A multi-arm bandit neighbourhood search for routing and scheduling problems

Abstract Local search based meta-heuristics such as variable neighbourhood search have achieved remarkable success in solving complex combinatorial problems. Local search techniques are becoming increasingly popular and are used in a wide variety of meta-heuristics, such as genetic algorithms. Typically, local search iteratively improves a solution by making a series of small moves. Traditionally these methods do not employ any learning mechanism. We treat the selection of a local search neighbourhood as a dynamic multi- armed bandit (D-MAB) problem where learning techniques for solving the D-MAB can be used to guide the local search process. We present a D-MAB neighbourhood search (D-MABNS) which can be embedded within any meta- heuristic or hyperheuristic framework. Given a set of neighbourhoods, the aim of D-MABNS is to adapt the search sequence, testing promising solutions rst. We demonstrate the eectiveness of D-MABNS on two vehicle routing and scheduling problems, the real-world geographically distributed mainte- nance problem (GDMP) and the periodic vehicle routing problem (PVRP). We present comparisons to benchmark instances and give a detailed analysis of parameters, performance and behaviour. Keywords Meta-heuristic Local search Vehicle routin

Using Association Rule Mining to Predict Opponent Deck Content in Android: Netrunner

As part of their design, card games often include information that is hidden from opponents and represents a strategic advantage if discovered. A player that can discover this information will be able to alter their strategy based on the nature of that information, and therefore become a more competent opponent. In this paper, we employ association rule-mining techniques for predicting item multisets, and show them to be effective in predicting the content of Netrunner decks. We then apply different modifications based on heuristic knowledge of the Netrunner game, and show the effectiveness of techniques which consider this knowledge during rule generation and prediction

A Conceptual Framework of Business Model Emerging Resilience

In this paper we introduce an environmentally driven conceptual framework of Business Model change. Business models acquired substantial momentum in academic literature during the past decade. Several studies focused on what exactly constitutes a Business Model (role model, recipe, architecture etc.) triggering a theoretical debate about the Business Model’s components and their corresponding dynamics and relationships. In this paper, we argue that for Business Models as cognitive structures, are highly influenced in terms of relevance by the context of application, which consequently enriches its functionality. As a result, the Business Model can be used either as a role model (benchmarking) or a recipe (strategy). For that purpose, we assume that the Business Model is embedded within the economic (task) environment, and consequently affected by it. Through a typology of the environmental impact on the Business Model productivity, we introduce a conceptual framework that aims to capture the salient features of Business Model emergent resilience as reaction to two types impact: productivity constraining and disturbin

Narrative Bytes : Data-Driven Content Production in Esports

Esports - video games played competitively that are broadcast to large audiences - are a rapidly growing new form of mainstream entertainment. Esports borrow from traditional TV, but are a qualitatively different genre, due to the high flexibility of content capture and availability of detailed gameplay data. Indeed, in esports, there is access to both real-time and historical data about any action taken in the virtual world. This aspect motivates the research presented here, the question asked being: can the information buried deep in such data, unavailable to the human eye, be unlocked and used to improve the live broadcast compilations of the events? In this paper, we present a large-scale case study of a production tool called Echo, which we developed in close collaboration with leading industry stakeholders. Echo uses live and historic match data to detect extraordinary player performances in the popular esport Dota 2, and dynamically translates interesting data points into audience-facing graphics. Echo was deployed at one of the largest yearly Dota 2 tournaments, which was watched by 25 million people. An analysis of 40 hours of video, over 46,000 live chat messages, and feedback of 98 audience members showed that Echo measurably affected the range and quality of storytelling, increased audience engagement, and invoked rich emotional response among viewers

Win Prediction in Multi-Player Esports: Live Professional Match Prediction

Esports are competitive videogames watched by audiences. Most esports generate detailed data for each match that are publicly available. Esports analytics research is focused on predicting match outcomes. Previous research has emphasised pre-match prediction and used data from amateur games, which are more easily available than professional level. However, the commercial value of win prediction exists at the professional level. Furthermore, predicting real-time data is unexplored, as is its potential for informing audiences. Here we present the first comprehensive case study on live win prediction in a professional esport. We provide a literature review for win prediction in a multi-player online battle arena (MOBA) esport. The paper evaluates the first professional-level prediction models for live DotA 2 matches, one of the most popular MOBA games and trials it at a major international esports tournament. Using standard machine learning models, feature engineering and optimization, our model is up to 85\% accurate after 5 minutes of gameplay. Our analyses highlight the need for algorithm evaluation and optimization. Finally, we present implications for the esports/game analytics domains, describe commercial opportunities, practical challenges, and propose a set of evaluation criteria for research on esports win prediction