Using the Anti-Mind with Feedack Algorithm to Find a Good Playing Strategy

Sem PDF conforme despacho.In a previous work we developed the Anti-Mind algorithm. The Anti-Mind program simulated a good player of the Mastermind game, discovering the secret code defined by the human operator (a sequence of four integers in the interval [0 5] ) very quickly. Then we used the algorithm of Anti-Mind to help and correct a human operator trying to discover the secret code defined by the computer resulting in the Anti-Mind with Feedback algorithm. In this paper, we revisited this work and developed another faster implementation of the Anti-Mind with Feedback algorithm which has the drawback that it does not know the set of next good guesses, it just compares each guess with the previous moves and accepts it if it is \textit{coherent} with all the previous moves. Nevertheless, we introduced an option to generate the set of good guesses, i.e., the guesses that are \textit{coherent} with all the previous moves. This implementation allows generalizing the Mastermind game to more than four digits and more than six colours. We begin to define rigorously what we mean by a guess \textit{coherent} with a previous move, next we define what is a good guess and, then, we enunciate five hypotheses about the Anti-Mind algorithm namely one that guarantees that if we always play a good guess we will find the code in a finite bounded number of guesses. We propose a strategy to play Mastermind with the maximization of repetions at the beginning of the game which reduces the \textit{cognitive overload} to play well and validate it with the Anti-Mind with Feedback algorithm. Finally we compare the Anti-Mind algorithm with the Ant-Mind with maximization of repetitions of the guesses through intensive simulations and conclude that the original Anti-Mind algorithm has a better average performance in terms of the number of guesses to break the secret code.publishersversionpublishe

Learning Character Strings via Mastermind Queries, with a Case Study Involving mtDNA

We study the degree to which a character string, $Q$, leaks details about itself any time it engages in comparison protocols with a strings provided by a querier, Bob, even if those protocols are cryptographically guaranteed to produce no additional information other than the scores that assess the degree to which $Q$ matches strings offered by Bob. We show that such scenarios allow Bob to play variants of the game of Mastermind with $Q$ so as to learn the complete identity of $Q$. We show that there are a number of efficient implementations for Bob to employ in these Mastermind attacks, depending on knowledge he has about the structure of $Q$, which show how quickly he can determine $Q$. Indeed, we show that Bob can discover $Q$ using a number of rounds of test comparisons that is much smaller than the length of $Q$, under reasonable assumptions regarding the types of scores that are returned by the cryptographic protocols and whether he can use knowledge about the distribution that $Q$ comes from. We also provide the results of a case study we performed on a database of mitochondrial DNA, showing the vulnerability of existing real-world DNA data to the Mastermind attack.Comment: Full version of related paper appearing in IEEE Symposium on Security and Privacy 2009, "The Mastermind Attack on Genomic Data." This version corrects the proofs of what are now Theorems 2 and 4

Tailoring persuasive health games to gamer type

Persuasive games are an effective approach for motivating health behavior, and recent years have seen an increase in games designed for changing human behaviors or attitudes. However, these games are limited in two major ways: first, they are not based on theories of what motivates healthy behavior change. This makes it difficult to evaluate why a persuasive approach works. Second, most persuasive games treat players as a monolithic group. As an attempt to resolve these weaknesses, we conducted a large-scale survey of 642 gamers' eating habits and their associated determinants of healthy behavior to understand how health behavior relates to gamer type. We developed seven different models of healthy eating behavior for the gamer types identified by BrainHex. We then explored the differences between the models and created two approaches for effective persuasive game design based on our results. The first is a one-size-fits-all approach that will motivate the majority of the population, while not demotivating any players. The second is a personalized approach that will best motivate a particular type of gamer. Finally, to make our approaches actionable in persuasive game design, we map common game mechanics to the determinants of healthy behavior

Playing Mastermind With Constant-Size Memory

We analyze the classic board game of Mastermind with $n$ holes and a constant number of colors. A result of Chv\'atal (Combinatorica 3 (1983), 325-329) states that the codebreaker can find the secret code with $\Theta(n / \log n)$ questions. We show that this bound remains valid if the codebreaker may only store a constant number of guesses and answers. In addition to an intrinsic interest in this question, our result also disproves a conjecture of Droste, Jansen, and Wegener (Theory of Computing Systems 39 (2006), 525-544) on the memory-restricted black-box complexity of the OneMax function class.Comment: 23 page