This post introduces an informal framework for understanding intelligent agents as "belief webs." This model integrates concepts from active inference, agent foundations, and machine learning, proposing that beliefs, goals, and actions are interconnected facets of a single phenomenon. The framework suggests that an agent's beliefs are typically locally consistent but not necessarily globally consistent, a challenge for models relying on a single probability distribution. It draws inspiration from probabilistic dependency graphs (PDGs) and Garrabrant induction to handle such inconsistencies and incorporates hierarchical concept formation, similar to deep learning architectures. AI
IMPACT Proposes a new conceptual framework for understanding AI agent architecture and decision-making processes.
RANK_REASON The item is a blog post discussing a theoretical framework for AI agents, not a formal research paper or a new model release. [lever_c_demoted from research: ic=1 ai=1.0]
- Abram Demski
- Active Inference
- Agent Foundations
- deep learning
- Garrabrant induction
- Less Wrong
- machine learning
- PDGs
- probabilistic dependency graphs
- Richardson
- Solomonoff Induction
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
