Artificial Open World for Evaluating AGI: a Conceptual Design
Bowen Xu, Quansheng Ren

TL;DR
This paper introduces the concept of an Artificial Open World designed to evaluate AGI by avoiding developer bias and ensuring the agent encounters truly novel problems, with a focus on conceptual design and future formalization.
Contribution
It proposes a novel evaluation framework called Artificial Open World that aims to better assess AGI by preventing developer influence and promoting genuine novelty in problem-solving.
Findings
Conceptual framework for Artificial Open World proposed
A metric for measuring AGI progress introduced
Design principles to ensure world openness discussed
Abstract
How to evaluate Artificial General Intelligence (AGI) is a critical problem that is discussed and unsolved for a long period. In the research of narrow AI, this seems not a severe problem, since researchers in that field focus on some specific problems as well as one or some aspects of cognition, and the criteria for evaluation are explicitly defined. By contrast, an AGI agent should solve problems that are never-encountered by both agents and developers. However, once a developer tests and debugs the agent with a problem, the never-encountered problem becomes the encountered problem, as a result, the problem is solved by the developers to some extent, exploiting their experience, rather than the agents. This conflict, as we call the trap of developers' experience, leads to that this kind of problems is probably hard to become an acknowledged criterion. In this paper, we propose an…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Reinforcement Learning in Robotics · Machine Learning and Algorithms
