Fast Interpretation of Pen Gestures with Competent Agents

Modeling the interpretation process of gestures generated by digital pen traces with a specialised agential architecture provides access to many advantages not normally available in routine programming methodologies. While speed of interpretation is an obvious plus point for the overall system performance, accurate and competent percept of the sketching environment provided by three autonomously self-learning agents allows for their seamless integration into elaborate applications without affecting the main program flow. This paper focuses on the techniques used to decipher ink traces by utilising ‘Ink’, ‘Gesture’, and ‘Decision’ agents in an ambient learning environment. Expediting the responses for interaction between users and synthetic hardware systems is expected to have an improved effect on user-interface (UI) supports for natural communications.