Practical AI Programming with Prolog

Cover Material, Copyright, and License

Preface

1. Who This Book Is For
2. How To Read This Book
3. Open Source Example Programs and Manuscript Files
4. Acknowledgments

Setting Up Your Prolog Development Environment

1. Installing SWI-Prolog
2. Editor Support
3. The SWI-Prolog Interactive Top Level
4. Installing Packs (Libraries)
5. Cloning the Book’s GitHub Repository
6. Project Organization and Best Practices

Prolog Tutorial

1. Facts, Rules, and Queries
2. Unification and Pattern Matching
3. Backtracking and Search
4. Lists and Recursive Data Structures
5. Arithmetic and Comparison
6. Input and Output
7. Modules and Code Organization
8. Definite Clause Grammars (DCGs) — A First Look
9. Common SWI-Prolog Built-in Predicates

Search Algorithms in Prolog

1. Loading Graph Data from a File
2. Depth-First and Breadth-First Search
3. Iterative Deepening
4. A* Heuristic Search
5. State-Space Search and Puzzle Solving
6. Constraint-Based Search: The N-Queens Problem

Natural Language Processing with Definite Clause Grammars

1. DCG Fundamentals
2. Tokenizing and Preprocessing Text
3. Parsing Natural Language Sentences
4. Semantic Analysis with DCGs
5. Named Entity Recognition
6. Text Categorization
7. Text Summarization

Reasoning and Inference

1. Propositional and First-Order Logic in Prolog
2. Forward and Backward Chaining
3. Generating and Visualizing Proof Trees
4. Non-Monotonic Reasoning and Defaults
5. Case Study: A Medical Diagnosis Reasoner

Expert Systems and Rule-Based AI

1. What Is an Expert System?
2. Building an Expert System Shell in Prolog
3. Knowledge Acquisition and Rule Representation
4. Explanation Facilities
5. Case Study: A Wine Selection Advisor
6. Case Study: A Fault Diagnosis System

Explainable AI and Computational Law with s(CASP)

1. What is s(CASP)?
2. Implementing Compliance Rules
3. Running the Compliance Check
4. Key Design Decisions

Constraint Logic Programming

1. Introduction to CLP
2. CLP(FD): Constraints over Finite Domains
3. Solving Sudoku with CLP(FD)
4. The N-Queens Problem
5. Scheduling and Resource Allocation
6. CLP(R) and CLP(Q): Constraints over Reals and Rationals
7. CLP(B): Boolean Constraints

Probabilistic Logic Programming

1. Why Probabilistic Reasoning?
2. ProbLog and Probabilistic Facts
3. Learning Probabilities from Data
4. Bayesian Networks in Prolog
5. Practical Applications

Probability

1. Words of Warning
2. Glossary of Terms
3. A SWI-Prolog Library to Explore Probability
4. Walking Through the Bayesian Code
5. The Correlation Module
6. Frequentists vs. Bayesians
7. Experimenting with Frequentist Methods
8. Prolog-Specific Design Decisions
9. Wrap Up

Anomaly Detection

1. The Gaussian Approach
2. The Training Pipeline
3. Module Structure
4. Loading and Subsampling
5. Preprocessing
6. Data Splitting
7. Computing Statistics
8. The Gaussian PDF
9. Epsilon Search
10. Putting It Together
11. Evaluation
12. Running the Example
13. Prolog-Specific Design Decisions
14. Wrap Up

Knowledge Graphs and Knowledge Representation

1. Representing Knowledge in Prolog
2. Building a Knowledge Graph
3. Multi-Hop Reasoning Over Knowledge Graphs
4. Generating RDF and Neo4j Cypher Data from Prolog
5. Integrating with DBpedia and Wikidata

Semantic Web Tools

1. Loading and Querying RDF Data
2. Querying Remote SPARQL Endpoints
3. RDFS and OWL Reasoning
4. Practical Applications

Web Clients in Prolog

1. HTTP GET and POST Requests
2. Working with JSON
3. Web Scraping
4. Practical Applications

Client-Side Prolog with WebAssembly

1. Architecture of a WASM Prolog Application
2. Recommender System Logic
3. JavaScript Integration
4. Running the Application Locally
5. Key Design Decisions

LLM Integration

1. Calling LLM APIs from Prolog
2. Structured Output from LLMs
3. Combining LLMs with Prolog Reasoning

Cache Engine

1. Design Overview
2. Implementation
3. Usage Examples
4. Key Design Decisions
5. Practical Applications

LLM Logic Guardrails

1. The Neuro-Symbolic Guardrail Pattern
2. Prolog Guardrail Rules
3. Python Verification Harness
4. Running the Verification Script
5. Key Design Decisions

Daily Use REPL: Gemini with Search and Cache

1. Design Overview
2. Keyword Extraction
3. Cache Context Builder
4. Gemini API Integration
5. The REPL Loop
6. Running the REPL
7. Wrap Up

The Janus Python Bridge

1. Setting Up Janus
2. Calling Python from Prolog
3. Hybrid AI Pipelines
4. Calling Prolog from Python
5. Practical Applications

Building AI Agents with Prolog

1. What Is an AI Agent?
2. A Simple Reactive Agent
3. Goal-Directed Agents
4. Tool-Using Agents with LLM Integration
5. Multi-Agent Communication
6. Case Study: A Research Assistant Agent

Agent Behavior Trees

1. Behavior Tree Node Types
2. Implementing the BT Engine in Prolog
3. Defining a Robot Agent
4. Simulating the Agent
5. Key Design Decisions

Meta-Interpreters: Prolog Reasoning About Prolog

1. The Vanilla Meta-Interpreter
2. Adding Proof Trees
3. Bounded Reasoning
4. Reasoning with Uncertainty
5. Custom Search Strategies
6. Debugging and Tracing Meta-Interpreters
7. Summary

Planning and Scheduling

1. Classical Planning in Prolog
2. The Blocks World
3. Planning with Constraints
4. Partial-Order Planning
5. Practical Job Scheduling Applications

Inductive Logic Programming with Popper

1. How Popper Works
2. The Grandparent Problem Setup
3. Python Orchestrator
4. Running the Learning Algorithm
5. Key Design Decisions

Examples Using Scryer Prolog (The Modern Wave)

1. Why Scryer Prolog?
2. Installing Scryer Prolog
3. Differences from SWI-Prolog
4. DCG Processing of Large Text with Scryer
5. Constraint Logic Programming in Scryer
6. Porting SWI-Prolog Code to Scryer

Book Wrap Up

1. Summary of What We Covered
2. Where to Go from Here
3. The Future of Prolog in AI