Executable Knowledge Model for Task-Oriented Dialogue Management

This paper presents an executable knowledge model for task-oriented dialogue management, developed by Xolution to support the execution of complex, multi-step tasks through a unified, rule-based mechanism. The proposed architecture separates declarative task definitions from their execution. Tasks are represented as hierarchical knowledge models containing subtasks, questions, logical conditions, production rules, and reusable expert-system components. A common dialogue manager interprets these models, continuously updates the task state, and determines subsequent actions through rule-based inference. Unlike traditional dialogue workflows, the approach supports mixed-initiative interaction, allowing users to provide relevant information outside the expected question sequence. The system incorporates forward- and backward-chaining inference, activation networks, and mechanisms for detecting logical inconsistencies. Task execution is independent of the interaction interface and output generation, enabling results to be represented as structured data, documents, or other machine-processable formats. The approach is demonstrated through an expert-system use case illustrating dynamic dialogue management, knowledge reuse, and explainable decision-making. The research provides a foundation for knowledge-driven intelligent assistants and the automation of complex business processes.