Approximating n-player behavioural strategy nash equilibria using coevolution

Coevolutionary algorithms are plagued with a set of problems related to intransitivity that make it questionable what the end product of a coevolutionary run can achieve. With the introduction of solution concepts into coevolution, part of the issue was alleviated, however efficiently representing and achieving game theoretic solution concepts is still not a trivial task. In this paper we propose a coevolutionary algorithm that approximates behavioural strategy Nash equilibria in n-player zero sum games, by exploiting the minimax solution concept. In order to support our case we provide a set of experiments in both games of known and unknown equilibria. In the case of known equilibria, we can confirm our algorithm converges to the known solution, while in the case of unknown equilibria we can see a steady progress towards Nash. Copyright 2011 ACM

Fast Approximate Max-n Monte Carlo Tree Search for Ms Pac-Man

We present an application of Monte Carlo tree search (MCTS) for the game of Ms Pac-Man. Contrary to most applications of MCTS to date, Ms Pac-Man requires almost real-time decision making and does not have a natural end state. We approached the problem by performing Monte Carlo tree searches on a five player maxn tree representation of the game with limited tree search depth. We performed a number of experiments using both the MCTS game agents (for pacman and ghosts) and agents used in previous work (for ghosts). Performance-wise, our approach gets excellent scores, outperforming previous non-MCTS opponent approaches to the game by up to two orders of magnitude. © 2011 IEEE

Viewpoint: Artificial Intelligence and Labour

The welfare of modern societies has been intrinsically linked to wage labour. With some exceptions, the modern human has to sell her labour-power to be able reproduce biologically and socially. Thus, a lingering fear of technological unemployment features predominately as a theme among Artificial Intelligence researchers. In this short paper we show that, if past trends are anything to go by, this fear is irrational. On the contrary, we argue that the main problem humanity will be facing is the normalisation of extremely long working hours

Propagation of a gravity current in a two‐layer stratified environment

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94807/1/jgrc10098.pd

On monte carlo tree search and reinforcement learning

Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved widespread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been outlined in the past; however, the use of RL techniques within tree search has not been thoroughly studied yet. In this paper we re-examine in depth this close relation between the two fields; our goal is to improve the cross-awareness between the two communities. We show that a straightforward adaptation of RL semantics within tree search can lead to a wealth of new algorithms, for which the traditional MCTS is only one of the variants. We confirm that planning methods inspired by RL in conjunction with online search demonstrate encouraging results on several classic board games and in arcade video game competitions, where our algorithm recently ranked first. Our study promotes a unified view of learning, planning, and search

Knowledge-based fast evolutionary MCTS for general video game playing

General Video Game Playing is a game AI domain in which the usage of game-dependent domain knowledge is very limited or even non existent. This imposes obvious difficulties when seeking to create agents able to play sets of different games. Taken more broadly, this issue can be used as an introduction to the field of General Artificial Intelligence. This paper explores the performance of a vanilla Monte Carlo Tree Search algorithm, and analyzes the main difficulties encountered when tackling this kind of scenarios. Modifications are proposed to overcome these issues, strengthening the algorithm's ability to gather and discover knowledge, and taking advantage of past experiences. Results show that the performance of the algorithm is significantly improved, although there remain unresolved problems that require further research. The framework employed in this research is publicly available and will be used in the General Video Game Playing competition at the IEEE Conference on Computational Intelligence and Games in 2014

Convolutional-Match Networks for Question Answering

In this paper, we present a simple, yet effective, attention and memory mechanism that is reminis- cent of Memory Networks and we demonstrate it in question-answering scenarios. Our mechanism is based on four simple premises: a) memories can be formed from word sequences by using convo- lutional networks; b) distance measurements can be taken at a neuronal level; c) a recursive soft- max function can be used for attention; d) extensive weight sharing can help profoundly. We achieve state-of-the-art results in the bAbI tasks, outper- forming Memory Networks and the Differentiable Neural Computer, both in terms of accuracy and stability (i.e. variance) of results

Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice

Hyperparameters play a critical role in machine learning. Hyperparameter tuning can make the difference between state-of-the-art and poor prediction performance for any algorithm, but it is particularly challenging for structure learning due to its unsupervised nature. As a result, hyperparameter tuning is often neglected in favour of using the default values provided by a particular implementation of an algorithm. While there have been numerous studies on performance evaluation of causal discovery algorithms, how hyperparameters affect individual algorithms, as well as the choice of the best algorithm for a specific problem, has not been studied in depth before. This work addresses this gap by investigating the influence of hyperparameters on causal structure learning tasks. Specifically, we perform an empirical evaluation of hyperparameter selection for some seminal learning algorithms on datasets of varying levels of complexity. We find that, while the choice of algorithm remains crucial to obtaining state-of-the-art performance, hyperparameter selection in ensemble settings strongly influences the choice of algorithm, in that a poor choice of hyperparameters can lead to analysts using algorithms which do not give state-of-the-art performance for their data.Comment: 26 pages, 16 figure

Single-Trial EEG Classification with EEGNet and Neural Structured Learning for Improving BCI Performance

Research and development of new machine learning techniques to augment the performance of Brain-computer Interfaces (BCI) have always been an open area of interest among researchers. The need to develop robust and generalised classifiers has been one of the vital requirements in BCI for realworld application. EEGNet is a compact CNN model that had been reported to be generalised for different BCI paradigms. In this paper, we have aimed at further improving the EEGNet architecture by employing Neural Structured Learning (NSL) that taps into the relational information within the data to regularise the training of the neural network. This would allow the EEGNet to make better predictions while maintaining the structural similarity of the input. In addition to better performance, the combination of EEGNet and NSL is more robust, works well with smaller training samples and requires on separate feature engineering, thus saving the computational cost. The proposed approach had been tested on two standard motor imagery datasets: the first being a two-class motor imagery dataset from Graz University and the second is the 4-class Dataset 2a from BCI competition 2008. The accuracy has shown that our combined EEGNet an NSL approach is superior to the sole EEGNet model