Opponent models are necessary in games where the game state is only partially known to the player, since the player must infer the state of the game based on the opponent's actions. This paper presents an architecture and a process for developing neural network game players that utilize explicit opponent models in order to improve game play against unseen opponents. The model is constructed as a mixture over a set of cardinal opponents, i.e. opponents that represent maximally distinct game strategies. The model is trained to estimate the likelihood that the opponent will make the same move as each of the cardinal opponents would in a given game situation. Experiments were performed in the game of Guess It, a simple game of imperfect information that has no optimal strategy for defeating specific opponents. Opponent modeling is therefore crucial to play this game well. Both opponent modeling and game-playing neural networks were trained using NeuroEvolution of Augmenting Topologies (NEAT). The results demonstrate that game-playing provided with the model outperform networks not provided with the model when played against the same previously unseen opponents. The "cardinal mixture" architecture therefore constitutes a promising approach for general and dynamic opponent modeling in gameplaying.

We present the results of a multiple case study of how architects view and address the issues in transforming requirements into architectures in practice. Specifically we report how they view and address issues of requirements, architecture, and the transformation of requirements into architecture. We then summarize the important lessons learned from these practicing architects about this critically important step in creating and evolving software systems.

Active research is being done in how to go from requirements to architecture. However, no studies have been attempted in this area despite a long history of empirical research in software engineering (SE). Our goal is to establish a framework for the transformation from requirements to architecture on the basis of a series of empirical studies. The first step is to collect evidence about practice in industry before designing relevant techniques, methods and tools. As part of this step, we use an interview-based multiple-case study with a carefully designed process of conducting the interviews and of preparing the data collected for analysis while preserving its integrity. In this paper, we describe the design of this multiple-case study, delineate the evidence trail, discuss validity issues, outline the data analysis focus, discuss meta issues on evidence-based SE particularly on combining and using evidence, describe triangulation approaches, and present two methods for accumulating evidence.

Active research is being done in how to go from requirements to architecture. However, no studies have been attempted in this area despite a long history of empirical research in software engineering (SE). Our goal is to establish a framework for the transformation from requirements to architecture on the basis of a series of empirical studies. The first step is to collect evidence about practice in industry before designing relevant techniques, methods and tools. As part of this step, we use an interview-based multiple-case study with a carefully designed process of conducting the interviews and of preparing the data collected for analysis while preserving its integrity. In this paper, we describe the design of this multiple-case study, delineate the evidence trail, discuss validity issues, outline the data analysis focus, discuss meta issues on evidence-based SE particularly on combining and using evidence, describe triangulation approaches, and present two methods for accumulating evidence.

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