norvig/paip-lisp

Features

• Game AI - Implements a comprehensive library of game AI search strategies including minimax and alpha-beta pruning.
• Symbolic AI Implementations - Provides reference implementations of foundational symbolic AI algorithms and general problem solving techniques.
• Game AI - Executes search strategies and heuristic evaluations to implement autonomous agents for board games.
• Logic Programming - Provides a full Prolog-style execution engine featuring unification and goal-driven backtracking.
• Backtracking Binding Trails - Reverts variable assignments using a trail to support goal-driven backtracking in the logic engine.
• Backtracking Search Algorithms - Provides a goal-driven backtracking search engine for exploring state spaces and finding valid solutions.
• Backtracking Solution Search - Finds and presents logical solutions one by one using depth-first search and automatic backtracking.
• Backward Chaining Engines - Implements a backward chaining reasoning process that evaluates premises through rules and queries to determine values.
• Certainty Factor Models - Calculates overall confidence levels of conclusions by combining probabilistic values using certainty factor formulas.
• Complex Problem Solving - Implements general problem solving by searching through a state space using a goal-driven approach.
• Constituent Syntax Analysis - Deconstructs sentences into hierarchical phrases and clauses using phrase-structure grammar rules to reveal linguistic structure.
• Minimax - Implements a Minimax search algorithm to determine optimal moves in adversarial games.
• Expert System Shells - Provides expert system shells that allow the creation of specialized knowledge bases for medical or technical rules.
• Alpha-Beta Pruning - Implements alpha-beta pruning to calculate optimal game moves by discarding irrelevant search tree branches.
• Heuristic State Evaluation - Calculates the value of game states using heuristics such as piece differentials and mobility measures.
• Logic Assertion Databases - Maintains a database of facts and rules to represent relationships and contingent truths for reasoning.
• Logical Form Translation - Transforms natural language strings into predicate calculus representations by mapping words to logical expressions.
• Search Pruning - Implements search tree pruning to discard irrelevant branches in game-state evaluations.
• Natural Language Processing - Provides a suite for parsing English sentences into logical forms using unification grammars.
• Natural Language Parsers - Implements a Definite Clause Grammar (DCG) parser that analyzes the grammatical structure of sentences.
• Sentence Structure Analysis - Analyzes natural language input to produce semantic expressions for declarative sentences, commands, and questions.
• State Space Search Algorithms - Implements classic state space search algorithms to navigate through problem states to find goal solutions.
• State Transition Solvers - Analyzes operator effects to determine the sequence of actions required to reach a target goal state.
• Symbolic Knowledge Representation Systems - Provides a comprehensive system for modeling worlds and entities using symbolic logic and associated functions.
• Unification Algorithms - Implements unification-based pattern matching to create consistent variable bindings between expressions.
• Logical Unification - Implements symbolic unification to determine if two expressions can be made identical by assigning values to variables.
• Prolog Interpretation - Evaluates logical goals by unifying them against a database of clauses to find valid substitutions.
• Term Unification - Provides a core unification mechanism to match queries against stored facts using variable bindings.
• Linguistic Pattern Definition - Specifies linguistic patterns and constraints for noun and verb phrases to facilitate natural language parsing.
• Pattern Matching - Provides symbolic pattern matching on data structures using variables and boolean operators to extract bound values.
• Decision Simulations - Simulates human decision-making by combining rule-based logic with certainty factors to provide probabilistic conclusions.
• State-Based Knowledge Bases - Deno Data Storage & Sync stores and retrieves parameter-value pairs and confidence levels in a dynamic database to track state.
• Logic Query Engines - Provides a logic-based query engine that uses unification and backtracking to evaluate goals against a database.
• Conjunction Querying - Implements the ability to retrieve bindings that satisfy multiple simultaneous goals through recursive conjunction solving.
• Syntax-to-Semantic Mappings - Associates grammar rules with functions that transform syntactic constituents into formal semantic representations.
• Grammar Rule Specification - Provides high-level syntax for defining grammar rules that automatically manage input and output string arguments.
• Logic Programming Engines - Implements a logic programming engine that derives conclusions from clauses via unification and backtracking.
• Lisp Dialects - Implements a lexical analyzer and reader that converts input into Lisp-style symbolic expressions.
• Symbolic Environment Management - Tracks and updates variable bindings across global and local scopes to resolve symbol values during execution.
• Algebraic Reduction Rules - Simplifies mathematical and logical expressions using an ordered set of symbolic transformation rules.
• Computer Algebra Systems - Implements a system for finding numeric values for unknowns in a set of algebraic equations.
• Infix-to-Prefix Translators - Converts mathematical expressions from infix notation to prefix notation using operator precedence rules.
• Expression Simplifiers - Provides tools to reduce mathematical expressions to their simplest algebraic form using symbolic rewrite rules.
