Reaching Your Goal Optimally by Playing at Random with No Memory

Shortest-path games are two-player zero-sum games played on a graph equipped with integer weights. One player, that we call Min, wants to reach a target set of states while minimising the total weight, and the other one has an antagonistic objective. This combination of a qualitative reachability objective and a quantitative total-payoff objective is one of the simplest settings where Min needs memory (pseudo-polynomial in the weights) to play optimally. In this article, we aim at studying a tradeoff allowing Min to play at random, but using no memory. We show that Min can achieve the same optimal value in both cases. In particular, we compute a randomised memoryless ?-optimal strategy when it exists, where probabilities are parametrised by ?. We also show that for some games, no optimal randomised strategies exist. We then characterise, and decide in polynomial time, the class of games admitting an optimal randomised memoryless strategy

Behavior in a dynamic decision problem: An analysis of experimental evidence using a bayesian type classification algorithm

It has been long recognized that different people may use different strategies, or decision rules, when playing games or dealing with other complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules that are present in a population of agents. We show that the algorithm performs well in both a Monte Carlo study and in an empirical application. We apply our procedure to analyze the actual behavior of subjects who are confronted with a difficult dynamic stochastic decision problem in a laboratory setting. The procedure does an excellent job of grouping the subjects into easily interpretable types. Given the difficultly of the decision problem, we were surprised to find that nearly a third of subjects were a “Near Rational” type that played a good approximation to the optimal decision rule. More than 40% of subjects followed a rule that we describe as “fatalistic,” since they play as if they don’t appreciate the extent to which payoffs are a controlled stochastic process. And about a quarter of the subjects are classified as “Confused,” since they play the game quite poorly. Interestingly, we find that those subjects who practiced most before playing the game for money were the most likely to play poorly. Thus, lack of effort does not seem to account for poor performance. It is our hope that, in future work, our type classification algorithm will facilitate the positive analysis of peoples’ behavior in many types of complex decision problems.behavioral experiments type-classification bayesian

General-Purpose Planning Algorithms In Partially-Observable Stochastic Games

Partially observable stochastic games (POSGs) are difficult domains to plan in because they feature multiple agents with potentially opposing goals, parts of the world are hidden from the agents, and some actions have random outcomes. It is infeasible to solve a large POSG optimally. While it may be tempting to design a specialized algorithm for finding suboptimal solutions to a particular POSG, general-purpose planning algorithms can work just as well, but with less complexity and domain knowledge required. I explore this idea in two different POSGs: Navy Defense and Duelyst. In Navy Defense, I show that a specialized algorithm framework, goal-driven autonomy, which requires a complex subsystem separate from the planner for explicitly reasoning about goals, is unnecessary, as simple general planners such as hindsight optimization exhibit implicit goal reasoning and have strong performance. In Duelyst, I show that a specialized expert-rule-based AI can be consistently beaten by a simple general planner using only a small amount of domain knowledge. I also introduce a modification to Monte Carlo tree search that increases performance when rollouts are slow and there are time constraints on planning

Sport Specialization by Youth Athletes

Sport specialization and sampling are two ways youth athletes can play sports. Athletes playing in college, at the Division III level, sample sports throughout their childhood instead of specializing in one sport. There is not a lot of specific research done for each Division in the NCAA. This study is a cross-sectional survey of students and athletes at SJFC, which is a small Division III school in Western New York. Using surveys, data was found that supports the research question that Division III athletes and students sampled sports throughout their childhoods rather than specializing. This question can help parents determine what kind of athlete they want their children to be in order for them to play at a higher competition level

Inside the brain of an elite athlete: The neural processes that support high achievement in sports

Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

Hybrid Minimax-MCTS and Difficulty Adjustment for General Game Playing

Board games are a great source of entertainment for all ages, as they create a competitive and engaging environment, as well as stimulating learning and strategic thinking. It is common for digital versions of board games, as any other type of digital games, to offer the option to select the difficulty of the game. This is usually done by customizing the search parameters of the AI algorithm. However, this approach cannot be extended to General Game Playing agents, as different games might require different parametrization for each difficulty level. In this paper, we present a general approach to implement an artificial intelligence opponent with difficulty levels for zero-sum games, together with a propose of a Minimax-MCTS hybrid algorithm, which combines the minimax search process with GGP aspects of MCTS. This approach was tested in our mobile application LoBoGames, an extensible board games platform, that is intended to have an broad catalog of games, with an emphasis on accessibility: the platform is friendly to visually-impaired users, and is compatible with more than 92\% of Android devices. The tests in this work indicate that both the hybrid Minimax-MCTS and the new difficulty adjustment system are promising GGP approaches that could be expanded in future work

Health and safety in the United Kingdom higher education libraries: a review of the literature

The focus of this article is to review the literature relating to health and safety in UK Higher Education libraries. This will include an overview of the literature on accident theories and also the human element. Various key findings emerge from this analysis. Personal safety is achieved through self-responsibility, following guidelines and having a working knowledge of reporting procedures. A safety culture in the work environment is developed through a proactive approach on the part of management, the provision of information, training, and carrying out safety inspections. These inspections are aimed at preventing the environment from creating a situation where an accident could occur. There can never be a work environment in which no accidents will occur and best practice can only minimize the risk of accidents