Abstract
On the last decade Poker has been one of the most interesting subjects for artificial intelligence, because it is a game that requires game playing agents to deal with an incomplete information and stochastic scenario. The development of Poker agents has seen significant advances but it is still hard to evaluate agents' performance against human players. This is either because it is illicit to use agents in online games, or because human players cannot create agents that play like themselves due to lack of knowledge on computer science and/or AI. The purpose of this work is to fill the gap between poker players and AI in Poker by allowing players without programming skills to build their own agents. To meet this goal, a high-level language of poker concepts - PokerLang -was created, whose structure is easy to read and interpret for domain experts. This language allows for the quick definition of an agent strategy. A graphical application was also created to support the writing of PokerLang strategies. To validate this approach, some Poker players created their agents using the graphical application. Results validated the usability of the application and the language that supports it. Moreover, the created agents showed very good results against agents developed by other experts.

Abstract
Computer Assisted Qualitative Data Analysis Software (CAQDAS) may be defined as tools that help researchers developing qualitative research projects. These software packages help the users with tasks such as transcription analysis, writing and annotation, coding and text interpretation, recursive abstraction, content search and analysis, discourse analysis, data mapping, grounded theory methodology, among several other types of analysis. This paper surveys the most relevant CAQDAS software packages comparing their features on different areas such as data management and organization, data annotation, search and query capacities, data visualization, import/export potentialities and teamwork/collaborative work features.

Abstract

Abstract

Abstract

Abstract
Some of the most successful Poker agents that participate in the Annual Computer Poker Competition (ACPC) use an almost zero regret strategy: a strategy that approximates a Nash Equilibrium. However, it is still unfeasible to efficiently compute a Nash Equilibrium without some sort of information set abstraction due to the size of Poker's search tree. One popular technique for abstracting Poker information sets is to group hands with similar Expected Hand Strength (E[HS]) and thus play them in the same way. For large Poker variants, algorithms like CFR might need to calculate E[HS] billions of times, when the game abstraction is so large that it cannot be pre-computed, implying that E[HS] must be determined online. This way, improving the efficiency of this method would certainly reduce the computation time needed by CFR for these cases. In this paper we describe Average Rank Strength; a technique based on a pre-computed lookup table that speeds up E[HS] computation. Ours results demonstrate speed improvements of about three orders of magnitude and negligible results difference, when compared to the original E[HS]. Copyright