• Polynomial Canonicalization - Converts polynomial expressions into a standard canonical form to identify equivalent expressions.
• Symbolic Computation Engines - Implements a symbolic computation engine for differentiation, integration, and algebraic simplification.
• Symbolic Differentiation Tools - Deno Symbolic Computation computes the derivative of a mathematical expression by treating differentiation as an operator.
• Semantic Logical Form Mapping - Translates parsed syntactic structures into logical forms that represent the intended meaning of a sentence.
• Semantic Phrase Resolution - Resolves the meaning of phrases by applying semantic functions directly to parse tree constituents.
• Notation Converters - Translates mathematical expressions between infix, prefix, and canonical representations.
• S-Expression Parsers - Uses nested S-expressions as the primary data structure for representing logic, grammars, and mathematical expressions.
• Reasoning Contexts - Groups related objects into types and tracks instances to resolve references without the use of logic variables.
• Reasoning Process Monitors - Justifies conclusions by providing pseudo-English translations of the reasoning chains and rules applied.
• Goal-Based Solvers - Finds a sequence of operators to achieve target goals by satisfying preconditions and updating state.
• Fact-Based Knowledge Representation - Stores and indexes logical predicates and arguments in a database for efficient symbolic retrieval.
• Search Heuristics - Implements the Killer Heuristic to optimize alpha-beta pruning in game search trees.
• Goal Sequence Resolution - Finds a sequence of actions to move from an initial state to a goal state by applying operators.
• Iterative Deepening Search - Prevents infinite loops in logic spaces by searching with incrementally increasing depth bounds.
• Knowledge Indexing - Deno Data Storage & Sync stores predicate-argument pairs in a tree structure to enable efficient retrieval based on atoms or variables.
• Knowledge Primitive Modeling - Implements a restricted first-order language for modeling categories, relations, and individuals within a knowledge representation system.
• Lexical Knowledge Bases - Implements a lexicon that stores nouns and verbs with pluralization, conjugation, and semantic frames.
• Beam Search Implementations - Optimizes search performance by limiting the number of candidate states considered at each level via beam search.
• Multi-World Knowledge Management - Indexes and fetches data within isolated environments to maintain separate sets of facts for different logical contexts.
• Derivation Tree Generation - Creates hierarchical tree structures representing the grammatical derivation of generated sentences.
• Grammar Enumeration - Finds every valid string a finite grammar can produce by exhaustively expanding rewrite rules.
• Stochastic Sentence Generation - Produces random sentences by recursively expanding a context-free phrase-structure grammar.
• Syntactic Structure Representation - Creates nested list representations of a sentence's full grammatical structure.
• Conversational Dialogue Systems - Emulates a dialogue partner by matching user input against patterns and returning randomized responses.
• Lexical Lookups - Deno Data Storage & Sync looks up words in the lexicon and matches them against specific categories or property constraints.
• Operator Definitions - Creates operations consisting of a name, required preconditions, and the resulting changes to the system state.
• Possible World Semantics - Handles uncertainty and negation by associating propositions with different truth values across alternative states.
• Relational Indexing - Deno Data Storage & Sync retrieves potential matches for a specific relation by traversing a decision tree and filtering by patterns.
• Rule-Based NLP Engines - Ships a rule-based engine that transforms natural language input into responses via ordered pattern matching and variable substitution.
• Sequence Search Algorithms - Utilizes beam search to identify an optimal sequence of operators to reach a target state from a starting point.
• State Space Modeling - Represents complex environments through specialized operator generators and formal state definitions.
• Certainty Factors - Combines multiple pieces of evidence using certainty factors to calculate probabilistic belief scores for conclusions.
• Destructive Unification - Optimizes logic programming by modifying internal structures during variable binding instead of using a separate list.
• Word Problem Translation - Converts natural language algebra word problems into mathematical expressions using rule-based pattern matching.
• Parse Interpretation Scoring - Assigns numeric weights to rules and constituents to determine the most likely interpretation of a sentence.
• Iterative Search Timing - Allocates time limits to individual moves and manages the decision to stop searching and return the best found move.
• Negation-Based Proofs - Implements proofs by contradiction by verifying that it is impossible to prove the opposite of a goal.
• Logic Primitive Implementations - Builds custom logic relations using Lisp functions that support continuations and specific calling conventions.
• Logic Programming Compilation - Transforms logic programming clauses into executable functions by mapping predicates and arities to native code.
• Variable-Length Segment Matching - Identifies sequences of items in an input that match a segment variable for variable-length matching.
• Ambiguity Detection - Detects multiple possible grammatical derivations for a single sentence and returns all valid parse trees.
• Frame-Based Representations - Implements a frame-based syntax for asserting facts about individuals and categories using slots.
