Q-Search Trees: An Information-Theoretic Approach Towards Hierarchical Abstractions for Agents with Computational Limitations
This addresses the challenge of efficient decision-making for agents with computational constraints, though it appears incremental as it builds on information-theoretic compression concepts.
The paper tackles the problem of generating hierarchical abstractions for decision-making agents with limited computational resources by formulating a novel optimization problem for tree-based abstractions, and demonstrates the framework on a non-trivial environment.
In this paper, we develop a framework to obtain graph abstractions for decision-making by an agent where the abstractions emerge as a function of the agent's limited computational resources. We discuss the connection of the proposed approach with information-theoretic signal compression, and formulate a novel optimization problem to obtain tree-based abstractions as a function of the agent's computational resources. The structural properties of the new problem are discussed in detail, and two algorithmic approaches are proposed to obtain solutions to this optimization problem. We discuss the quality of, and prove relationships between, solutions obtained by the two proposed algorithms. The framework is demonstrated to generate a hierarchy of abstractions for a non-trivial environment.