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<meta property="og:title" content="A2C2 - Natural Language-Instructed Autonomous Agent for Computer Contrl"/>
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<h1 class="title is-1 publication-title">A2C2 - Natural Language-Instructed Autonomous Agent for Computer Control</h1>
<div class="is-size-5 publication-authors">
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<span class="author-block">
<a href="https://github.com/yingrjimsch" target="_blank">Gabriel Nobel</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://vonwareb.github.io" target="_blank">Rebekka von Wartburg-Kottler</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://sagerpascal.github.io/" target="_blank">Pascal Sager</a><sup>°</sup>,</a>
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<span class="author-block">
<a href="https://stdm.github.io" target="_blank">Prof. Thilo Stadelmann</a><sup>°</sup>,</a>
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<span class="author-block">Zuerich University of Applied Science<br>2024</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
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First, we look at the input, then learning takes its turn,<br>
Next, we sail to input decomposition, where insights we discern.<br>
We cross the seas to plan refinement, with choices firm and stout,<br>
Until at last, the system’s output is what it is about.
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<p>
Recent advances in artificial intelligence (AI) have boosted progress across various
domains, particularly enabling breakthroughs in the discipline of Natural Language-
Instructed Autonomous Agents for Computer Control (A2C2s). Due to their capabilities
of understanding natural language and executing actions the same way a human
would, these agents have the potential to significantly simplify human-machine
interaction, reduce resource requirements in business, and empower non-technical
users to operate computer systems effortlessly.
This thesis aims to provide an overview of the vast yet fragmented research landscape
of A2C2s, enabling further innovation. Through a comprehensive literature
review, existing agents and their capabilities were identified, summarized, categorized,
and analyzed to extract potentials and challenges.
The result is a detailed taxonomy, likened to an archipelago, encompassing providing
information to the agent, refining skills and building knowledge, ensuring task comprehensibility,
debating and refining subtasks and interacting with the environment. Besides
reviewing existing work, this thesis offers an analysis of pinnacle agents with foundational
A2C2 skills and proposes a novel architecture for a comprehensive A2C2
that leverages the identified strengths. The findings suggest that an A2C2 must be
able to decompose and structure user and system input and compare and reason
plans in a closed loop. Consequently, state-of-the-art A2C2s utilize large foundation
models because of their solid planning and image comprehension capabilities.
Promising progress in AI highlights strengths in general reasoning and image description.
Key challenges include specializing these strengths for A2C2s, specifically
reducing possible actions, decomposing instructions, and refining plans. Addressing
these issues, along with considerations for security, performance, and personalization,
is essential for future research.
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<pre><code id="codeBlock">@thesis{nobelgab_vonwareb_a2c2,
author = {Gabriel Nobel and Rebekka von Wartburg-Kottler and Pascal Sager and Thilo Stadelmann},
title = {A2C2 - Natural Language-Instructed Autonomous Agent for Computer Control},
type = {Bachelor's Thesis},
school = {Zuerich University of Applied Science},
year = {2024},
month = {June},
}</code></pre>
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