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|Title:||Representing High-Level Knowledge Structures in Massively Parallel Networks|
|Author(s):||Chun, Hon Wai|
|Doctoral Committee Chair(s):||Waltz, David L.|
|Department / Program:||Electrical Engineering|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Abstract:||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.
These problems are explored in this thesis through the presentation of massively parallel models for schema structures, an approach to defining knowledge modules, and a model to represent rules. The massively parallel representational techniques introduced in this thesis include a representation of constraints which has been used to model schemata with temporal sequence constraints, a representation of modular knowledge structures that have been used to model adaptive competition as well as interactions among higher-order concepts, a hybrid schema representation which combines connectionist value-passing with marker passing, and a hybrid natural language rule-based system. These techniques have been implemented and successfully tested in applications such as speech recognition and natural language understanding.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1987.
|Date Available in IDEALS:||2014-12-15|
This item appears in the following Collection(s)
Dissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer Engineering
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois