Expectation-Based Learning in Design

Abstract

Design problems typically have a very large number of problem states, many of which cannot be anticipated at the onset of the design. Some design problem states are characterized by as many as hundreds of parameters. Given these amounts of uncertainty and information, AI design systems faced with learning tasks cannot know from the beginning what needs to be learned, and whether these needs will remain the same. In this abstract we describe how LEAD (Learning Expectations in Agent-based Design), a multiagent system for parametric and configuration design, addresses these challenges in design learning.