Gamification of Smart Meter Home Display Units Using Targeted Reward Mechanics.

This research is part of a TSB funded feasibility study (ARIA) and is the preliminary reward based design concept for a game to accompany electricity Smart Meters’ Home Display Units. The purpose is to engage players in the game for an extended period so they would similarly engage in using their Home Display Units in order to lower their electricity consumption and modify their behaviour. This paper looks at reward within computer games and establishes a visual framework for displaying the reward mechanics used in games in the form of “Three Corners of Reward”. It establishes the link between personal reward, material reward and competitive reward through three drivers, intrinsic, extrinsic and social play which is then linked to player demographics. It this case the target market require a spread of male and female, but majority female with a broad appeal to many age groups

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

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication

CAPIR: Collaborative Action Planning with Intention Recognition

We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.Comment: 6 pages, accepted for presentation at AIIDE'1

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

How group identification distorts beliefs

This paper investigates how group identification distorts people’s beliefs about the ability of their peers in social groups. We find that experimentally manipulated identification with a randomly composed group leads to overconfident beliefs about fellow group members’ performance on an intelligence test. This result cannot be explained by individual overconfidence, i.e., participants overconfident in their own skill believing that their group performed better because of them, as this was ruled out by experimental design. Moreover, we find that participants with stronger group identification put more weight on positive signals about their group when updating their beliefs. These in-group biases in beliefs can have important economic consequences when group membership is used to make inference about an individual’s characteristics as, for instance, in hiring decisions