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GPU-Accelerated Synthesis of Probabilistic Programs
Marcin, Vladimír ; Matyáš, Jiří (referee) ; Češka, Milan (advisor)
V tejto práci sa zoberáme problémom automatizovanej syntézy pravdepodobnostných programov: majme konečnú rodinu kandidátnych programov, v ktorej chceme efektívne identifikovať program spĺňajúci danú špecifikáciu. Aj riešenie tých najjednoduchších syntéznych problémov v praxi predstavuje NP-ťažký problém. Pokrok v tejto oblasti prináša nástroj Paynt, ktorý na riešenie tohto problému používa novú integrovanú metódu syntézy pravdepodobnostných programov. Aj keď sa tento prístup dokáže efektívne vysporiadať s exponenciálnym rastom rodín kandidátnych riešení, stále tu existuje problém spôsobený exponenciálnym rastom jednotlivých členov týchto rodín. S cieľom vysporiadať sa aj s týmto problémom, sme implementovali GPU orientované algoritmy slúžiace na overovanie kandidátnych programov (modelov), ktoré danú úlohu paralelizujú na stavovej úrovni pravdepodobnostých modelov. Celkové zrýchlenie doshiahnuté týmto prístupom za určitých podmienok potom prinieslo takmer teoretický limit možného zrýchlenia syntézneho procesu.
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Intelligent Reactive Agent for the Game Ms.Pacman
Bložoňová, Barbora ; Zbořil, František (referee) ; Drahanský, Martin (advisor)
This thesis focuses on artificial intelligence for difficult decision problemes such as the game with uncertainty Ms. Pacman. The aim of this work is to design and implement intelligent reactive agent using a method from the field of reinforcement learning, demonstrate it on visual demo Ms.Pacman and compare its intelligence with well-known informed methods of playing games (Minimax, AlfaBeta Pruning, Expectimax). The thesis is primarily structured into two parts. The theoretical part deals with adversarial search (in games), reactivity of agent and possibilities of machine learning, all in the context of Ms. Pacman. The second part addresses the design of agent's versions behaviour implementation and finally its comparison to other methods of adversarial search problem, evaluation of results and a few ideas for future improvements.
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Stochastic Dynamic Programming Problems: Theory and Applications.
Lendel, Gabriel ; Sladký, Karel (advisor) ; Lachout, Petr (referee)
Title: Stochastic Dynamic Programming Problems: Theory and Applications Author: Gabriel Lendel Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Karel Sladký CSc. Supervisor's e-mail address: sladky@utia.cas.cz Abstract: In the present work we study Markov decision processes which provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. We study iterative procedures for finding policy that is optimal or nearly optimal with respect to the selec- ted criteria. Specifically, we mainly examine the task of finding a policy that is optimal with respect to the total expected discounted reward or the average expected reward for discrete or continuous systems. In the work we study policy iteration algorithms and aproximative value iteration algorithms. We give numerical analysis of specific problems. Keywords: Stochastic dynamic programming, Markov decision process, policy ite- ration, value iteration
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GPU-Accelerated Synthesis of Probabilistic Programs
Marcin, Vladimír ; Matyáš, Jiří (referee) ; Češka, Milan (advisor)
V tejto práci sa zoberáme problémom automatizovanej syntézy pravdepodobnostných programov: majme konečnú rodinu kandidátnych programov, v ktorej chceme efektívne identifikovať program spĺňajúci danú špecifikáciu. Aj riešenie tých najjednoduchších syntéznych problémov v praxi predstavuje NP-ťažký problém. Pokrok v tejto oblasti prináša nástroj Paynt, ktorý na riešenie tohto problému používa novú integrovanú metódu syntézy pravdepodobnostných programov. Aj keď sa tento prístup dokáže efektívne vysporiadať s exponenciálnym rastom rodín kandidátnych riešení, stále tu existuje problém spôsobený exponenciálnym rastom jednotlivých členov týchto rodín. S cieľom vysporiadať sa aj s týmto problémom, sme implementovali GPU orientované algoritmy slúžiace na overovanie kandidátnych programov (modelov), ktoré danú úlohu paralelizujú na stavovej úrovni pravdepodobnostých modelov. Celkové zrýchlenie doshiahnuté týmto prístupom za určitých podmienok potom prinieslo takmer teoretický limit možného zrýchlenia syntézneho procesu.
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Stochastic Dynamic Programming Problems: Theory and Applications.
Lendel, Gabriel ; Sladký, Karel (advisor) ; Lachout, Petr (referee)
Title: Stochastic Dynamic Programming Problems: Theory and Applications Author: Gabriel Lendel Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Karel Sladký CSc. Supervisor's e-mail address: sladky@utia.cas.cz Abstract: In the present work we study Markov decision processes which provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. We study iterative procedures for finding policy that is optimal or nearly optimal with respect to the selec- ted criteria. Specifically, we mainly examine the task of finding a policy that is optimal with respect to the total expected discounted reward or the average expected reward for discrete or continuous systems. In the work we study policy iteration algorithms and aproximative value iteration algorithms. We give numerical analysis of specific problems. Keywords: Stochastic dynamic programming, Markov decision process, policy ite- ration, value iteration
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Intelligent Reactive Agent for the Game Ms.Pacman
Bložoňová, Barbora ; Zbořil, František (referee) ; Drahanský, Martin (advisor)
This thesis focuses on artificial intelligence for difficult decision problemes such as the game with uncertainty Ms. Pacman. The aim of this work is to design and implement intelligent reactive agent using a method from the field of reinforcement learning, demonstrate it on visual demo Ms.Pacman and compare its intelligence with well-known informed methods of playing games (Minimax, AlfaBeta Pruning, Expectimax). The thesis is primarily structured into two parts. The theoretical part deals with adversarial search (in games), reactivity of agent and possibilities of machine learning, all in the context of Ms. Pacman. The second part addresses the design of agent's versions behaviour implementation and finally its comparison to other methods of adversarial search problem, evaluation of results and a few ideas for future improvements.
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Ramseův růstový model za neurčitosti
Sladký, Karel
We consider an extended version of the Ramsey growth model under stochastic uncertaity modelled by Markov processes. In contrast to the standard model we assume that splitting of production between consumption and capital accumulation is influenced by some random factor, e.g. governed by transition probabilities depending on the current value of the accumulated capital, along with possible interventions of the decision maker. Basic properties of the standation formulation are summarized and compared with their counterpart in the extended version. Finding optimal policy of the extended model can be either performed by additional compensation of the (random) disturbances or can be also formulated as finding optimal control of a Markov decision process.
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