kmizu · February 3, 2025 05:40
diff --git a/o3-mini-high-thinking.txt b/o3-mini-high-thinking.txt
 Mapping out the solution
 I’m piecing together a JavaScript-based Prolog interpreter, leveraging HTML for browser functionality and CSS for a sleek interface. This requires implementing a minimal Prolog subset like basic facts and queries.
 Planning the layout
 I’m crafting an HTML file with embedded CSS and JavaScript for a Prolog interpreter. It includes areas for Prolog code input, query input, a button to run queries, and an output division.
 Mapping a plan
 Crafting a JavaScript-based Prolog interpreter with depth-first search resolution for Horn clauses. Including a simple parser and basic query handling.
 Breaking down the solution
 I’m developing a JavaScript code to parse Prolog rules, store them in a database, and implement a resolution algorithm for queries. Clauses will be represented as {head: Term, body: [Term]}.
 Mapping out Prolog parsing
 I’m considering a simplified parser for Prolog terms, focusing on atoms and structures with functor and arguments, while adhering to a limited format like “parent(john, mary).” and “grandparent(X, Y) :- parent(X, Z), parent(Z, Y).”
 Honing the function
 I’m focusing on a parseProgram() function to tokenize Prolog code, split it by periods, and carefully manage dots within parentheses, ensuring accurate recursive descent parsing for terms.
 Mapping the process
 I’m crafting a Prolog code tokenizer to identify identifiers and punctuation, followed by a recursive descent parser to parse clauses, carefully managing dots within parentheses.
 Composing the parser
 I’m planning to create a tokenizer for Prolog code, identify terms as variables, atoms, or compound terms, and detail the parsing of clauses and terms, considering unification and resolution.
 Mapping out the approach
 I’m working through a plan to tokenize Prolog code, parse clauses into terms and bodies, and incorporate unification and resolution algorithms to handle variable substitution and unification.
 Crafting unify function
 I’m developing a unify function to handle variables, atoms, and compound terms in Prolog, ensuring variables are consistently managed and substitutions are thoroughly evaluated.
 Formulating the solution
 I’m pulling together a Prolog resolution algorithm using a depth-first search to unify goals with program clauses, standardizing variables, and recursively generating solutions.
 Tackling recursion and variables
 I’m addressing potential infinite recursion by demarcating variables with unique names, using a global counter, and ensuring a more controlled and efficient clause standardization.
 Setting up the interface
 I’m working on constructing a minimal Prolog interpreter with HTML, CSS, and JavaScript. It features a text area for the Prolog program, an input field for queries, and a “Run Query” button.
 Building the interface
 I’m developing a Prolog interpreter with HTML, CSS, and JavaScript, featuring a text area for the Prolog program, an input field for queries, and a “Run Query” button.
 Designing Prolog interpreter
 I’ve crafted a Prolog interpreter using HTML, JavaScript, and CSS. Users can input Prolog queries and programs, with results shown upon execution.
 Working through the tokenizer
 I’m mapping out the tokenizer, considering regex groups, token types, and methods. Parsing involves identifying heads and bodies of clauses, handling rules, and token consumption.
 Processing terms
 I’m detailing how to handle nested terms, numbers as atoms, and identifying clauses. Parsing ensures smooth consumption of tokens, aligning with the expected structure for accurate term detection.
 Refining parsing and unification
 I’m working through parsing queries, ensuring token consumption, and refining unification to handle variable and compound types efficiently. This ensures precise term matching and accurate substitutions.
 Validating unification
 I’m confirming unification handles atoms, numbers, and compounds, checking equality and argument lengths, and managing variable clashes with occurrence checks. The resolution algorithm is also evaluated.
 Enhancing unification
 I’m refining unification and standardizing clauses. The resolution algorithm employs depth-first search, using utility functions for term handling and formatting substitutions. The HTML involves interactable elements.
 Collecting and presenting solutions
 Noticing that the event listener on runButton gathers program and query text, parses them, runs prove to find solutions, and displays them with “解” for each solution.
