The OAI² Core Class Model and its application in the Minimal Viable Model (MVM) for Personal Event Recognition are designed to demonstrate the conceptual feasibility of creating object-oriented AI models. This effort serves as a starting point to explore how open, extensible, and standardized architectures can support the incremental development of autonomous intelligence capabilities, eventually simulating general intelligence.

However, this model is an initial proposal, acknowledging its imperfections and the need for further refinement. To achieve its full potential, it must undergo rigorous vetting and enhancement by a collaborative community of subject matter experts, including those from academia, industry, and government. Such collaboration will ensure that the model is robust, practical, and aligned with real-world needs.

The flexibility embedded in the core class definitions—especially the modularity of the Controller class—ensures that the architecture is adaptable and scalable. By providing a framework that encourages vendor-specific innovation while maintaining interoperability, the model sets the stage for developing complex, interoperable systems capable of addressing diverse applications.

Ultimately, the success of OAI² depends on its adoption by a broader community, the contributions of stakeholders, and the collective effort to build an ecosystem of open, ethical, and standards-based AI systems. This proposal invites collaboration and refinement, aiming to move closer to realizing the promise of standardized autonomous intelligence.

Entity

Purpose

The Entity class serves as the foundational base class for all other classes in the model. It represents the abstract concept of “things” that can exist and have properties, behaviors, and relationships. By defining the most generic attributes and methods, it establishes a flexible and extensible blueprint for derived classes, ensuring consistency and scalability across the system. The Entity class is inherently abstract and not instantiated directly but provides the structural foundation for its subclasses.

Core Role in the Model

As the root of all other classes, the Entity class ensures that all subclasses—such as Device, Data, PreProcessor, Processor, Controller, and Log—inherit its essential features. Each subclass will expand upon or specialize these features, detailing their unique attributes, methods, relationships, and responsibilities while adhering to the foundational structure of Entity.

Attributes

The Entity class defines three primary attributes:

id: A unique identifier that ensures every entity can be distinctly referenced within the system.

name: A descriptive label providing human-readable context for each entity.

attributes: A dictionary storing key-value pairs to define an entity’s properties dynamically. This design accommodates diverse use cases, allowing subclasses to define specialized attributes without modifying the base class.

Subclasses such as Device or Log will further refine and add to these attributes based on their specific roles. For instance, the Device class introduces attributes like type and status, which contextualize its operational state.

Methods

The Entity class provides basic methods to manipulate its attributes:

get_attribute(key): Retrieves the value of a specific property stored in the attributes dictionary.

set_attribute(key, value): Adds or updates a property dynamically, enabling flexible customization.

to_string(): Generates a human-readable string representation of the entity, summarizing its id, name, and attributes.

These methods are inherited and can be overridden or extended by subclasses. For example, the Log class might adapt to_string() to provide a specialized format for event records.

Relationships

The Entity class establishes the following relationships:

Inheritance: Acts as the base class for all other core classes, such as Device, Data, PreProcessor, Processor, Controller, and Log. Each subclass inherits its attributes and methods, ensuring structural consistency.

Composition: Not applicable at this level, as the Entity class itself does not directly embed other classes.

Subclasses will provide more specific relationships. For example, the Controller class interacts dynamically with multiple Processor and Device instances during system operation.

Responsibilities

As a base class, Entity provides a generic blueprint for all entities in the system. Its primary responsibility is to ensure that derived classes maintain consistent structure and behavior. Subclasses take on additional, specialized responsibilities, such as managing data, orchestrating workflows, or recording system events.

Constraints

The Entity class enforces the following constraints:

Every subclass must define id, name, and attributes upon initialization.

Subclasses must implement additional logic and attributes relevant to their specific role while adhering to the generic structure.

Interfaces

No interfaces are explicitly associated with the Entity class, as it provides intrinsic functionality that applies universally to all subclasses.

Lifecycle

The lifecycle of an Entity object begins with its initialization, which requires the id and name as mandatory attributes. Optional attributes can be added dynamically during runtime. Subclasses may extend the lifecycle to include specific operational or termination phases, such as the Log class’s periodic maintenance and clearing of old entries.

