Facilitating open plot structures in story driven video games using situation generation

Story driven video games are rising in popularity, along with the players desire to make meaningful choice within the plot and therefore become more involved and immersed within the experience. This paper investigates the problems which arise from implementing interactive narrative within video games and potential techniques to solve those problems. The main focus of the study was the situation generation technique, used to maintain the continuity within open, emergent plot structures, using behaviour trees as a means to implement and traverse plot sequences. The ISGEngine was developed during the course of this study in order to implement and evaluate the situation generation technique

Affect and believability in game characters:a review of the use of affective computing in games

Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions

A simple hybrid algorithm for improving team sport AI

In the very popular genre of team sports games defeating the opposing AI is the main focus of the gameplay experience. However the overall quality of these games is significantly damaged because, in a lot of cases, the opposition is prone to mistakes or vulnerable to exploitation. This paper introduces an AI system which overcomes this failing through the addition of simple adaptive learning and prediction algorithms to a basic ice hockey defence. The paper shows that improvements can be made to the gameplay experience without overly increasing the implementation complexity of the system or negatively affecting its performance. The created defensive system detects patterns in the offensive tactics used against it and changes elements of its reaction accordingly; effectively adapting to attempted exploitation of repeated tactics. This is achieved using a fuzzy inference system that tracks player movement, which greatly improves variation of defender positioning, alongside an N-gram pattern recognition-based algorithm that predicts the next action of the attacking player. Analysis of implementation complexity and execution overhead shows that these techniques are not prohibitively expensive in either respect, and are therefore appropriate for use in games

Creating an acute energy deficit without stimulating compensatory increases in appetite: is there an optimal exercise protocol?

Recent years have witnessed significant interest from both the scientific community and the media regarding the influence of exercise on subsequent appetite and energy intake responses. This review demonstrates a consensus among the majority of scientific investigations that an acute bout of land-based endurance exercise does not stimulate any compensatory increases in appetite and energy intake on the day of exercise. Alternatively, preliminary evidence suggests that low volume, supramaximal exercise may stimulate an increase in appetite perceptions during the subsequent hours. In accordance with the apparent insensitivity of energy intake to exercise in the short term, the daily energy balance response to exercise appears to be primarily determined by the energy cost of exercise. This finding supports the conclusions of recent training studies that the energy expenditure of exercise is the strongest predictor of fat loss during an exercise programme

The computation of the cohomology rings of all groups of order 128

We describe the computation of the mod-2 cohomology rings of all 2328 groups of order 128. One consequence is that all groups of order less than 256 satisfy the strong form of Benson's Regularity Conjecture.Comment: 15 pages; revised versio

Industry Implications of Value Creation and Appropriation Investment Decisions

As managers weigh their resource investment decisions, we argue that these investments have a direct impact on the growth and volatility of the firm’s industry. With data covering 377 industries across 16 years, we investigate relationships for aggregate firm investments on the growth and volatility of industry profit and sales. Results reveal important, complex relationships between investment in value creation and appropriation and different elements of the industry environment

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

There is a high prevalence of coronary artery disease (CAD) in patients with left bundle branch block (LBBB); however there are many other causes for this electrocardiographic abnormality. Non-invasive assessment of these patients remains difficult, and all commonly used modalities exhibit several drawbacks. This often leads to these patients undergoing invasive coronary angiography which may not have been necessary. In this review, we examine the uses and limitations of commonly performed non-invasive tests for diagnosis of CAD in patients with LBBB

Congressional Vote Options

Among political practitioners, there is conventional wisdom about the outcomes of critical and salient legislative votes. 'This vote,' we hear, ' will either win by a little or lose by a lot.' Real-world examples suggest coalition leaders purchase 'hip-pocket' votes and "if you need me" pledges, which are converted to favorable votes when they will yield a victory. When the outcome is uncertain, such a process -- securing commitments in advance and calling them in if necessary -- is advantageous relative to traditional vote buying. Excess votes are not bought, nor are votes purchased for a losing effort. In effect, the leader secures options on votes. Given uncertainty, buying vote options yields two outcomes in conceivably winnable situations, one a narrow victory, the other a substantial loss. Such a distribution of outcomes is not explicable in a traditional vote-buying framework. We look for evidence of this pattern -- the tracings of 'if you need me pledges' -- by examining all Congressional Quarterly key votes from 1975 through 1998. On these critical and salient votes, narrow victories are much more frequent than narrow losses. Furthermore, when leaders lose key votes, as predicted, they lose by bigger margins than when they win. Finally, we discuss leadership strategies for keeping 'narrow wins' from unraveling into 'big losses.'

A review of electricity load profile classification methods

With the electricity market liberalisation in Indonesia, the electricity companies will have the right to develop tariff rates independently. Thus, precise knowledge of load profile classifications of customers will become essential for designing a variety of tariff options, in which the tariff rates are in line with efficient revenue generation and will encourage optimum take up of the available electricity supplies, by various types of customers. Since the early days of the liberalisation of the Electricity Supply Industries (ESI) considerable efforts have been made to investigate methodologies to form optimal tariffs based on customer classes, derived from various clustering and classification techniques. Clustering techniques are analytical processes which are used to develop groups (classes) of customers based on their behaviour and to derive representative sets of load profiles and help build models for daily load shapes. Whereas classification techniques are processes that start by analysing load demand data (LDD) from various customers and then identify the groups that these customers' LDD fall into. In this paper we will review some of the popular clustering algorithms, explain the difference between each method