Publication Date

2015-07-16

Availability

Open access

Embargo Period

2015-07-16

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PHD)

Department

Computer Science (Arts and Sciences)

Date of Defense

2015-07-06

First Committee Member

Ubbo E. Visser

Second Committee Member

Dilip Sarkar

Third Committee Member

Geoff C. J. Sutcliffe

Fourth Committee Member

Stephan C. Schürer

Fifth Committee Member

Vance P. Lemmon

Abstract

While computation power has increased and the statistical machine learning methods have made substantial advancement, many problems that would benefit from real-time interpretation have not exploited their combined strengths. For instance, the problem of gathering data from the environment and transforming it into knowledge as well as updating the knowledge as new data become available. Currently, with substantial expressivity and moderate computational cost, high-level languages or first-order predicate logic or model-based machine learning are used for static representation of knowledge, that is used for reasoning and inferring. In this dissertation, we address how an entity dynamically gather knowledge from environmental data and use that for inferring evolving events and dynamically update the current knowledge. We develop theoretical and empirical solutions using Description Logic representation and reasoning, and General Value Functions in Reinforcement Learning. The proposed solutions dynamically extract low-level knowledge from available data and update the high-level knowledge, which is used to predict the evolving future events. We show its applications in three real world domains: 1) RoboCup 3D Soccer Simulation environment, 2) High-throughput screening, and 3) Axon regeneration.

Keywords

Learnable Knowledge; Autonomous Agents; Description Logic; Real-Time Interpretation and Reasoning; General Value Functions; Reinforcement Learning; RoboCup 3D Soccer Simulation; High-Throughput Screening; Axon Regeneration

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