AIFeb 15, 2024

Agents Need Not Know Their Purpose

arXiv:2402.09734v1h-index: 1
Originality Highly original
AI Analysis

This addresses the alignment challenge for AI safety by introducing a novel architectural approach that improves alignment with human values as agents become more intelligent, potentially reducing risks in advanced AI systems.

The paper tackles the AI alignment problem by proposing oblivious agents that aggregate known and hidden utility sub-functions, showing that these agents infer and maximize alignment with human intentions as their intelligence increases, in contrast to prior methods where alignment degrades.

Ensuring artificial intelligence behaves in such a way that is aligned with human values is commonly referred to as the alignment challenge. Prior work has shown that rational agents, behaving in such a way that maximizes a utility function, will inevitably behave in such a way that is not aligned with human values, especially as their level of intelligence goes up. Prior work has also shown that there is no "one true utility function"; solutions must include a more holistic approach to alignment. This paper describes oblivious agents: agents that are architected in such a way that their effective utility function is an aggregation of a known and hidden sub-functions. The hidden component, to be maximized, is internally implemented as a black box, preventing the agent from examining it. The known component, to be minimized, is knowledge of the hidden sub-function. Architectural constraints further influence how agent actions can evolve its internal environment model. We show that an oblivious agent, behaving rationally, constructs an internal approximation of designers' intentions (i.e., infers alignment), and, as a consequence of its architecture and effective utility function, behaves in such a way that maximizes alignment; i.e., maximizing the approximated intention function. We show that, paradoxically, it does this for whatever utility function is used as the hidden component and, in contrast with extant techniques, chances of alignment actually improve as agent intelligence grows.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes