A Short-Term Memory for Deliberative Agents in Everyday Environments

Abstract

Humans have the impressive capability to efficiently find near-optimal solutions to complex, multistep problems. AI planning can model such problems well, but is inefficient for realistic problems. We propose to use AI planning in combination with a short-term memory, inspired by models of human short-term memory, to structure real-world problem domains and make the planning process more efficient, while still producing satisficing solutions. We evaluate the method in the domain of a household robot.