Device

Purpose

The Device class represents physical or virtual components that interact with the system. These components may include sensors, actuators, or software interfaces, and they serve as the entry and exit points for data within the system. By encapsulating interaction and data exchange mechanisms, the Device class ensures seamless integration of external inputs and outputs into the model.

In the context of the Minimal Viable Model (MVM) for Personal Event Recognition, the Device class plays a critical role by representing hardware like motion sensors and smart locks. These devices generate the raw data necessary for detecting events like “User Arrives Home” and “User Leaves Home.”

Core Role in the Model

As a subclass of Entity, the Device class inherits the foundational attributes and methods while introducing specialized features that cater to its specific role. The Device class provides a unified interface for interacting with various hardware and software components, allowing other classes like PreProcessor and Controller to access data consistently.

Attributes

The Device class expands upon the attributes of Entity with the following:

type: Specifies the category of the device (e.g., sensor, actuator, or edge device).
status: Indicates the current operational state of the device (e.g., “active,” “inactive,” or “error”).
data: A container for storing input/output data associated with the device.

In the MVM, attributes like type and status enable the system to differentiate between sensors detecting motion and actuators like locks, ensuring accurate event processing.

Methods

The Device class introduces methods for interacting with devices:

read_data(): Retrieves data from the device, typically used by sensors to supply raw input.
write_data(data): Sends data to the device, commonly used for actuators to execute commands.
get_status(): Provides the current operational state of the device, enabling the system to monitor device health.

For example, in the MVM, read_data() could be used to fetch motion detection data, while write_data(data) might trigger a smart lock to unlock the front door.

Relationships

The Device class establishes the following relationships:

Inheritance: Extends the Entity class, inheriting its attributes and methods.
Composition: Not applicable at this level.
Association:
- PreProcessor: Sends raw data to be transformed into a usable format.
- Processor: Supplies preprocessed data for further analysis and decision-making.
- Controller: Communicates with the Controller class, which orchestrates device operations and data flow within the system.

Responsibilities

The Device class is responsible for:

Capturing input data from the environment (e.g., motion detected by a sensor).
Sending output commands (e.g., unlocking a smart lock).
Reporting its operational status to the Controller class.

In the MVM, these responsibilities ensure that the system accurately detects and processes events related to the front door.

Constraints

The Device class enforces the following constraints:

Each device must define a valid type upon initialization.
Devices must handle failures gracefully, ensuring continuous operation of the system.

Interfaces

The Device class provides a standardized interface for interacting with diverse components. This abstraction simplifies system design and enhances scalability, particularly in the MVM, where multiple devices must work in concert.

Lifecycle

The lifecycle of a Device object begins with its initialization, during which its type, status, and other attributes are defined. Devices operate continuously, periodically reporting their status and supplying data to the system. At the end of their lifecycle, devices are decommissioned and removed from the system.

Data

Purpose

The Data class acts as a container for storing and transferring raw input and output information within the system. It provides a standardized mechanism for managing information, ensuring consistency across processes while maintaining a separation between data representation and logic.

In the context of the Minimal Viable Model (MVM) for Personal Event Recognition, the Data class facilitates the exchange of sensor data and processing outputs. For example, it stores motion detection readings from devices and delivers preprocessed or processed insights to other system components.

Core Role in the Model

The Data class is a fundamental element that underpins the flow of information in the system. As a subclass of Entity, it inherits the foundational attributes and methods, while introducing specialized properties tailored to its role. It serves as a bridge between Device, PreProcessor, Processor, and other components, enabling seamless integration and interaction.

Attributes

The Data class introduces attributes specifically designed for handling information:

content: Stores the actual data being managed, such as sensor readings or processing results.

format: Specifies the data format (e.g., JSON, XML), ensuring compatibility with various components.

timestamp: Records when the data was created or last updated, facilitating temporal analysis and debugging.

In the MVM, the content attribute might store a motion sensor’s binary output (e.g., “motion detected” or “no motion”), while timestamp tracks when the detection occurred.

