**Conceptual Document: autologos - Foundational Concepts and Alpha v0.1 Idealized Structures** **1. Introduction: The Vision of autologos and aiOS** * **autologos (The Language):** Envisioned as a highly fault-tolerant, context-sensitive **controlled natural language (CNL)**. It aims to be the "Logo for the AI Age," allowing humans to express logical intent and orchestrate AI capabilities using intuitive, near-natural language constructs. The primary burden of parsing, disambiguation, and interpretation rests with the AI. Users are guided by principles and examples, not strict syntax rules. The goal is global accessibility, minimizing the need for users to memorize complex syntax or understand traditional programming paradigms. * **aiOS (The System):** A conceptual AI Operating System that understands and executes instructions written in autologos. It manages resources, orchestrates processes (like those defined in `MetaProcessEngineASO`), handles data, and facilitates human-AI collaboration. `MetaProcessEngineASO v3.0` is envisioned as a prime application built upon an aiOS, using autologos for its internal procedural logic. * **Core Principle - User-Centric Fault Tolerance:** The system "tries to understand" user input, asking for clarification on ambiguities rather than failing on syntactic deviations. "It'll run no matter what" (in terms of attempting interpretation), with output quality reflecting input clarity. **2. Guiding Philosophy for User Interaction with autologos** * **Simplicity (Child-Friendly Design):** Instructions should be expressible with the clarity one might use when explaining logic to a child – focusing on fundamental concepts. * **No Memorization of Strict Syntax:** Users interact by describing logic. ALL CAPS for conceptual keywords (e.g., `IF`, `THEN`, `FOR EACH`) can serve as a *convention* or *hint* to the AI for clarity, but their absence should not break interpretation. * **AI as the Interpreter:** The AI does the "heavy lifting" of parsing, disambiguating (using context), and mapping to internal actions. * **Focus on Logical Intent:** Users describe *what* they want to achieve logically; the aiOS figures out *how* based on its understanding of autologos and its available capabilities (e.g., external functions). * **Global Accessibility:** Designed with the aspiration to understand logical constructs across different natural languages and cultural phrasings (a long-term NLU challenge). * **User Guidance via Primers & Examples:** Documentation for users will be in the form of "Primers on Thinking Logically with autologos" and "Dictionaries/Cookbooks of Common Phrasings," not rigid syntax manuals. **3. autologos Alpha v0.1 - Idealized Logical Constructs & AI's Target Representation** *(The following describes the internal structures or idealized forms the AI aims to derive from user's autologos statements, or what an AI like FEL-MH might use when generating its own internal logic for consistency. Users are not expected to write this syntax perfectly.)* **3.1. Core Conceptual Keywords (AI's Interpretive Targets):** IF, THEN, ELSE, ELSE IF, AND, OR, NOT, IS, EQUALS, CONTAINS, MATCHES, ANY, ALL, GREATER_THAN, LESS_THAN, TRUE, FALSE, REPEAT, WHILE, FOR, EACH, IN, KEYS, PAIRS, VALUES, SWITCH, CASE, DEFAULT, DEFINE, RETURN, TRY, CATCH, FINALLY, RAISE, CONTINUE, BREAK, LOG, INVOKE, SET (...TO...), APPEND (...TO...), INSERT (...INTO...), REMOVE (...FROM...), GET (...FROM...), DELETE (...FROM...), CONCATENATE. **3.2. Conceptual Data Types (Inferred by AI):** STRING, INTEGER, BOOLEAN, LIST (of items), OBJECT (with properties), NULL, UNDEFINED. (Conceptual types for content: YAML, MARKDOWN). **3.3. Variables and State (Managed by AI based on user reference):** AI infers variables from descriptive nouns/phrases used by the user (e.g., "the user's request", "my list of tasks"). Assignment phrased naturally (e.g., "make the task status 'complete'"). *Idealized internal form:* `TaskStatus := "complete"` **3.4. Comments (Distinguished by AI):** AI distinguishes descriptive/commentary text from instructional autologos. **3.5. Control Flow Structures (AI's Target Understanding):** * **Conditionals:** `IF (condition) { actions } [ELSE IF (condition) { actions }] [ELSE { actions }]` * **Loops:** `WHILE (condition) { actions }`, `FOR EACH item IN list { actions }`, `FOR EACH key, value IN PAIRS(object) { actions }` * **Loop Control:** `CONTINUE`, `BREAK` * **Multi-way Branch:** `SWITCH (expression) { CASE value: { actions } DEFAULT: { actions } }` (No fallthrough) **3.6. Data Structure Operations (AI's Target Understanding):** * **Object Creation:** `MyObject := OBJECT { key1 := "value1" }` * **Object Access/Modification:** `MyObject.key1`, `MyObject["key"]`, `SET(MyObject, "key", value)` * **Object Info:** `HAS_KEY(MyObject, "key")`, `KEYS(MyObject)`, `VALUES(MyObject)` * **List Creation:** `MyList := LIST { "a", 1 }` * **List Access/Modification:** `MyList[index]`, `SET(MyList, index, value)` * **List Add/Remove:** `APPEND(MyList, item)`, `INSERT(MyList, index, item)`, `REMOVE_AT(MyList, index)`, `REMOVE_ITEM(MyList, item)` * **List Info:** `LENGTH(MyList)`, `IS_EMPTY(MyList)` * **List Concatenation:** `CONCATENATE(List1, List2)` * **Higher-Order List Functions (Conceptual):** * `FILTER(List, ConditionOnITEM)` * `MAP(List, TransformationOnITEM)` * `REDUCE(List, ReductionLogicOnACCUMULATOR_AND_ITEM, InitialValue)` **3.7. Defining and Invoking Procedures (Internal & External):** * **Defining Reusable Logic (AI's Internal Structuring):** `DEFINE FunctionName(param1, ...) { actions; RETURN value }` * **Invoking Logic:** `Result := FunctionName(arg1, ...)` (for internal/autologos functions) `JsonResult := INVOKE ExternalFunctionNameDeclaredInAIStudio(paramName := value, ...)` (for external, user-implemented functions) **3.8. Error Handling (Conceptual):** * `TRY { actions } CATCH ErrorType AS ErrorObject { handling_actions } FINALLY { cleanup_actions }` * `RAISE ErrorType "message"` **3.9. Logging:** * `LOG expression_or_string` **4. Relationship to `MetaProcessEngineASO_v3.0`** * `MetaProcessEngineASO_v3.0` will be an application whose core orchestration logic (within MHs, MRO, Kernel) is expressed using autologos statements. * These autologos statements will primarily guide the AI in making a sequence of `INVOKE` calls to external functions (defined in `function_declarations_v3.0.json` and implemented by the user). * The `ASO_EngineFile_Schema_vX.Y.Z` will define the Markdown/YAML file structure of the Engine, specifying which string fields contain these autologos instructions. The schema does *not* validate the autologos syntax itself; that is the job of the AI's interpreter. **5. Key Challenge: The AI's autologos Interpreter** The success of autologos hinges on the AI's ability to: * Robustly parse naturalistic logical statements. * Disambiguate intent using extensive context. * Map these statements to the idealized internal logical constructs. * Execute these constructs, including orchestrating external function calls. * Ask for clarification when interpretation confidence is low. * Learn and improve its interpretation over time (via LHLs and other feedback). This document summarizes the core concepts and idealized structures for autologos Alpha v0.1 as discussed. It is intended to be a living document, evolving as the aiOS concept matures. ---