Small Decision Trees for MDPs with Deductive Synthesis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0198879" target="_blank" >RIV/00216305:26230/26:0198879 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-98679-6_8" target="_blank" >https://doi.org/10.1007/978-3-031-98679-6_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-98679-6_8" target="_blank" >10.1007/978-3-031-98679-6_8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Small Decision Trees for MDPs with Deductive Synthesis

Popis výsledku v původním jazyce

Markov decision processes (MDPs) describe decision making subject to probabilistic uncertainty. A classical problem on MDPs is to compute a policy, selecting actions in every state, that maximizes the probability of reaching a dedicated set of target states. Computing such policies in tabular form is efficiently possible via standard algorithms. However, for further processing by either humans or machines, policies should be represented concisely, e.g., as a decision tree. This paper considers finding (almost) optimal decision trees of minimal depth and contributes a deductive synthesis approach. Technically, we combine pruning the space of concise policies with an abstraction-refinement loop with an SMT-encoding that maps candidate policies into decision trees. Our experiments show that this approach beats the state-of-the-art solver using an MILP encoding by orders of magnitude. The approach also pairs well with heuristic approaches that map a fixed policy into a decision tree: for an MDP with 1.5M states, our approach reduces the size of the given tree by 90%, while sacrificing only 1% of the optimal performance.

Název v anglickém jazyce

Small Decision Trees for MDPs with Deductive Synthesis

Popis výsledku anglicky

Markov decision processes (MDPs) describe decision making subject to probabilistic uncertainty. A classical problem on MDPs is to compute a policy, selecting actions in every state, that maximizes the probability of reaching a dedicated set of target states. Computing such policies in tabular form is efficiently possible via standard algorithms. However, for further processing by either humans or machines, policies should be represented concisely, e.g., as a decision tree. This paper considers finding (almost) optimal decision trees of minimal depth and contributes a deductive synthesis approach. Technically, we combine pruning the space of concise policies with an abstraction-refinement loop with an SMT-encoding that maps candidate policies into decision trees. Our experiments show that this approach beats the state-of-the-art solver using an MILP encoding by orders of magnitude. The approach also pairs well with heuristic approaches that map a fixed policy into a decision tree: for an MDP with 1.5M states, our approach reduces the size of the given tree by 90%, while sacrificing only 1% of the optimal performance.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA23-06963S" target="_blank" >GA23-06963S: VESCAA: Verifikovatelná a efektivní syntéza kontrolerů pro autonomní agenty</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computer Aided Verification

ISBN

978-3-031-98678-9

ISSN

—

e-ISSN

—

Počet stran výsledku

24

Strana od-do

169-192

Název nakladatele

Springer Cham

Místo vydání

—

Místo konání akce

Zahreb

Datum konání akce

23. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001562506600008