GenPlanX. Generation of Plans and Execution
This addresses the problem of enabling seamless human-AI collaboration in planning tasks for users needing productivity enhancements, though it appears incremental as it combines existing LLM and planning components.
The paper tackles the problem of classical AI planning techniques lacking natural language understanding by introducing GenPlanX, which integrates LLMs for interpreting human intents with a planning engine and execution framework, resulting in demonstrated efficacy in assisting with office-related tasks to streamline workflows.
Classical AI Planning techniques generate sequences of actions for complex tasks. However, they lack the ability to understand planning tasks when provided using natural language. The advent of Large Language Models (LLMs) has introduced novel capabilities in human-computer interaction. In the context of planning tasks, LLMs have shown to be particularly good in interpreting human intents among other uses. This paper introduces GenPlanX that integrates LLMs for natural language-based description of planning tasks, with a classical AI planning engine, alongside an execution and monitoring framework. We demonstrate the efficacy of GenPlanX in assisting users with office-related tasks, highlighting its potential to streamline workflows and enhance productivity through seamless human-AI collaboration.