[mod.ai] Seminar - Case-Based Learning System

FAWCETT@RED.RUTGERS.EDU (Tom Fawcett) (10/30/86)

TITLE:     Memory Access Techniques for a Case-based 
              Learning System
SPEAKER:   Wendy Lehnert
DATE:      Monday, November 3
LOCATION:  Princeton University, Green Hall, Langfeld Lounge
TIME:      12:00 - 1:00 p.m.

                     Abstract

Traditionally, symbolic processing techniques in artificial
intelligence have addressed "high-level" cognitive tasks
like expert reasoning, natural language processing,
and knowledge acquisition.  At the same time, a separate
paradigm of connectionist techniques has addressed 
"low-level" perceptual problems like word recognition,
stereoscopic vision and speech recognition.  While
symbolic computation models are frequently characterized as
brittle, difficult to extend, and exceedingly fragile, many
connectionist models exhibit graceful degradation and natural
methodologies for system expansion.
In this talk, we will look at how connectionist techniques
might be useful as a strategy for indexing symbolic memory.

Our discussion will focus on two seemingly unrelated tasks:
word pronunciation and narrative summarization.  We will
endeavor to show how both problems can be approached with
similar strategies for indexing memory and resolving
competing indices.


-------