Solving Canadian Traveller Problem

Tato práce se zabývá problémem kanadského cestujícího (CTP), který se dá definovat jako problém hledání nejkratší cesty ve stochastickém prostředí. V rešeršní části práce je zpracován přehled typů tohoto problému a k nim existujících metod řešení. V dalších částech se práce zaměřuje na stochastickou variantu CTP (SCTP), pro kterou jsou vybrané metody řešení (strategie) probrány více do hloubky. Zároveň jsou prezentovány i originální strategie pojmenované UCTO2 a UCTP. Dále se práce zabývá popisem okenní aplikace implementované v jazyku Java. Ta byla vyvinuta pro ověření a otestování správné funkce vybraných strategií. Nakonec jsou vyhodnoceny provedené experimenty, a z nich plynoucí srovnání vybraných strategií.This thesis deals with Canadian traveller problem (CTP), which can be defined as the shortest path problem in a stochastic environment. The overview of different CTP variants is presented in theoretical part of this thesis, as well as known solutions to these variants. In the next parts, the thesis focuses on the stochastic variation of CTP (SCTP). For this variant chosen solutions (strategies) are discussed more in depth. At the same time, the original strategies named UCTO and UCTP are presented. Further, the thesis deals with the description of a window application implemented in Java, which has been developed to validate and test the functionality of selected strategies. The final part contains experiments and comparison of selected strategies.

Solving Canadian Traveller Problem

Táto práca sa zaoberá riešením problému kanadského cestujúceho. Predstavme si, že cestujúci má mapu, na ktorej je každá cesta spojená s časom potrebným na jej prejdenie. Táto mapa však nemusí byť úplne spoľahlivá a na niektorých úsekoch môže byť tamojšími podmienkami cesty spôsobené, že čas na prejdenie daného úseku bude dlhší, alebo že priechod bude nemožný. Práca sa zaoberá prehľadom typov tohto problému a ich riešeniami. Ďalej sa zaoberá popisom dvoch aplikácií implementovaných v jazyku Python, ktoré slúžia na overenie daných stratégií riešenia problému. Na záver sú vyhodnotené dané experimenty a porovnaná efektivita daných stratégií.This thesis deals with Canadian traveller problem. Imagine a traveller that have a map on which every road is associated with time that is needed to get through this road. Hovewer, this map may not be totally reliable, and the time needed to pass through on some of the roads may be different due to bad road conditions, or the pass will be impossible. This thesis deals with type overview of this problem and the solutions. Further, the thesis deals with the description of two applications implemented in Python, which serves on verification of the strategies. The final part contains experiments and comparison of effectiveness of selected strategies.

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Path planning for mobile robots in the real world: handling multiple objectives, hierarchical structures and partial information

Autonomous robots in real-world environments face a number of challenges even to accomplish apparently simple tasks like moving to a given location. We present four realistic scenarios in which robot navigation takes into account partial information, hierarchical structures, and multiple objectives. We start by discussing navigation in indoor environments shared with people, where routes are characterized by effort, risk, and social impact. Next, we improve navigation by computing optimal trajectories and implementing human-friendly local navigation behaviors. Finally, we move to outdoor environments, where robots rely on uncertain traversability estimations and need to account for the risk of getting stuck or having to change route

Repeated-Task Canadian Traveler Problem

In the Canadian Traveler Problem (CTP) a traveling agent is given a weighted graph, where some of the edges may be blocked, with a known probability. The agent needs to travel to a given goal. A solution for CTP is a policy, that has the smallest expected traversal cost. CTP is intractable. Previous work has focused on the case of a single agent. We generalize CTP to a repeated task version where a number of agents need to travel to the same goal, minimizing their combined travel cost. We provide optimal algorithms for the special case of disjoint path graphs. Based on a previous UCT-based approach for the single agent case, a framework is developed for the multi-agent case and four variants are given - two of which are based on the results for disjoint-path graphs. Empirical results show the benefits of the suggested framework and the resulting heuristics. For small graphs where we could compare to optimal policies, our approach achieves near optimal results at only a fraction of the computation cost

Repeated-task Canadian traveler problem

In the Canadian Traveler Problem (CTP) a traveling agent is given a graph, where some of the edges may be blocked, with a known probability. A solution for CTP is a policy, that has the smallest expected traversal cost. CTP is intractable. Previous work has focused on the case of a single agent. We generalize CTP to a repeated task version where a number of agents need to travel to the same goal, minimizing their combined travel cost. We provide optimal algorithms for the special case of disjoint path graphs. Based on a previous UCT-based approach for the single agent case, a framework is developed for the multi-agent case and four variants are given- two of which are based on the results for disjoint-path graphs. Empirical results show the benefits of the suggested framework and the resulting heuristics. For small graphs where we could compare to optimal policies, our approach achieves near-optimal results at only a fraction of the computation cost