|
|
Advanced Methods for Synthesis of Probabilistic Programs
Stupinský, Šimon ; Holík, Lukáš (oponent) ; Češka, Milan (vedoucí práce)
Probabilistic programs play a crucial role in various engineering domains, including computer networks, embedded systems, power management policies, or software product lines. PAYNT is a tool for the automatic synthesis of probabilistic programs satisfying the given specifications. In this thesis, we extend this tool primarily to support optimal synthesis and synthesis for multi-property specifications. Further, we have proposed and implemented a new method that can efficiently synthesise continuous parameters affecting the transition probabilities alongside the synthesis of a program topology, i.e., support of both topology and parameter synthesis at the same time. We demonstrate the usefulness and performance of PAYNT on a wide range of real-world case studies from various application domains. For challenging synthesis problems, PAYNT can significantly decrease the run-time from days to minutes while ensuring the completeness of the synthesis process.
|
|
|
Using Counter-Examples in Controller Synthesis for POMDPs
Frejlach, Jakub ; Síč, Juraj (oponent) ; Češka, Milan (vedoucí práce)
This thesis examines partially observable Markov decision processes (POMDPs), a prominent stochastic model for decision-making under uncertainty and partial observability. POMDPs have diverse applications, from robot navigation to self-driving vehicles. The undecidable control problem of POMDPs has led to various approaches, including finite-state controllers (FSCs) based on observations and history. Identifying small and verifiable FSCs reduces the synthesis of Markov chains. This thesis focuses on counterexample-guided inductive synthesis (CEGIS) within the PAYNT program, exploring the use of Markov decision processes as counterexamples. A new greedy method for constructing counterexamples is outlined and implemented in PAYNT, showing improvements in some cases compared to the existing method.
|
|
|
Improving Synthesis of Finite State Controllers for POMDPs Using Belief Space Approximation
Macák, Filip ; Holík, Lukáš (oponent) ; Češka, Milan (vedoucí práce)
This work focuses on combining two state-of-the-art controller synthesis methods for partially observable Markov decision processes (POMDPs), a prominent model in sequential decision making under uncertainty. A central issue is to find a POMDP controller that achieves a total expected reward objective. As finding optimal controllers is undecidable, we concentrate on synthesising good finite-state controllers (FSCs). We do so by tightly integrating two modern, orthogonal methods for POMDP controller synthesis: a belief-based and an inductive approach. The former method obtains an FSC from a finite fragment of the so-called belief MDP, an MDP that keeps track of the probabilities of equally observable POMDP states. The latter is an inductive search technique over a set of FSCs with a fixed memory size. The key result of this work is a symbiotic anytime algorithm that tightly integrates both approaches such that each profits from the controllers constructed by the other. Experimental results indicate a substantial improvement in the value of the controllers while significantly reducing the synthesis time and memory footprint.
|
|
|
Advanced Methods for Synthesis of Probabilistic Programs
Stupinský, Šimon ; Holík, Lukáš (oponent) ; Češka, Milan (vedoucí práce)
Probabilistic programs play a crucial role in various engineering domains, including computer networks, embedded systems, power management policies, or software product lines. PAYNT is a tool for the automatic synthesis of probabilistic programs satisfying the given specifications. In this thesis, we extend this tool primarily to support optimal synthesis and synthesis for multi-property specifications. Further, we have proposed and implemented a new method that can efficiently synthesise continuous parameters affecting the transition probabilities alongside the synthesis of a program topology, i.e., support of both topology and parameter synthesis at the same time. We demonstrate the usefulness and performance of PAYNT on a wide range of real-world case studies from various application domains. For challenging synthesis problems, PAYNT can significantly decrease the run-time from days to minutes while ensuring the completeness of the synthesis process.
|
host ::
RSS.