                     Support  FAQ Technical support E-mail us feedback       Search catalogue   Browse catalogue Advanced search      Prolog Programming for Artificial Intelligence 3rd Edition Ivan Bratko ISBN-13: 978-0-20140-375-6 ISBN-10: 0-20140-375-7 Learn more about this title. To download the resources for this title, right-click on the file names below and save them to your hard disk. For further support, refer to the links in the left-hand menu.  Prolog code Format: Text .txt

 readme.txt Chapter 01 Figure 1.8 The family program Chapter 02 Figure 2.14 A program for the monkey and banana problem Figure 2.16 Four versions of the predecessor program Chapter 04 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 07 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 09 Figure 9.2 Quicksort 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 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 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 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 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 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 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 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 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 Induction of decision trees (program sketched on pages 466-468) File prune_tree.txt 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.txt An oscillator model with energy constraint (alternative to one in Fig. 20.14) Chapter 21 Figure 21.6 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 File chess.txt Figures 22.6, 22.7, 22.10 combined into single file 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 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 All chapters List of codes and figures (zip file)  