Methods

The Data class does not directly implement processing or transformation methods. Instead, its role is purely as a container, leaving all operations—such as filtering, normalization, or transformation—to PreProcessor and Processor classes. This design ensures a clean separation of concerns.

Relationships

The Data class establishes the following relationships:

Inheritance: As a subclass of Entity, it adopts its general structure and functionality.

Composition: Not applicable at this level.

Association:

PreProcessor: Accesses raw data to prepare it for processing.

Processor: Receives preprocessed data to execute workflows and generate insights.

Controller: Manages the flow of data between components and ensures that the correct Data object reaches its intended destination.

In the MVM, the Data class serves as the conduit for passing sensor readings from Device to PreProcessor and subsequently to Processor.

Responsibilities

The data class’s primary responsibility is to act as a standardized container for system information. This enables other components to focus on their core logic without being burdened by data management complexities. Its simplicity ensures scalability and adaptability across various use cases.

Constraints

The Data class enforces the following constraints:

A valid format attribute must be defined to ensure data compatibility with the system.

The content attribute should only contain information in the specified format.

In the MVM, ensuring that motion data adheres to a consistent format prevents errors and misinterpretations during processing.

Interfaces

The Data class does not define specific interfaces, as its sole purpose is to store and transport information. Its utility comes from its compatibility with the interfaces and methods of other system components, such as PreProcessor and Processor.

Lifecycle

The lifecycle of a Data object begins with its creation when a Device generates raw input or a Processor produces output. It exists in memory as long as needed for processing or storage, and is dynamically destroyed or archived after its purpose is fulfilled. In the MVM, Data objects might be transient, existing only to handle a single motion detection event.

Knowledge

Purpose

The Knowledge class represents structured and validated information derived from raw data. It enables decision-making and reasoning by transforming raw inputs into actionable intelligence, such as rules, patterns, or insights. This class bridges the gap between data and meaningful understanding, making it a vital component in intelligent systems.

In the context of the Minimal Viable Model (MVM) for Personal Event Recognition, the Knowledge class helps the system recognize patterns in motion data (e.g., distinguishing between motion caused by a person or an object) and applies rules to determine whether a detected event corresponds to a user arriving or leaving the home.

Core Role in the Model

As a subclass of Entity, the Knowledge class builds on the foundational attributes and methods to encapsulate intelligence. It interacts closely with the Processor class to generate and utilize insights, ensuring that the system makes informed decisions based on validated rules and patterns.

Attributes

The Knowledge class introduces attributes specifically designed for representing intelligence:

rules: A collection of logical conditions or guidelines derived from data, used to make decisions or classify events.
insights: Key patterns or findings extracted from data, which provide actionable understanding.
source: References the original data or process that contributed to the creation of the rules and insights.

In the MVM, for instance, a rules attribute might specify that motion detected within a certain range of the front door, coupled with the absence of motion elsewhere, indicates a “User Arrives Home” event.

Methods

The Knowledge class defines methods that enable the use and maintenance of intelligence:

apply_rule(rule): Applies a specific rule to new data to evaluate its significance or relevance.
update_insights(new_insight): Adds or modifies insights based on new data or observations.
summarize(): Provides a concise overview of the rules and insights currently held by the Knowledge object.

For example, in the MVM, apply_rule(rule) might verify whether a sequence of motion data conforms to a defined rule for identifying user arrival or departure events.

Relationships

The Knowledge class establishes the following relationships:

Inheritance: Extends the Entity class, inheriting its generic attributes and methods.
Composition: Not applicable at this level.
Association:
- Processor: Utilizes the Knowledge class to guide workflows and decision-making.
- Controller: Retrieves Knowledge to validate or inform system operations.

Responsibilities

The primary responsibility of the Knowledge class is to structure and validate information, enabling the system to make intelligent decisions. In the MVM, it ensures that event recognition is based on well-defined rules and patterns derived from motion data.

Constraints

The Knowledge class enforces the following constraints:

Rules must be logically consistent and applicable to the data.
Insights should be updated regularly to reflect the most recent and relevant data.

Interfaces

No explicit interfaces are defined for the Knowledge class, as it provides intrinsic functionality that integrates seamlessly with other system components.

