A Rebellion Framework with Learning for Goal-Driven Autonomy

Bibliographic Details
Main Author:	Mohammad, Zahiduddin
Language:	English
Published:	Wright State University / OhioLINK 2021
Subjects:	Computer Science Artificial Intelligence MIDCA Cognitive architectures Rebellion Learning
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=wright1621899990938131

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-wright16218999909381312021-08-03T07:17:36Z A Rebellion Framework with Learning for Goal-Driven Autonomy Mohammad, Zahiduddin Computer Science Artificial Intelligence MIDCA Cognitive architectures Rebellion Learning Modeling an autonomous agent that decides for itself what actions to take to achieve its goals is a central objective of artificial intelligence. There are various approaches used to build autonomous agents including neural networks, state machines, utility functions, learning agents, and cognitive architectures. In this thesis, we focus on cognitive architectures. Our approach uses specific knowledge of the world, the goals they pursue, and the actions being performed. Most agents do what they are told (i.e., achieve the goals given to them by a human), but a genuinely autonomous agent does more. It can formulate its own goal or change the goals given to it. Sometimes an agent should even refuse to accept a given goal because of issues that violate its preferences or motivations. Rebellion (the refusal of an autonomous agent to accept a goal) is a vital trust requirement if critical conditions are not met, such as ethics, safety, and behavioral correctness. We will exploit rebellion to realize an agent framework that is both goal-driven and adaptive.Using an existing cognitive architecture, we implemented a rebel agent that rejects goals having undesirable effects. In particular, the agent uses explicit expectations about its goals and reasons about the positive and negative interactions between them to make such decisions. We also implemented a learning version of this agent, which learns from its mistakes and seeks to avoid similar mistakes in the future. In this work, we seek the maximum achievement of goals when actions have both desirable and undesirable effects and demonstrate improved goal achievement by incorporating learning algorithms within a goal-driven rebel agent framework. 2021-05-28 English text Wright State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=wright1621899990938131 http://rave.ohiolink.edu/etdc/view?acc_num=wright1621899990938131 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Computer Science Artificial Intelligence MIDCA Cognitive architectures Rebellion Learning
spellingShingle	Computer Science Artificial Intelligence MIDCA Cognitive architectures Rebellion Learning Mohammad, Zahiduddin A Rebellion Framework with Learning for Goal-Driven Autonomy
author	Mohammad, Zahiduddin
author_facet	Mohammad, Zahiduddin
author_sort	Mohammad, Zahiduddin
title	A Rebellion Framework with Learning for Goal-Driven Autonomy
title_short	A Rebellion Framework with Learning for Goal-Driven Autonomy
title_full	A Rebellion Framework with Learning for Goal-Driven Autonomy
title_fullStr	A Rebellion Framework with Learning for Goal-Driven Autonomy
title_full_unstemmed	A Rebellion Framework with Learning for Goal-Driven Autonomy
title_sort	rebellion framework with learning for goal-driven autonomy
publisher	Wright State University / OhioLINK
publishDate	2021
url	http://rave.ohiolink.edu/etdc/view?acc_num=wright1621899990938131
work_keys_str_mv	AT mohammadzahiduddin arebellionframeworkwithlearningforgoaldrivenautonomy AT mohammadzahiduddin rebellionframeworkwithlearningforgoaldrivenautonomy
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A Rebellion Framework with Learning for Goal-Driven Autonomy

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