AGENTIC-SYSTEMSApril 13, 20261 min read15 views

Artifacts as Memory Beyond the Agent Boundary

Researchers have formalized a mathematical framework showing how AI agents can use environmental resources as functional memory. This approach proves that 'artifacts' within the environment can reduce the internal information needed for an agent to represent its history and learn effective policies.

Computer Science > Artificial Intelligence

Title:Artifacts as Memory Beyond the Agent Boundary

The situated view of cognition holds that intelligent behavior depends not only on internal memory, but on an agent's active use of environmental resources. Here, we begin formalizing this intuition within Reinforcement Learning (RL). We introduce a mathematical framing for how the environment can functionally serve as an agent's memory, and prove that certain observations, which we call artifacts, can reduce the information needed to represent history. We corroborate our theory with experiments showing that when agents observe spatial paths, the amount of memory required to learn a performant policy is reduced. Interestingly, this effect arises unintentionally, and implicitly through the agent's sensory stream. We discuss the implications of our findings, and show they satisfy qualitative properties previously used to ground accounts of external memory. Moving forward, we anticipate further work on this subject could reveal principled ways to exploit the environment as a substitute for explicit internal memory.

Source: arXiv cs.AI Recent

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