mxgmn/MarkovJunior

Features

• Constraint Propagation Engines - Provides a core constraint propagation engine that prunes impossible states and ensures goal reachability.
• Goal-Oriented Probabilistic Inference - Filters generated execution runs to only those that satisfy a target end state through forward and backward constraints.
• Goal-Directed - Generates only the execution runs that lead to a specific goal by imposing constraints on future states.
• Probabilistic Sequence Generation - Creates text or sequences based on pattern matching and probability to generate varied but structured output.
• Grid Rewrite Systems - Provides a tool for specifying transformations that replace sequences of values within a multi-dimensional grid.
• Grid-Based Rewrite Rules - Provides a pattern-matching rewrite system to transform specific value sequences within a multi-dimensional grid into new states.
• Probabilistic Programming Languages - A language that uses pattern matching and constraint propagation to generate sequences based on probabilistic rules.
• Goal-State Inference Tools - Implements a framework for generating only the specific execution runs that lead to a defined goal state.
• Grid Rewrite Definitions - Provides pattern-matching transformations that replace specific sequences of values within a multi-dimensional grid.
• Grid-Based State Management - Utilizes a multi-dimensional grid to represent system state where values are determined by overlapping constraints.
• Grid Sequence Synthesis - Defines transformation and rewrite rules to generate multi-dimensional grids of data based on specific patterns.
• Wave Function Collapse Implementations - Uses wave-based superposition to track all possible value assignments for each cell and collapse them based on restrictions.
• Node-Based Generative Pipelines - Allows combining individual rule sets into sequences or nested nodes to create multi-stage generation pipelines.
• Multi-Stage Pipeline Processing - Builds complex workflows by chaining multiple sequential processing nodes together to transform data.
• Output Chaining - Processes data through sequential pipelines where the output of one node serves as the input for the next.
• Sequence Synthesis - Produces sequences that must satisfy specific global rules and goal states through constraint propagation.

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