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Prolog Programming for Artificial Intelligence
3rd Edition
Ivan Bratko
ISBN-13: 978-0-20140-375-6
ISBN-10: 0-20140-375-7
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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
 

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
 

File shell.txt  

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  
 

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.txt  

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
 

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
     
  All chapters  
  List of codes and figures  
  Prolog code for all chapters   (zip file)


 
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