《Prolog Programming for Artificial Intelligence》附书源码

Some frequently used predicates: file frequent.pl CHAPTER 1 Figure 1.8 The family program. CHAPTER 2 Figure 2.14 A program for the monkey and banana problem. Figure 2.16 Four versions of the predecessor program. CHAPTER 4 Figure 4.5 A flight route planner and an example flight timetable. Figure 4.7 Program 1 for the eight queens problem. Figure 4.9 Program 2 for the eight queens problem. Figure 4.11 Program 3 for the eight queens problem. CHAPTER 7 Figure 7.2 A program for cryptoarithmetic puzzles. Figure 7.3 A procedure for substituting a subterm of a term by another subterm. Figure 7.4 An implementation of the findall relation. CHAPTER 9 Figure 9.2 Quicksort. Figure 9.3 A more efficient implementation of quicksort using difference-pair representation for lists. Figure 9.7 Finding an item X in a binary dictionary. Figure 9.10 Inserting an item as a leaf into the binary dictionary. Figure 9.13 Deleting from the binary dictionary. Figure 9.15 Insertion into the binary dictionary at any level of the tree. Figure 9.17 Displaying a binary tree. Figure 9.20 Finding an acyclic path, Path, from A to Z in Graph. Figure 9.21 Path-finding in a graph: Path is an acyclic path with cost Cost from A to Z in Graph. Figure 9.22 Finding a spanning tree of a graph: an `algorithmic' program. Figure 9.23 Finding a spanning tree of a graph: a `declarative' program. CHAPTER 10 Figure 10.6 Inserting and deleting in the 2-3 dictionary. Figure 10.7 A program to display a 2-3 dictionary. Figure 10.10 AVL-dictionary insertion. CHAPTER 11 Figure 11.7 A depth-first search program that avoids cycling. Figure 11.8 A depth-limited, depth-first search program. Figure 11.10 An implementation of breadth-first search. Figure 11.11 A more efficient program than that of Figure 11.10 for the breadth-first search. CHAPTER 12 Figure 12.3 A best-first search program. Figure 12.6 Problem-specific procedures for the eight puzzle, to be used in best-first search of Figure 12.3. Figure 12.9 Problem-specific relations for the task-scheduling problem. Figure 12.10 An implementation of the IDA* algorithm. Figure 12.13 A best-first search program that only requires space linear in the depth of search (RBFS algorithm). CHAPTER 13 Figure 13.8: Depth-first search for AND/OR graphs. Figure 13.12 Best-first AND/OR search program. CHAPTER 14 Figure 14.3 Scheduling with precedence constraints and no resource constraints. Figure 14.4 A CLP(R) scheduling program for problems with precedence and resource constraints. Figure 14.6 Constraints for some electrical components and connections. Figure 14.7 Two electrical circuits. Figure 14.8 A cryptarithmetic puzzle in CLP(FD). Figure 14.9 A CLP(FD) program for eight queens. CHAPTER 15 Figure 15.6 A backward chaining interpreter for if-then rules. Figure 15.7 A forward chaining rule interpreter. Figure 15.8 Generating proof trees. Figure 15.9 An interpreter for rules with certainties. Figure 15.11 An interpreter for belief networks. Figure 15.12 A specification of the belief network of Fig. 15.10 as expected by the program of Fig. 15.11. Figure 15.14 Some frames. CHAPTER 16 Figure 16.1 A simple knowledge base for identifying animals. Figure 16.3 A knowledge base for identifying faults in an electric network. Figures 16.6, 16.7, 16.8, 16.9 combined, with small modifications, into file shell.pl: (an expert system shell) CHAPTER 17 Figure 17.2 A definition of the planning space for the blocks world. Figure 17.3 A definition of the planning space for manipulating camera. Figure 17.5 A simple means-ends planner. Figure 17.6 A means-ends planner with goal protection. Figure 17.8 A planner based on goal regression. Figure 17.9 A state-space definition for means-ends planning based on goal regression. CHAPTER 18 Figure 18.9 Attribute definitions and examples for learning to recognize objects from their silhouettes (from Figure 18.8). Figure 18.11 A program that induces if-then rules. File learn_tree.pl: Induction of decision trees (program sketched on pages 466-468) File prune_tree.pl: Solution to Exercise 18.6 CHAPTER 19 Figure 19.1 A definition of the problem of learning predicate has_daughter. Figure 19.3 A loop-avoiding interpreter for hypotheses. Figure 19.4 MINIHYPER - a simple ILP program. Figure 19.5 Problem definition for learning list membership. Figure 19.7 The HYPER program. The procedure prove/3 is as in Figure 19.3. Figure 19.8 Learning about odd-length and even-length simultaneously. Figure 19.9 Learning about a path in a graph. Figure 19.10 Learning insertion sort. Figure 19.12 Learning the concept of arch. CHAPTER 20 Figure 20.3 Qualitative modelling program for simple circuits. Figure 20.8 A simulation program for qualitative differential equations. Figure 20.9 A qualitative model of bath tub. Figure 20.11 A qualitative model of the circuit in Figure 20.10. Figure 20.14 A qualitative model of the block-spring system. File energy.pl: An oscillator model with energy constraint (alternative to one in Fig. 20.14). CHAPTER 21 Figure 17.6 A DCG handling the syntax and meaning of a small subset of natural language. CHAPTER 22 Figure 22.2 A game tree translated to Prolog. Figure 22.3 A straightforward implementation of the minimax principle. Figure 22.5 An implementation of the alpha-beta algorithm. Figures 22.6, 22.7, 22.10 combined into single file chess.pl. CHAPTER 23 Figure 23.1 The basic Prolog meta-interpreter. Figure 23.2 A Prolog meta-interpreter for tracing programs in pure Prolog. Figure 23.3 Two problem definitions for explanation-based generalization. Figure 23.4 Explanation-based generalization. Figure 23.5 A simple interpreter for object-oriented programs. Figure 23.6 An object-oriented program about geometric figures. Figure 23.8 An object-oriented program about a robot world. Figure 23.12 A pattern-directed program to find the greatest common divisor of a set of numbers. Figure 23.13 A small interpreter for pattern-directed programs. Figure 23.15 A pattern-directed program for simple resolution theorem proving. Figure 23.16 Translating a propositional calculus formula into a set of (asserted) clauses.