	Mapping out the solution
	I’m piecing together a JavaScript-based Prolog interpreter, leveraging HTML for browser functionality and CSS for a sleek interface. This requires implementing a minimal Prolog subset like basic facts and queries.
	Planning the layout
	I’m crafting an HTML file with embedded CSS and JavaScript for a Prolog interpreter. It includes areas for Prolog code input, query input, a button to run queries, and an output division.
	Mapping a plan
	Crafting a JavaScript-based Prolog interpreter with depth-first search resolution for Horn clauses. Including a simple parser and basic query handling.
	Breaking down the solution
	I’m developing a JavaScript code to parse Prolog rules, store them in a database, and implement a resolution algorithm for queries. Clauses will be represented as {head: Term, body: [Term]}.
	Mapping out Prolog parsing
	I’m considering a simplified parser for Prolog terms, focusing on atoms and structures with functor and arguments, while adhering to a limited format like “parent(john, mary).” and “grandparent(X, Y) :- parent(X, Z), parent(Z, Y).”
	Honing the function
	I’m focusing on a parseProgram() function to tokenize Prolog code, split it by periods, and carefully manage dots within parentheses, ensuring accurate recursive descent parsing for terms.
	Mapping the process
	I’m crafting a Prolog code tokenizer to identify identifiers and punctuation, followed by a recursive descent parser to parse clauses, carefully managing dots within parentheses.
	Composing the parser
	I’m planning to create a tokenizer for Prolog code, identify terms as variables, atoms, or compound terms, and detail the parsing of clauses and terms, considering unification and resolution.
	Mapping out the approach
	I’m working through a plan to tokenize Prolog code, parse clauses into terms and bodies, and incorporate unification and resolution algorithms to handle variable substitution and unification.
	Crafting unify function
	I’m developing a unify function to handle variables, atoms, and compound terms in Prolog, ensuring variables are consistently managed and substitutions are thoroughly evaluated.
	Formulating the solution
	I’m pulling together a Prolog resolution algorithm using a depth-first search to unify goals with program clauses, standardizing variables, and recursively generating solutions.
	Tackling recursion and variables
	I’m addressing potential infinite recursion by demarcating variables with unique names, using a global counter, and ensuring a more controlled and efficient clause standardization.
	Setting up the interface
	I’m working on constructing a minimal Prolog interpreter with HTML, CSS, and JavaScript. It features a text area for the Prolog program, an input field for queries, and a “Run Query” button.
	Building the interface
	I’m developing a Prolog interpreter with HTML, CSS, and JavaScript, featuring a text area for the Prolog program, an input field for queries, and a “Run Query” button.
	Designing Prolog interpreter
	I’ve crafted a Prolog interpreter using HTML, JavaScript, and CSS. Users can input Prolog queries and programs, with results shown upon execution.
	Working through the tokenizer
	I’m mapping out the tokenizer, considering regex groups, token types, and methods. Parsing involves identifying heads and bodies of clauses, handling rules, and token consumption.
	Processing terms
	I’m detailing how to handle nested terms, numbers as atoms, and identifying clauses. Parsing ensures smooth consumption of tokens, aligning with the expected structure for accurate term detection.
	Refining parsing and unification
	I’m working through parsing queries, ensuring token consumption, and refining unification to handle variable and compound types efficiently. This ensures precise term matching and accurate substitutions.
	Validating unification
	I’m confirming unification handles atoms, numbers, and compounds, checking equality and argument lengths, and managing variable clashes with occurrence checks. The resolution algorithm is also evaluated.
	Enhancing unification
	I’m refining unification and standardizing clauses. The resolution algorithm employs depth-first search, using utility functions for term handling and formatting substitutions. The HTML involves interactable elements.
	Collecting and presenting solutions
	Noticing that the event listener on runButton gathers program and query text, parses them, runs prove to find solutions, and displays them with “解” for each solution.