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

High-performance computing for data analytics

One of the main challenges in data analytics is that discovering structures and patterns in complex datasets is a computer-intensive task. Recent advances in high-performance computing provide part of the solution. Multicore systems are now more affordable and more accessible. In this paper, we investigate how this can be used to develop more advanced methods for data analytics. We focus on two specific areas: model-driven analysis and data mining using optimisation techniques

Artificial intelligence in co-operative games with partial observability

This thesis investigates Artificial Intelligence in co-operative games that feature Partial Observability. Most video games feature a combination of both co-operation, as well as Partial Observability. Co-operative games are games that feature a team of at least two agents, that must achieve a shared goal of some kind. Partial Observability is the restriction of how much of an environment that an agent can observe. The research performed in this thesis examines the challenge of creating Artificial Intelligence for co-operative games that feature Partial Observability. The main contributions are that Monte-Carlo Tree Search outperforms Genetic Algorithm based agents in solving co-operative problems without communication, the creation of a co-operative Partial Observability competition promoting Artificial Intelligence research as well as an investigation of the effect of varying Partial Observability to Artificial Intelligence, and finally the creation of a high performing Monte-Carlo Tree Search agent for the game Hanabi that uses agent modelling to rationalise about other players

STUDIES ON STOCHASTIC ALGORITHMS INFORMATION CONTENT, PARALLELISATION AND DIFFUSION INDUCED STOCHASTIC ALGORITHMS FOR GLOBAL OPTIMISATION

This thesis presents the main results of two articles published by the authors in the field of stochastic optimization. We dedicated the chapter 1 to the article introduction to the information content of some stochastic algorithms written by Esquível, Machado, Krasii, and Mota, 2021. In this chapter, we formulate an optimization stochastic algorithm convergence theorem, of Solis and Wets type, and we show several instances of its application to concrete algorithms. In this convergence theorem the algorithm is a sequence of random variables and, in order to describe the increasing flow of information associated to this sequence we define a filtration – or flow of σ-algebras – on the probability space, depending on the sequence of random variables and on the function being optimized. We compare the flow of information of two convergent algorithms by comparing the associated filtrations by means of the Cotter distance of σ-algebras. The main result is that two convergent optimization algorithms have the same information content if both their limit minimization functions generate the full σ-algebra of the probability space. The article On a Parallelised Diffusion Induced Stochastic Algorithm with Pure Random Search Steps for Global Optimisation written by Esquível, Krasii, Mota, and Machado, 2021 was broken down into 2 chapters: the chapter 2 is related to parallelisation and the chapter 3 is related to Diffusion Induced Stochastic Algorithms. In the chapter 2 we show that an adequate procedure of parallelisation of the algorithm can increase the rate of convergence, thus superseding the main drawback of the addition of the pure random search step. Finally, in the chapter 3 we propose a stochastic algorithm for global optimisation of a regular function, possibly unbounded, defined on a bounded set with regular boundary; a function that attains its extremum in the boundary of its domain of definition. The algorithm is determined by a diffusion process that is associated with the function by means of a strictly elliptic operator that ensures an adequate maximum principle. In order to preclude the algorithm to be trapped in a local extremum, we add a pure random search step to the algorithm. As the two articles have their own introductions, we decided to create a glossary that together with the annexes and appendices, include the concepts, definitions and theorems that are relevant to the understanding of the thesis