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arXiv · 2109.10303

Computing Complexity-aware Plans Using Kolmogorov Complexity

Elis Stefansson·Karl H. Johansson
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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 intuition.

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Elis Stefansson, Karl H. Johansson. 2021-09-21. Computing Complexity-aware Plans Using Kolmogorov Complexity. https://arxiv.org/abs/2109.10303

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