Monte-Carlo Search in Games

This paper implements and analyzes four algorithms for improving computer play of the board game Go. These algorithms use machine pattern learning to find better Monte-Carlo simulation policies for use with Monte-Carlo Tree Search. Two of these algorithms maximize individual move strength, and two minimize overall simulation error. These algorithms are tested using UCT on 9x9 Go with 3x3 patterns

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

Improving democratic governance through institutional design: civic participation and democratic ownership in Europe

In this article we provide a conceptual and argumentative framework for studying how institutional design can enhance civic participation and ultimately increase citizens’ sense of democratic ownership of governmental processes. First, we set out the socio-political context for enhancing the democratic governance of regulatory policies in Europe, and highlight the way in which civic participation and democratic ownership is given equal weight to economic competitiveness. We then discuss the potential for institutionalised participatory governance to develop and their prospects for improving effective and democratic governance in the multi-layered European polity. The article concludes by outlining a research agenda for the field and identifying the priorities for scholars working in interaction with civil society and governments

Soil Sampling at Sword Beach – Luc-Sur-Mer, France, 1943: How Geotechnical Engineering Influenced the D-Day Invasion and Directed the Course of Modern History

This paper presents an historical account of covert soil sampling operations conducted by the British Royal Navy’s No. 1 Combined Operations Pilotage and Beach Reconnaissance Party on December 31, 1943, near Luc-sur-Mer, France at the beach later given the codename “Sword.” With the tactical goal of determining whether the beach sand would support heavy invasion craft such as tanks, trucks, and bulldozers, this commando-style mission provided the field data by which the Supreme Allied Command established the site for the main landing beaches where the initial assault phase of Operation Overlord, the Allied invasion of Normandy, took place on June 6, 1944. Incorporated into a site characterization lecture, this case study illustrates soil exploration methodology and introduces students to the nature, practice and significance of geotechnical engineering as a profession that can directly influence world events and even the course of modern history

Intelligent Traffic Management: From Practical Stochastic Path Planning to Reinforcement Learning Based City-Wide Traffic Optimization

This research focuses on intelligent traffic management including stochastic path planning and city scale traffic optimization. Stochastic path planning focuses on finding paths when edge weights are not fixed and change depending on the time of day/week. Then we focus on minimizing the running time of the overall procedure at query time utilizing precomputation and approximation. The city graph is partitioned into smaller groups of nodes and represented by its exemplar. In query time, source and destination pairs are connected to their respective exemplars and the path between those exemplars is found. After this, we move toward minimizing the city wide traffic congestion by making structural changes include changing the number of lanes, using ramp metering, varying speed limit, and modifying signal timing is possible. We propose a multi agent reinforcement learning (RL) framework for improving traffic flow in city networks. Our framework utilizes two level learning: a) each single agent learns the initial policy and b) multiple agents (changing the environment at the same time) update their policy based on the interaction with the dynamic environment and in agreement with other agents. The goal of RL agents is to interact with the environment to learn the optimal modification for each road segment through maximizing the cumulative reward over the set of possible actions in state space