Computing Complexity-aware Plans Using Kolmogorov Complexity
Elis Stefansson, Karl H. Johansson

TL;DR
This paper proposes a novel complexity-aware planning method using Kolmogorov complexity to balance policy performance and simplicity in deterministic automata, with algorithms for low-complexity policy synthesis.
Contribution
It introduces a new planning objective based on Kolmogorov complexity and develops algorithms to find low-complexity policies balancing performance and simplicity.
Findings
Algorithms produce low-complexity policies consistent with intuition.
The approach effectively balances policy performance and complexity.
Evaluations on a navigation task demonstrate practical applicability.
Abstract
In this paper, we introduce complexity-aware planning for finite-horizon deterministic finite automata with rewards as outputs, based on Kolmogorov complexity. Kolmogorov complexity is considered since it can detect computational regularities of deterministic optimal policies. We present a planning objective yielding an explicit trade-off between a policy's performance and complexity. It is proven that maximising this objective is non-trivial in the sense that dynamic programming is infeasible. We present two algorithms obtaining low-complexity policies, where the first algorithm obtains a low-complexity optimal policy, and the second algorithm finds a policy maximising performance while maintaining local (stage-wise) complexity constraints. We evaluate the algorithms on a simple navigation task for a mobile robot, where our algorithms yield low-complexity policies that concur with…
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
TopicsOptimization and Search Problems · Computability, Logic, AI Algorithms · Machine Learning and Algorithms