• Declarative Knowledge Definitions - Provides a declarative syntax for specifying expert rules and parameters with certainty factors.
• Knowledge Base Storage - Provides structured storage for facts and assertions defined via logical predicates.
• Production Rules - Implements production rules that map clinical observations and patient attributes to probabilities of specific identities.
• Knowledge Graph Querying - Retrieves answers from a stored knowledge base by satisfying logical queries and constraints.
• Partial Parse Caching - Caches rule lookup results and partial parses to accelerate the processing of complex grammars.
• Dead Code Elimination - Includes an optimizer that eliminates dead code and removes redundant operations from generated instruction sequences.
• Lexicon Management - Deno Data Storage & Sync stores words with categories, subcategory information, and semantic properties to support natural language processing.
• Grammar Compilation - Translates high-level grammar rules into executable functions to eliminate runtime interpretation overhead.
• Peephole Bytecode Optimization - Replaces inefficient sequences of assembly-like instructions with high-performance patterns using peephole optimization.
• Move Ordering - Optimizes search efficiency by prioritizing the evaluation of promising moves in the game tree.
• Board Games - Simulates the Othello board game, managing turn-based logic and state tracking.
• Abstract Machine Emulations - Ships a virtual machine that executes compiled bytecode using an internal operand stack and program counter.
• Bytecode Compilation - Translates symbolic expressions into a flat vector of bytecode for execution by an abstract machine.
• Class Blueprint Definitions - Organizes instance variables and methods into named classes to standardize object creation.
• Class Inheritance - Implements the ability to build specialized classes that inherit variables and methods from base classes.
• Predicate Rewriting Rules - Creates transformation rules that allow the compiler to optimize or rewrite specific predicates during the compilation process.
• Virtual Machine Instruction Generation - Translates code into VM instructions using a peephole optimizer to improve execution efficiency.
• First-Class Continuations - Captures current execution state as first-class continuations to enable non-local exits and complex control flow.
• Logic-to-Function Compilers - Implements a compiler that translates logic programming predicates into executable Lisp functions.
• Instruction Vector Assembly - Converts symbolic lists of instructions into flat code vectors by resolving labels and jump addresses.
• Custom Macro Definitions - Implements the ability to define custom macros that expand into other code during the compilation process.
• Macro Expansion Engines - Implements a macro expansion engine that transforms specific expressions into custom language constructs.
• Symbolic Pattern Matching - Identifies literal matches and variable assignments within lists to substitute input values into generated responses.
• Polymorphic Dispatch - Defines a global interface that executes the appropriate method for an object based on its class.
• Scheme Implementations - Provides an interpreter that evaluates Scheme expressions by processing symbols, atoms, and procedure applications.
• Source Code Compilers - Implements a compiler that transforms source code into executable form with tail recursion and peephole optimizations.
• Peephole Optimizers - Translates symbolic instructions into a flat code vector with a peephole optimizer to remove redundant operations.
• Tail-Call Optimizations - Prevents stack overflow by optimizing recursive calls in the final position using variable renaming and branching.
• Variable Segment Capturing - Matches sequences of arbitrary length within an input list and binds them to a variable for flexible pattern matching.
• Rational Expressions - Deno Symbolic Computation represents the quotient of two polynomials as a rational expression and reduces it to simplest form.
• Natural Language Algebra Solvers - Translates English phrases into mathematical equations and solves them using constraint propagation techniques.
• Operator Simplification Logic - Associates specific operators with custom simplification functions to override standard rule-based rewriting.
• Polynomials - Deno Symbolic Computation calculates the derivative of a polynomial with respect to a specified variable.
• Logic-to-Lisp Translation - Translates Prolog clauses into executable Lisp functions by grouping predicates by arity and transforming logic bodies.
• Closures - Implements functions that capture and maintain access to variables from their defining lexical environment.
• Constraint Propagation Engines - Implements constraint propagation to prune invalid label states by enforcing consistency between neighboring vertices.
• Constraint Satisfaction Solvers - Determines valid labels for vertices in a diagram by combining constraint propagation and search.
• Consistent Labeling - Finds all valid interpretations of line diagrams that satisfy specified vertex and edge constraints.
• Filler-Gap Resolution - Tracks filler-gap dependencies in relative clauses or questions to identify missing subjects or objects.
• Closure-Based State Encapsulations - Implements objects as closures to encapsulate internal state and restrict data access.
• Declarative Grammar Definitions - Defines grammatical rules as data structures to allow updates without changing generation logic.
• Exhaustive Phrase Expansions - Computes every valid expansion of a phrase by recursively applying all matching grammar rules.
• AI & Machine Learning - Classic paradigms of artificial intelligence programming.
• Artificial Intelligence - Code examples from the classic AI programming textbook.

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