| Foreword |
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xiii | |
| Preface |
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xvii | |
| PART I The Prolog Language |
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1 | (236) |
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3 | (22) |
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Defining relations by facts |
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3 | (5) |
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Defining relations by rules |
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8 | (6) |
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14 | (4) |
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How Prolog answers questions |
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18 | (5) |
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Declarative and procedural meaning of programs |
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23 | (2) |
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Syntax and Meaning of Prolog Programs |
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25 | (36) |
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26 | (7) |
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33 | (5) |
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Declarative meaning of Prolog programs |
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38 | (3) |
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41 | (5) |
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Example: monkey and banana |
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46 | (4) |
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Order of clauses and goals |
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50 | (7) |
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The relation between Prolog and logic |
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57 | (4) |
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Lists, Operators, Arithmetic |
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61 | (27) |
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61 | (3) |
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64 | (10) |
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74 | (6) |
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80 | (8) |
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Using Structures: Example Programs |
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88 | (26) |
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Retrieving structured information from a database |
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88 | (4) |
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92 | (2) |
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Simulating a non-deterministic automation |
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94 | (4) |
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98 | (5) |
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103 | (11) |
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114 | (18) |
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114 | (5) |
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119 | (5) |
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124 | (3) |
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Problems with cut and negation |
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127 | (5) |
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132 | (15) |
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132 | (3) |
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Processing files of terms |
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135 | (5) |
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140 | (1) |
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Constructing and decomposing atoms |
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141 | (3) |
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144 | (3) |
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147 | (24) |
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Testing the type of terms |
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147 | (8) |
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Constructing and decomposing terms: = .., functor, arg, name |
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155 | (5) |
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Various kinds of equality and comparison |
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160 | (1) |
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161 | (5) |
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166 | (1) |
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bagof, setof, and findall |
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167 | (4) |
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Programming Style and Technique |
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171 | (26) |
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General principles of good programming |
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171 | (2) |
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How to think about Prolog programs |
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173 | (3) |
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176 | (3) |
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179 | (2) |
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181 | (16) |
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Operations on Data Structures |
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197 | (27) |
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197 | (5) |
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Representing sets by binary trees |
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202 | (6) |
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Insertion and deletion in binary dictionary |
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208 | (5) |
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213 | (2) |
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215 | (9) |
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Advanced Tree Representations |
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224 | (13) |
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224 | (7) |
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AVL-tree: an approximately balanced tree |
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231 | (6) |
| PART II Prolog in Artificial Intelligence |
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237 | (410) |
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Basic Problem-Solving Strategies |
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239 | (21) |
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Introductory concepts and examples |
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239 | (5) |
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Depth-first search and iterative deepening |
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244 | (6) |
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250 | (5) |
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Analysis of basic search techniques |
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255 | (5) |
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Best-First Heuristic Search |
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260 | (32) |
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260 | (10) |
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Best-first search applied to the eight puzzle |
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270 | (4) |
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Best-first search applied to scheduling |
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274 | (6) |
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Space-saving techniques for best-first search |
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280 | (12) |
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Problem Decomposition and AND/OR Graphs |
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292 | (27) |
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AND/OR graph representation of problems |
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292 | (4) |
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Examples of AND/OR representation |
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296 | (4) |
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Basic AND/OR search procedures |
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300 | (5) |
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305 | (14) |
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Constraint Logic Programming |
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319 | (28) |
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Constraint satisfaction and logic programming |
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319 | (5) |
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CLP over real numbers: CLP(R) |
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324 | (5) |
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329 | (7) |
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A simulation program with constraints |
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336 | (5) |
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CLP over finite domains: CLP(FD) |
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341 | (6) |
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Knowledge Representation and Expert Systems |
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347 | (36) |
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Functions and structure on an expert system |
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347 | (2) |
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Representing knowledge with it-then rules |
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349 | (3) |
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Forward and backward chaining in rule-based systems |
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352 | (6) |
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358 | (2) |
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360 | (3) |
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363 | (9) |
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Semantic networks and frames |
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372 | (11) |
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383 | (30) |
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Knowledge representation format |
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383 | (5) |
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Designing the inference engine |
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388 | (4) |
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392 | (18) |
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410 | (3) |
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413 | (29) |
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413 | (5) |
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Deriving plans by means-ends analysis |
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418 | (4) |
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422 | (2) |
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Procedural aspects and breadth-first regime |
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424 | (3) |
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427 | (3) |
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Combining means-ends planning with best-first heuristic |
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430 | (4) |
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Uninstantiated actions and partial-order planning |
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434 | (8) |
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442 | (42) |
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442 | (1) |
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The Problem of learning concepts from examples |
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443 | (5) |
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Learning relational descriptions: a detailed examples |
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448 | (6) |
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Learning simple if-then rules |
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454 | (8) |
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Induction of decision trees |
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462 | (7) |
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Learning from noisy data and tree pruning |
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469 | (7) |
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476 | (8) |
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Inductive Logic Programming |
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484 | (36) |
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484 | (3) |
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Constructing Prolog programs from examples |
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487 | (13) |
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500 | (20) |
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520 | (35) |
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Common sense, qualitative reasoning and naive physics |
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520 | (5) |
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Qualitative reasoning about static systems |
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525 | (4) |
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Qualitative reasoning about dynamic systems |
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529 | (7) |
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A qualitative simulation program |
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536 | (11) |
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Discussion of the qualitative simulation program |
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547 | (8) |
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Languages Processing with Grammar Rules |
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555 | (26) |
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555 | (8) |
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563 | (5) |
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Defining the meaning of natural language |
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568 | (13) |
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581 | (31) |
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Two-person, perfect-information games |
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581 | (2) |
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583 | (3) |
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The alpha-beta algorithm; an efficient implementation of minimax |
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586 | (4) |
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Minimax-based programs: refinements and limitations |
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590 | (2) |
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Pattern knowledge and the mechanism of `advice' |
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592 | (4) |
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A chess endgame program in Advice Language 0 |
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596 | (16) |
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612 | (35) |
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Meta-programs and meta-interpreters |
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612 | (1) |
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613 | (5) |
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Explanation-based generalization |
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618 | (6) |
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Object-oriented programming |
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624 | (7) |
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Pattern-directed programming |
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631 | (7) |
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A simple theorem prove as a pattern-directed program |
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638 | (9) |
| Appendix A: Some Differences Between Prolog Implementations |
|
647 | (2) |
| Appendix B: Some Frequently Used Predicates |
|
649 | (3) |
| Solutions to Selected Exercises |
|
652 | (19) |
| Index |
|
671 | |
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