Heuristics Engine V0.1: Features, And Implementation Details

In the realm of user experience (UX) design, creating interfaces that adapt to user behavior is a significant leap forward. This article delves into the Heuristics Engine v0.1, a pivotal component designed to generate derived signals and adaptive insights from raw behavioral inputs. This engine serves as the cornerstone for complex UI adaptation scenarios, offering a non-machine learning (ML) approach to understanding user interactions. Let's explore the core requirements, proposed solutions, and the exciting possibilities this engine unlocks.

Problem / Motivation

The primary motivation behind the Heuristics Engine is to bridge the gap between raw user data and meaningful insights. Raw data, such as scrolls, idle time, focus changes, and clicks, provides a foundation, but the true value lies in interpreting this data to understand user behavior. The Heuristics Engine steps in to provide this interpretation, generating derived signals that represent interpreted meaning, such as low engagement, overload, or high-focus variability. This interpretation is crucial for creating adaptive user interfaces that respond intelligently to user needs without relying on complex machine learning models.

Core Requirements for the Heuristics Engine

To ensure the Heuristics Engine meets its objectives, several core requirements have been defined. These requirements encompass the engine's functionality, integration, and performance, setting the stage for a robust and effective system.

1. Creating the Heuristics Engine Module

The cornerstone of the project is the implementation of the createHeuristicsEngine() function. This function serves as the entry point for the engine, responsible for initializing and configuring the system. The engine must be capable of accepting input from signals and the current state of the application, allowing it to access the raw data and context necessary for generating heuristics. Crucially, the engine must emit heuristic outputs, providing insights such as engagementLevel, focusStability, scrollAggression, and idlePattern. These outputs form the basis for understanding user behavior and driving UI adaptations.

2. Derived Signal System

A key feature of the Heuristics Engine is its ability to output new “virtual signals.” These derived signals represent the interpreted meaning of raw data, providing a higher-level view of user behavior. To ensure seamless integration, these signals must be fed back into the core system’s signals.update() function. The core system should treat these derived signals as first-class citizens, indistinguishable from raw signals. This approach allows the system to react to both raw and interpreted data, creating a more responsive and adaptive user experience. The derived signal system allows for a more nuanced understanding of user behavior, going beyond simple metrics to capture the underlying context and intent.

3. Base Heuristics (MVP)

For the minimum viable product (MVP), the focus is on implementing 2–4 simple, deterministic heuristics. These heuristics serve as the foundation for more complex interpretations, providing a clear and transparent understanding of user behavior. Examples of base heuristics include:

• Focus Variability: This heuristic measures frequent oscillations in a