Lifecycle

The lifecycle of a Knowledge object begins with its initialization, often created by the Processor or PreProcessor classes as they analyze data. The Knowledge object is continuously updated during system operation as new rules and insights are derived. It persists as long as the system requires it for decision-making and is eventually archived or replaced as part of the system’s maintenance process.

PreProcessor

Purpose

The PreProcessor class represents a system component that prepares raw data for further processing. It performs operations such as filtering, normalization, and transformation to ensure that data is clean, structured, and ready for use by subsequent components in the workflow.

In the Minimal Viable Model (MVM) for Personal Event Recognition, the PreProcessor class is critical for transforming raw motion data from sensors into a standardized format. For instance, it might filter out irrelevant noise or normalize values to ensure compatibility with the event detection logic.

Core Role in the Model

The PreProcessor class is a specialized subclass of Entity that centralizes data preparation logic. It ensures that Data objects are refined and usable before being passed to the Processor. This separation of concerns enhances modularity and simplifies the design of complex workflows.

Attributes

The PreProcessor class introduces the following attributes:

steps: A list of preprocessing steps, such as filtering or normalization, that defines the operations applied to the data.

errors: A log of errors encountered during preprocessing, which can be used for debugging and quality assurance.

In the MVM, the steps attribute might include operations to ignore data from a sensor if no motion is detected for a certain duration.

Methods

The PreProcessor class provides methods for data preparation:

filter(data, criteria): Removes data points that do not meet specified criteria, ensuring relevance and accuracy.

normalize(data): Standardizes data values to a consistent format or scale, making them compatible with the processing logic.

log_error(error_message): Records any errors encountered during preprocessing, allowing for future diagnostics.

For example, in the MVM, filter(data, criteria) could be used to exclude motion data generated by non-human movements, such as pets.

Relationships

The PreProcessor class establishes the following relationships:

Inheritance: Extends the Entity class, inheriting its generic attributes and methods.

Composition: Not applicable at this level.

Association:

Device: Receives raw data from sensors or other devices for preprocessing.

Processor: Supplies preprocessed data for further analysis or workflows.

Controller: Activated by the Controller to perform preprocessing tasks.

In the MVM, the PreProcessor works closely with motion sensors and the Processor to refine raw data into actionable insights.

Responsibilities

The primary responsibility of the PreProcessor class is to transform raw data into a usable format. It ensures that data passed to the Processor is relevant, accurate, and standardized, minimizing the risk of errors during processing.

Constraints

The PreProcessor class enforces the following constraints:

A valid sequence of preprocessing steps must be defined to ensure consistency and reliability.

Errors during preprocessing must be logged for debugging and system monitoring.

Interfaces

The PreProcessor class provides a standard interface for applying preprocessing operations. This interface is designed to be flexible, allowing new preprocessing steps to be added without disrupting existing workflows.

Lifecycle

The lifecycle of a PreProcessor object begins when it is initialized with a predefined set of preprocessing steps. During runtime, it operates on incoming data, preparing it for processing. After completing its tasks, the PreProcessor may log errors or update its configuration to adapt to changing requirements. In the MVM, this lifecycle aligns with the real-time nature of event detection, where preprocessing occurs dynamically as data is captured.

Processor

Purpose

The Processor class is responsible for executing core logic and operations within the system. It takes preprocessed data as input, applies workflows or algorithms, and produces actionable insights or outputs. This class serves as the decision-making and computational hub, ensuring that raw or preprocessed data is transformed into meaningful results.

In the Minimal Viable Model (MVM) for Personal Event Recognition, the Processor class analyzes motion data and applies rules from the Knowledge class to determine whether the front door opening corresponds to a “User Arrives Home” or “User Leaves Home” event.

Core Role in the Model

As a subclass of Entity, the Processor class leverages its foundational attributes and methods while introducing specialized features for managing workflows. It interacts closely with PreProcessor, Knowledge, and Controller to execute system logic efficiently. The Processor plays a pivotal role in ensuring that the system responds intelligently to detected events.

