On Similarities between Inference in Game Theory and Machine Learning

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution)

Decentralised Monte Carlo Tree Search for Active Perception

We propose a decentralised variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimise its own individual action space by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of these search trees, which are used to update the locally-stored joint distributions using an optimisation approach inspired by variational methods. Our method admits any objective function defined over robot actions, assumes intermittent communication, and is anytime. We extend the analysis of the standard MCTS for our algorithm and characterise asymptotic convergence under reasonable assumptions. We evaluate the practical performance of our method for generalised team orienteering and active object recognition using real data, and show that it compares favourably to centralised MCTS even with severely degraded communication. These examples support the relevance of our algorithm for real-world active perception with multi-robot systems

Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning

A large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference learning framework. Specifically, a preference learning problem is seen as a two-players zero-sum game. An algorithm is proposed to incrementally include new useful features into the hypothesis. This can be particularly important when dealing with a very large number of potential features like, for instance, in relational learning and rule extraction. A game theoretical analysis is used to demonstrate the convergence of the algorithm. Furthermore, leveraging on the natural analogy between features and rules, the resulting models can be easily interpreted by humans. An extensive set of experiments on classification tasks shows the effectiveness of the proposed method in terms of interpretability and feature selection quality, with accuracy at the state-of-the-art.Comment: AAAI 201

Simultaneity Modeling Analysis of the Environmental Kuznets Curve Hypothesis

The environmental Kuznets curve (EKC) hypothesis has been recognized in the environmental economics literature since the 1990's. Various statistical tests have been used on time series, cross section and panel data related to single and groups of countries to validate this hypothesis. In the literature, the validation has always been conducted by using a single equation. However, since both the environment and income variables are endogenous, the estimation of a single equation model when simultaneity exists produces inconsistent and biased estimates. Therefore, we formulate simultaneous two-equation models to investigate the EKC hypothesis for fifty-six countries, using annual panel data from 1990 to 2012, with the end year is determined by data availability for the panel. To make the panel data analysis more homogeneous, we investigate this issue for a three income-based panels (namely, high-, middle-, and low-income panels) given several explanatory variables. Our results indicate that there exists a bidirectional causality between economic growth and pollution emissions in the overall panels. We also find that the relationship is nonlinear and has an inverted U-shape for all the considered panels. Policy implications are provided

Simultaneity Modeling Analysis of the Environmental Kuznets Curve Hypothesis

The environmental Kuznets curve (EKC) hypothesis has been recognized in the environmental economics literature since the 1990's. Various statistical tests have been used on time series, cross section and panel data related to single and groups of countries to validate this hypothesis. In the literature, the validation has always been conducted by using a single equation. However, since both the environment and income variables are endogenous, the estimation of a single equation model when simultaneity exists produces inconsistent and biased estimates. Therefore, we formulate simultaneous two-equation models to investigate the EKC hypothesis for fifty-six countries, using annual panel data from 1990 to 2012, with the end year is determined by data availability for the panel. To make the panel data analysis more homogeneous, we investigate this issue for a three income-based panels (namely, high-, middle-, and low-income panels) given several explanatory variables. Our results indicate that there exists a bidirectional causality between economic growth and pollution emissions in the overall panels. We also find that the relationship is nonlinear and has an inverted U-shape for all the considered panels. Policy implications are provided

Literature survey on the relationships between energy variables, environment and economic growth

This paper provides an extensive survey of the great progress in the literature of energy- environment-growth nexus for both specific- and multi-county studies covering the period from 1978 to 2014. The survey focuses on country (ies) coverage, periods, modeling methodologies, and empirical conclusions. Our survey is based on the direction of causality between (i)energy consumption (electricity, nuclear, renewable and non-renewable) and economic growth; (ii) between economic growth and environment; and between the three variables at the same time. As a general remark from these studies is that the literature produced paradoxical and not conclusive results which energy consumption can boost economic growth through the productivity enhancement and it can boost also the environmental damages through the enhancement of pollutant emissions. This survey gives researchers a ‘snap shot’ of the literature on the causality between the four types of energy, environment and economic growth for both individual and collective cases. Understanding the causal links between environment, economic growth and different types of energy consumption provides a basis for discussion in order to design and implementating effective energy and environmental policies

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

A Development of a Game-Theoretic Artificially Intelligent Neural Network Revenue Management Forecasting Model

The aim of this dissertation is to create and test a risk induced game-theoretic price forecasting model. The models were tested with datasets from 3 Upper Midscale hotels in 3 locations (urban, interstate and suburb), one hotel from each location. The data was obtained from STR, a leading hospitality marketing company which consolidates all of the daily hotel data from hotels in the United States. Multiple error measures were used to compare the accuracy of models. Three LSTM models were proposed and tested; LSTM model 1 that relied on ADR to forecast ADR, LSTM model 2 that used ADR, supply, demand, and day of the week to generate the forecast, and finally LSTM model 3 that used all the predictors of LSTM model 2 plus ADR of 4 competitors of the same size and scale to predict ADR values. The LSTM models were tested against traditional forecasting methods. The findings showed that LSTM model 2 was the most accurate of all the models tested. Moreover, LSTM model 1 and 3 showed higher accuracy than traditional models in some cases. In particular, all the LSTM models outperformed the traditional methods in the most volatile property (property C). Overall, the results indicated the higher accuracy of LSTM models for times of uncertainty. Finally, estimation of Value at Risk was introduced into the LSTM models, however the accuracy of the models did not change significantly