Computer Science / Artificial Intelligence

The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis

February 09, 2026

Reading time: 2 minute

...

📝 Original Info

Title: The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis
ArXiv ID: 2512.23419
Date: 2025-12-29
Authors: Alex Lewandowski, Adtiya A. Ramesh, Edan Meyer, Dale Schuurmans, Marlos C. Machado

📝 Abstract

Continual learning is often motivated by the idea, known as the big world hypothesis, that "the world is bigger" than the agent. Recent problem formulations capture this idea by explicitly constraining an agent relative to the environment. These constraints lead to solutions in which the agent continually adapts to best use its limited capacity, rather than converging to a fixed solution. However, explicit constraints can be ad hoc, difficult to incorporate, and may limit the effectiveness of scaling up the agent's capacity. In this paper, we characterize a problem setting in which an agent, regardless of its capacity, is constrained by being embedded in the environment. In particular, we introduce a computationally-embedded perspective that represents an embedded agent as an automaton simulated within a universal (formal) computer. Such an automaton is always constrained; we prove that it is equivalent to an agent that interacts with a partially observable Markov decision process over a countably infinite state-space. We propose an objective for this setting, which we call interactivity, that measures an agent's ability to continually adapt its behaviour by learning new predictions....

📄 Full Content

...(본문 내용이 길어 생략되었습니다. 사이트에서 전문을 확인해 주세요.)

The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis

📝 Original Info

📝 Abstract

📄 Full Content

Table of Contents

Table of Contents

📝 Original Info

📝 Abstract

📄 Full Content

Start searching

No results found