Attributes

The Processor class introduces attributes that define and manage workflows:

workflow: Specifies the sequence of operations or tasks to execute.

inputs: A collection of preprocessed Data objects required for processing.

outputs: Stores the results of the executed workflow, which can be utilized by other system components.

In the MVM, the workflow attribute might define steps to classify motion data and correlate it with user arrival or departure events.

Methods

The Processor class defines methods for managing and executing workflows:

execute(data): Executes the specified workflow using the provided Data object(s).

add_step(step): Adds a new operation or task to the workflow.

get_results(): Retrieves the results of the workflow after processing the input data.

handle_error(error_message): Logs and handles errors encountered during processing.

For example, in the MVM, execute(data) might apply rules from the Knowledge class to determine whether a detected motion event is valid.

Relationships

The Processor class establishes the following relationships:

Inheritance: Extends the Entity class, inheriting its generic attributes and methods.

Composition: Not applicable at this level.

Association:

PreProcessor: Receives preprocessed data to perform additional analysis or workflows.

Knowledge: Utilizes rules and insights to guide decision-making within workflows.

Controller: Activated by the Controller class to execute specific workflows.

In the MVM, the Processor class relies on the PreProcessor for clean data and on the Knowledge class for logic-driven workflows.

Responsibilities

The Processor class is responsible for:

Transforming preprocessed data into actionable insights by applying workflows and algorithms.

Managing workflows, ensuring tasks are executed in the correct sequence.

Providing results to downstream components like Controller or Log.

In the MVM, the Processor class ensures that raw motion data evolves into meaningful event classifications.

Constraints

The Processor class enforces the following constraints:

A valid workflow must be defined before execution.

Preprocessed data must meet required quality and format standards to avoid processing errors.

Interfaces

The Processor class provides a standard interface for managing and executing workflows. This interface supports the addition of new steps and the retrieval of results, ensuring flexibility and adaptability.

Lifecycle

The lifecycle of a Processor object begins with its initialization, including the definition of its workflow. During runtime, it operates on incoming data, transforming it according to predefined tasks. Results are stored for use by other components, and errors are logged if encountered. In the MVM, the Processor operates in real time, continuously analyzing data and updating its outputs as events occur.

Controller

Purpose

The Controller orchestrates workflows across the system, ensuring seamless integration and coordination of all components. It is the central hub for managing interactions between the Processor, PreProcessor, Device, Knowledge, and Log classes. Unlike traditional software architectures reliant on applications, the Controller in this model facilitates a system architecture where operations are driven entirely by classes.

In the Minimal Viable Model (MVM) for Personal Event Recognition, the Controller manages workflows to detect events such as “Front Door Opens” and “Front Door Closes.” It coordinates data flow between sensors, preprocessing, and processing, ensuring that actionable insights are generated and logged without requiring standalone applications.

Core Role in the Model

The Controller class is the linchpin of the architecture, responsible for initiating, monitoring, and managing workflows. As a component designed to eliminate the need for traditional applications, it consolidates logic and control mechanisms within the class structure. By leveraging direct interactions with other core classes, the Controller ensures modularity, extensibility, and ease of integration in various implementations.

Attributes

The Controller class introduces attributes that manage workflows and system status:

active_workflows: A list of currently running workflows, enabling real-time tracking and management.

system_status: Indicates the overall status of the system (e.g., “idle,” “processing,” or “error”).

event_queue: A queue for managing incoming events or tasks, ensuring orderly execution.

In the MVM, active_workflows might track tasks such as analyzing motion data or applying rules from the Knowledge class, while event_queue organizes events like “motion detected near the front door.”

Methods

The Controller class defines methods to oversee workflows and system operations:

initialize_workflow(workflow_name): Starts a specific workflow and tracks its execution.

trigger_processor(processor_name, data): Activates a specific Processor instance with the provided data.

monitor_workflows(): Continuously monitors active workflows for progress, errors, or completion.

handle_event(event): Processes incoming events and assigns them to appropriate workflows or processors.

For example, in the MVM, initialize_workflow(“user_arrival”) might activate the Processor and Knowledge classes to confirm if motion detected aligns with a “User Arrives Home” event.

