Information-Theoretic Abstractions for Planning in Agents with Computational Constraints
This addresses computational constraints in planning for agents, but it appears incremental as it builds on existing concepts like anytime algorithms and bounded rationality.
The paper tackles the problem of path-planning for agents with limited computational resources by developing a framework where abstractions emerge based on available resources, and it presents theoretical results and a numerical example to show utility.
In this paper, we develop a framework for path-planning on abstractions that are not provided to the agent a priori but instead emerge as a function of the available computational resources. We show how a path-planning problem in an environment can be systematically approximated by solving a sequence of easier to solve problems on abstractions of the original space. The properties of the problem are analyzed, and a number of theoretical results are presented and discussed. A numerical example is presented to show the utility of the approach and to corroborate the theoretical findings. We conclude by providing a discussion detailing the connections of the proposed approach to anytime algorithms and bounded rationality.