Reasoning about Unforeseen Possibilities During Policy Learning

TL;DR

This paper introduces a model for autonomous agents that can discover and learn to utilize unforeseen possibilities during policy learning, combining probabilistic and symbolic reasoning to adapt to new information.

Contribution

It presents a novel approach enabling agents to identify and exploit previously unknown possibilities through interaction and expert communication.

Findings

Agent converges on optimal policies despite initial unawareness of key factors.

Combines probabilistic and symbolic reasoning for comprehensive decision problem estimation.

Demonstrates adaptability in dynamic, uncertain environments.

Abstract

Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is known in advance and does not change during learning. This is an unrealistic assumption in many scenarios, because new evidence can reveal important information about what is possible, possibilities that the agent was not aware existed prior to learning. We present a model of an agent which both discovers and learns to exploit unforeseen possibilities using two sources of evidence: direct interaction with the world and communication with a domain expert. We use a combination of probabilistic and symbolic reasoning to estimate all components of the decision problem, including its set of random variables and their causal dependencies. Agent simulations show that the agent converges on optimal polices even when it starts out unaware of factors that are critical to behaving optimally.