Natural language sensing for autonomous agents

Abstract

In sufficiently narrow domains, natural language understanding may be achieved via an analysis of surface features without the use of a traditional symbolic parser. We illustrate this notion by describing the natural language sensing in Virtual Mattie (VMattie), an autonomous clerical agent. VMattie "lives" in an UNIX system, communicates with humans via e-mail in natural language with no agreed upon protocol, and autonomously carries out her tasks without human intervention. VMattie's limited domain allows for surface level natural language processing. VMattie's language understanding module has been implemented as a copycat-like architecture though her understanding takes place differently. The mechanism includes a slipnet storing domain knowledge and a pool of codelets templates for building and verifying understanding. We describe the design and implementation of natural language sensing for autonomous agents such as VMattie.