging queries with l
24、ocal variables13.5 Rearranging queries based on dependencies13.6 Summary of guidelines for optimal query arrangements13.7 Rearrangement and improvement of the photo interpretation query13.8 Dependency-based backtracking13.9 Reasoning about possibilities13.10 Using relaxation for the photo interpreta
25、tion examplehttp:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html (6 of 8) 23/04/2002 17:38:31http:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html13.11 Quantifying the effect (*)13.12 Formalization of pure relaxation13.13 Another relaxation example: cryptarithmetic13.14 Im
26、plementation of pure relaxation (*)13.15 Running a cryptarithmetic relaxation (*)13.16 Implementing double relaxation (*)14. A more general logic programming14.1 Logical limitations of Prolog14.2 The logical (declarative) meaning of Prolog rules and facts14.3 Extending Prolog rules14.4 More about cl
27、ause form14.5 Resolution14.6 Resolution with variables14.7 Three important applications of resolution14.8 Resolution search strategies14.9 Implementing resolution without variables (*)15. Testing and debugging of artificial intelligence programs15.1 The gold standard15.2 Cases15.3 Focusing on bugs15
28、.4 Exploiting pairs of similar cases15.5 Composite results15.6 Numbers in comparisons15.7 Preventive measures15.8 Supporting intuitive debugging15.9 Evaluating cooperativeness15.10 On problems unsuitable for artificial intelligencehttp:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html (7
29、 of 8) 23/04/2002 17:38:31http:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.htmlAppendix A: basics of logicAppendix B: Basics of recursionAppendix C: Basics of data structuresAppendix D: summary of the Prolog dialect used in this bookD.1 Managing facts and rulesD.2 The format of facts, r
30、ules and queriesD.3. Program layoutD.4. ListsD.5. NumbersD.6. Output and inputD.7. StringsD.8. Treating rules and facts as dataD.9. Miscellaneous predicatesD.10. Definable predicatesD.11. DebuggingAppendix E: Using this book with Micro-PrologAppendix F: For further readingAppendix G: Answers to sele
31、cted exerciseshttp:/www.cs.nps.navy.mil/people/faculty/rowe/book/tableconts.html (8 of 8) 23/04/2002 17:38:31http:/www.cs.nps.navy.mil/people/faculty/rowe/book/preface.htmlPreface Artificial intelligence is a hard subject to learn. I have written a book to make it easier. I explain difficult concept
32、s in a simple, concrete way. I have organized the material in a new and (I feel) clearer way, a way in which the chapters are in a logical sequence and not just unrelated topics. I believe that with this book, readers can learn the key concepts of artificial intelligence faster and better than with
33、other books. This book is intended for all first courses in artificial intelligence at the undergraduate or graduate level, requiring background of only a few computer science courses. It can also be used on ones own. Students often complain that while they understand the terminology of artificial i
34、ntelligence, they dont have a gut feeling for whats going on or how you apply the concepts to a situation. One cause is the complexity of artificial intelligence. Another is the unnecessary baggage, like overly formal logical calculi, that some books and teachers saddle students with. But an equally
35、 important cause is the often poor connection made between abstract concepts and their use. So I considered it essential to integrate practical programming examples into this book, in the style of programming language and data structures books. (I stress practical, not missionaries and cannibals, de
36、finitions of “grandfather“, or rules for identifying animals in zoos-at least rarely.) This book has about 500 chunks of code. Clear, concrete formalization of artificial intelligence ideas by programs and program fragments is all the more critical today with commercialization and media discovery of
37、 the field, which has caused a good deal of throwing around of artificial intelligence terms by people who dont understand them. But artificial intelligence is a tool for complex problems, and its program examples can easily be forbiddingly complicated. Books attempting to explain artificial intelli
38、gence with examples from the programming language Lisp have repeatedly demonstrated this. But I have come to see that the fault lies more with Lisp than with artificial intelligence. Lisp has been the primary language of artificial intelligence for many years, but it is a low-level language, too low
39、 for most students. Designed in the early 1960s, Lisp reflects the then-primitive understanding of good programming, and requires the programmer to worry considerably about actual memory references (pointers). Furthermore, Lisp has a weird, hard-to-read syntax unlike that of any other programming la
40、nguage. To make matters worse, the widespread adoption of Common Lisp as a de facto standard has discouraged research on improved Lisps. Fortunately there is an alternative: Prolog. Developed in Europe in the 1970s, the language Prolog has steadily gained enthusiastic converts, bolstered by its surp
41、rise choice as the initial language of the Japanese Fifth Generation Computer project. Prolog has three positive features that give it key advantages over Lisp. First, Prolog syntax and semantics are much closer to formal logic, the most common way of representing facts and reasoning methods used in
42、 the artificial intelligence literature. Second, Prolog provides automatic backtracking, a feature making for considerably easier “search“, the most central of all artificial intelligence techniques. Third, Prolog supports multidirectional (or multiuse) reasoning, in which arguments to a procedure can freely be designated inputs and outputs in different http:/www.cs.nps.navy.mil/people/faculty/rowe/book/preface.html (1 of 5) 23/04/2002 17:38:32
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