Relationships

The Controller class establishes the following relationships:

Inheritance: Not applicable, as the Controller class is a standalone component in the architecture.

Composition: Manages and interacts with instances of the Processor, PreProcessor, Device, Knowledge, Log, and Data classes.

Association: Acts as the central coordinator for all system interactions, ensuring smooth data flow and operation execution.

In the MVM, the Controller directly interacts with motion sensors (Device), preprocessing tasks (PreProcessor), event workflows (Processor), and logs for recording actions.

Responsibilities

The Controller class is responsible for:

Coordinating system operations, ensuring workflows execute in the correct sequence.

Responding to external triggers or events by routing tasks to appropriate components.

Monitoring system health and logging errors or critical events for auditability.

By centralizing these responsibilities, the Controller eliminates the need for application-level logic, allowing the system to function entirely through its class-based architecture.

Constraints

The Controller class enforces the following constraints:

Requires a valid workflow definition before initialization.

Must process the event_queue efficiently to avoid bottlenecks or delays.

Ensures all components, such as Processor and PreProcessor, adhere to standardized interfaces for seamless communication.

Interfaces

The Controller class interfaces with external systems or APIs to receive triggers and send notifications. It also interacts with internal classes to coordinate operations, monitor progress, and manage outputs. This design reinforces the system’s modular and application-free architecture.

Lifecycle

The lifecycle of a Controller object begins with its initialization at system startup. It continuously monitors workflows, processes events, and coordinates interactions between components during operation. The Controller persists as long as the system is active and shuts down gracefully during maintenance or termination. In the MVM, the Controller operates in real time, ensuring immediate responses to sensor data and event triggers.

Controller Justification and Design

Justification for Standalone Design

The Controller class is designed as a standalone component to emphasize its unique role as a high-level orchestrator in the system. Unlike subclasses of Entity, which represent tangible or conceptual objects within the model, the Controller focuses on coordinating workflows and interactions across all system components. Its standalone nature ensures:

Separation of Concerns: The Controller operates independently of Entity-level attributes like id and name, which are unnecessary for its orchestration role.
Flexibility: By not inheriting from Entity, the Controller avoids unnecessary constraints and complexity, allowing it to focus solely on managing workflows and communication between classes.
System Coordination: The Controller serves as the system’s operational backbone, unifying components such as Processor, PreProcessor, Device, Knowledge, and Log under a centralized logic.

Flexibility of the Controller

The Controller is designed to be flexible and adaptable, allowing different vendors to customize its functionality while adhering to the core architectural principles. This flexibility ensures:

Support for Innovation: Vendors can optimize the Controller for specific use cases, such as real-time event handling, distributed processing, or enhanced multitasking.
Scalability: Different implementations can support varying levels of complexity, from minimal viable products to systems simulating general intelligence.
Interoperability: Standardized interfaces ensure all customized Controller implementations remain compatible with other core classes.

Vendor Customization

Vendors can create their own versions of the Controller by:

Subclassing the Base Controller: Vendors can inherit from a base Controller class and implement their own logic for workflow management and event handling.
Defining Custom Workflows: Specific workflows, tailored to the vendor’s application, can be introduced without altering the core architecture.
Optimizing for Performance: Vendors may focus on specific priorities such as reducing latency, improving concurrency, or enabling distributed coordination.

Advantages of Controller Flexibility

Encourages Ecosystem Growth: Flexibility promotes vendor engagement and innovation.
Ensures Long-Term Scalability: Custom implementations can grow with the system’s complexity.
Maintains System Integrity: Standardized interfaces and abstract methods enforce compatibility, ensuring the overall architecture remains robust.

Abstract Methods and Interfaces

To support flexibility while maintaining consistency, the Controller class defines abstract methods and interfaces that all implementations must adhere to:

Abstract Methods:
- initialize_workflow(workflow_name): Starts and tracks the execution of a specific workflow.
- trigger_processor(processor_name, data): Activates a Processor instance with the provided data.
- monitor_workflows(): Continuously monitors active workflows for progress or errors.
- handle_event(event): Processes incoming events and assigns them to appropriate workflows or processors.
Standardized Interfaces:
- Interfaces for interacting with Processor, PreProcessor, Device, Knowledge, and Log ensure seamless communication across components.
- External APIs for receiving triggers and sending notifications maintain integration with third-party systems.

