Abstract

Abstract
The No-Limit Texas Hold'em variant of Poker is the game that is most frequently used to assess new developments in incomplete information problems, through the development of game playing agents. For this particular game, current state-ofthe-art techniques consist in the pre-computation of a set of strategies that are in a Nash-Equilibrium state. However, due to the game's decision tree size, current algorithms only work in an abstracted version of No-Limit Poker. Moreover, since these strategies are static, they ignore the opponents' playing style thus being unable to maximize profit against certain kinds of opponents. This makes these strategies unusable when playing in an online environment against human players. In this paper we present a rule-based strategy approach for a No-Limit Poker agent that was developed to play online, against human players and in online multiplayer matches. This strategy is based on a popular technique used by human players short stack playing which consists of playing in tables with up to 6 players and low initial resources. Using domain specific opponent modeling techniques and limiting the decisions to the first round of the game, the agent was able to make a good profit margin of 11.5% per game when playing against human players. The significance of our results resides in the fact that, for the first time in the Computer Poker literature, we present a game playing agent that can match human players in multiplayer games.

Abstract

Abstract
Analysis of the usability of an interactive system requires both an understanding of how the system is to be used and a means of assessing the system against that understanding. Such analytic assessments are particularly important in safety-critical systems as latent vulnerabilities may exist which have negative consequences only in certain circumstances. Many existing approaches to assessment use tasks or scenarios to provide explicit representation of their understanding of use. These normative user behaviours have the advantage that they clarify assumptions about how the system will be used but have the disadvantage that they may exclude many plausible deviations from these norms. Assessments of how a design fails to support these user behaviours can be a matter of judgement based on individual experience rather than evidence. We present a systematic formal method for analysing interactive systems that is based on constraints rather than prescribed behaviour. These constraints capture precise assumptions about what information resources are used to perform action. These resources may either reside in the system itself or be external to the system. The approach is applied to two different medical device designs, comparing two infusion pumps currently in common use in hospitals. Comparison of the two devices is based on these resource assumptions to assess consistency of interaction within the design of each device.

Abstract
This study considers a production lot sizing and scheduling problem in the brewery industry. The underlying manufacturing process can be basically divided into two main production stages: preparing the liquids including fermentation and maturation inside the fermentation tanks; and bottling the liquids on the filling lines, making products of different liquids and sizes. This problem differs from other problems in beverage industries due to the relatively long lead times required for the fermentation and maturation processes and because the "ready" liquid can remain in the tanks for some time before being bottled. The main planning challenge is to synchronize the two stages (considering the possibility of a "ready" liquid staying in the tank until bottling), as the production bottlenecks may alternate between these stages during the planning horizon. This study presents a novel mixed integer programming model that represents the problem appropriately and integrates both stages. In order to solve real-world problem instances, MIP-based heuristics are developed, which explore the model structure. The results show that the model is able to comprise the problem requirements and the heuristics produce relatively good-quality solutions.

Abstract
This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.