Theory of Machine Networks: A Case Study
Rooz Mahdavian, Richard Diehl Martinez
TL;DR
This paper introduces a simplified architecture for modeling complex deterministic machines as proxies for understanding nondeterministic, conscious entities, validated through experiments on understanding engines.
Contribution
It presents a new simplified architecture for Theory-of-Mind Networks, focusing on complex deterministic machines as proxies for conscious entities.
Findings
Validated architecture on understanding engines.
Demonstrated meaningful abstractions of complex systems.
Proposed a simplified approach to Theory-of-Mind modeling.
Abstract
We propose a simplification of the Theory-of-Mind Network architecture, which focuses on modeling complex, deterministic machines as a proxy for modeling nondeterministic, conscious entities. We then validate this architecture in the context of understanding engines, which, we argue, meet the required internal and external complexity to yield meaningful abstractions.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBayesian Modeling and Causal Inference · Advanced Graph Neural Networks · Evolutionary Game Theory and Cooperation