Abstract reasoning for multiagent coordination and planning

Abstract

As autonomous software and robotic systems (or agents) grow in complexity, they will increasingly need to communicate and coordinate with each other. These agents will need planned courses of action to achieve their goals while sharing limited resources. This dissertation addresses the problem of efficiently interleaving planning and coordination for multiple agents. As part of my approach, I represent agents as having hierarchies of tasks that can be decomposed into executable primitive actions. Using task hierarchies, an agent can reason efficiently about its own goals and tasks (and those of others) at multiple levels of abstraction. By exploiting hierarchy, these agents can make planning and coordination decisions while avoiding complex computation involving unnecessary details of their tasks. To reason at abstract levels, agents must be aware of the constraints an abstract task embodies in its potential decompositions. Thus, I provide algorithms that summarize these constraints (represented as propositional state conditions and metric resource usages) for each abstract task in an agent’s library of hierarchical plans. This summary