Representing High-Level Knowledge Structures in Massively Parallel Networks

Traditional artificial intelligence (AI) research has concentrated mostly on modeling high-level thought processes such as problem-solving and planning, and high-level representations such as rule-based heuristics and frame-based knowledge structures. Massively parallel networks, on the other hand, have been used mainly to model low-level perceptual processes such as vision, speech, associative memory, and learning. Recent research has started to bridge the gap between these disciplines. Massively parallel networks have many representational and computational advantages to bring to traditional AI work. These networks are very good at filling in partial information and at learning and representing subtle relationships among concepts. In addition, they provide a means to tightly integrate information from different sources as well as a model to encode parallel processing. However, many difficult issues need to be solved before massively parallel techniques can become more applicable and be able to complement traditional AI techniques. These issues include the problem of variable binding, multiple instantiations of knowledge structures, recursion, hierarchical abstraction, and temporal constraints.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Chun, Hon Wai

Contributors dc:contributor

• Waltz, David L.

• Artificial Intelligence
• Computer Science

Identifier

(UMI)AAI8803002

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/69368