The Potentials of AI Planning on the Edge

Abstract

Edge computing brings computation closer to sources of data and knowledge by embedding computation in the physical space and close to the end users. Edge computing is becoming the ultimate platform where modern applications based on IoT and AI are deployed in a truly distributed manner. When edge applications require goal-oriented behaviour, AI planning comes into play as a powerful tool for achieving such behaviour. In turn, this necessitates AI planning systems that can be deployed and operate on the edge possibly on a multitude of dispersed nodes. Current approaches to distributed AI planning are mainly designed around the requirements and peculiarities of multi-agent systems, such as communication constraints and the self-interest of agents. In this work, we postulate that edge computing provides new perspectives for distributing AI planning. We propose the concept of edge AI planning where multiple AI planning components are distributed on edge nodes and communicate over a vast network. These components need to have clearly defined requirements of what can be distributed and how in order for the overall AI planning to work effectively, in turn enabling correct and consistent executions across the whole system.