Conclusion

The standalone and flexible design of the Controller class is essential to achieving the goals of the OAI² architecture. By enabling vendors to create custom implementations, the model fosters innovation, scalability, and interoperability while maintaining the integrity of the system through standardized interfaces and abstract methods.

Log

Purpose

The Log class maintains a detailed record of system events, actions, and errors, providing a critical mechanism for transparency, accountability, and debugging. By systematically tracking the behavior of the system, the Log class enhances the inherent transparency of object-oriented models and ensures ethical accountability by documenting every significant action or decision made by the system.

In the Minimal Viable Model (MVM) for Personal Event Recognition, the Log class records events such as “Front Door Opens” or “User Arrives Home,” along with associated timestamps and processing outcomes. This facilitates auditing and debugging of the workflows.

Core Role in the Model

The Log class is an essential component of the architecture, serving as the memory of the system. It captures and stores event records for later analysis, closely integrating with other classes like Processor, PreProcessor, and Controller, which rely on it to document their operations. This class provides the transparency and accountability necessary to ensure the ethical operation of autonomous intelligence systems.

Attributes

The Log class introduces attributes specifically designed for event tracking:

log_entries: A collection of recorded events or messages, capturing the details of system operations.

log_level: Specifies the level of detail to be logged (e.g., ERROR, INFO, DEBUG), allowing flexibility in the depth of logged information.

In the MVM, the log_entries attribute could include records like “Motion detected at front door” or “Workflow ‘User Arrives Home’ executed successfully,” along with relevant timestamps.

Methods

The Log class provides methods to add, retrieve, and manage log entries:

add_entry(entry): Adds a new event or message to the log, ensuring that all significant actions are documented.

get_entries(filter_criteria): Retrieves log entries matching specified criteria, allowing targeted analysis or debugging.

clear_log(): Clears all log entries, typically used during system maintenance or resets.

For instance, in the MVM, add_entry(entry) might record the timestamp and status of a “Front Door Opens” event.

Relationships

The Log class establishes the following relationships:

Inheritance: Not applicable, as the Log class operates as a standalone component.

Composition: Frequently utilized by Processor, PreProcessor, and Controller to record actions and errors.

Association:

Processor: Logs the outcomes of workflows and decisions.

PreProcessor: Logs errors or significant preprocessing actions.

Controller: Tracks system-wide events, including workflow activations and errors.

In the MVM, the Log serves as the central repository for all system activity, ensuring a comprehensive record of operations.

Responsibilities

The primary responsibility of the Log class is to provide a transparent record of system activity. This includes:

Tracking system events, actions, and errors for auditing and debugging purposes.

Supporting ethical accountability by documenting decisions and actions.

Facilitating system monitoring and performance evaluation.

Constraints

The Log class enforces the following constraints:

A valid log_level must be defined to determine the granularity of logging.

Log entries must follow a standardized format to ensure consistency and readability.

In the MVM, this ensures that logs remain meaningful and actionable, even during complex workflows.

Interfaces

The Log class does not directly interface with external systems but provides an internal mechanism for storing and retrieving records. It integrates seamlessly with other components, offering a flexible and extensible framework for tracking system behavior.

Lifecycle

The lifecycle of a Log object begins at system initialization, when logging parameters are configured. During operation, the Log class continuously records system events and actions, updating its log_entries attribute. Logs can be cleared periodically during maintenance or archived for long-term analysis. In the MVM, the Log operates in real time, ensuring that every motion detection, workflow execution, and system action is documented for review.

Conclusion

Ultimately, the success of OAI² depends on its adoption by a broader community, the contributions of stakeholders, and the collective effort to build an ecosystem of open, ethical, and standards-based AI systems. This proposal is an invitation to collaborate and refine, aiming to move closer to realizing the promise of simulated general intelligence.