Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

The use of computer science practices and methods for developing social simulations to stimulate changes in travellers’ mode choice

In this thesis, Computer Science practices and methods including Software Engineering and Artificial Intelligence techniques are explored to incorporate Human Factors and Psychology knowledge in a structured way into agent-based models to model modal shift in a social system. Observations of peoples’ behaviours in social systems regarding choice-making suggest that they tend to have preferences among the available alternatives in many situations. Experts in the domain of Psychology have been interested in the relationships that exist between the psychological processes (factors) and peoples’ behaviours. Human Factors’ experts are concerned with, among other things, the study of factors and development of tools that improve users’ experiences. The findings from the literature suggest that the two groups have been working from the perspective of their domains without much collaboration. Also, no known framework or methodology offers the required collaborative modelling support and techniques to model people’s emotion as they traverse the system. The aim of this thesis is, therefore, to provide modelling techniques that better support the use of Human Factors and Psychology knowledge in understanding factors that influence travellers’ decision-making in travel mode choice so as to stimulate changes in their behaviours. The support also provides collaboration among relevant stakeholders to work on modal shift project in the transport system. The method adopted in carrying out the research reported in this thesis is informed by the descriptive, developmental, and exploratory nature of the objectives of the research. Our novel methodology which includes a framework is named MOdal SHift (MOSH) methodology. Its development process involves the use of design principles that include encapsulation, data abstraction, inheritance, and polymorphism in defining and integrating the Human Factors and Psychology practices into the methodology. The structures and behaviours of the system components are described and documented using the Unified Modelling Language (UML) as a standard specification language to promote uniform communication among a group of experts. The decision variable decomposition module and techniques for deriving travellers’ emotions that correspond to their context involved the use of the Fuzzy sets system. The methodology contains guides that include the process map diagram showing the major stages in the methodology as well as the step-by-step development guidelines. To verify and to validate the methodology, two case studies in the transport domain are selected. The first case study aims at demonstrating the use of the framework included in the methodology for policy formulation. The second case study has the goal of demonstrating the use of the methodology for understanding individuals’ abilities to satisfy travel requirements. Data Science methods including both supervised and unsupervised learning algorithms are applied at relevant stages of the case studies. The reflection from the cases investigated with the MOSH methodology reveals its novelty in modelling interdependencies among the transport system’s constraints and in modelling travellers’ emotional state as they traverse the transport system’s environment. In addition, the adoption of the standard specification language in the design of the methodology provides the means for easy communication and transfer of knowledge among stakeholders. The use of Software Engineering tools and methods in conjunction with the agent-based modelling paradigm in the MOSH methodology design and development phases promotes the separation of concerns for the interrelated and non-linear levels of organisation within a sociotechnical system. It also promotes extensibility of various aspect of the methodology as a result of the independence among the components and makes reusability of relevant aspects possible when there are needs to use the same functionality in a new project. The agent-based modelling paradigm provides opportunities for investigating the interactions among the agents and the environment as well as providing insights into the various complex interrelated behaviours

Unified Theory of Acceptance and Use of Technology (UTAUT) for Intelligent Process Automation

Intelligent process automation is a technological innovation that combines symbolic automation tools with machine learning. Intelligent process automation can automate complex tasks that otherwise have to be performed by humans when symbolic automation is not powerful enough. Regardless of the high economic potential for companies, the adoption rate in practice is comparatively low. This could be due to the adoption behavior of the employees. In our work, we iteratively develop a Unified Theory of Acceptance and use of Technology (UTAUT) model for the adoption of intelligent process automation and evaluate it with an empirical study. With our research we want to empower designers to adapt the corresponding tools in the future to increase adoption. The study shows that, in addition to established factors for technology adoption, trust, transparency, and attitude towards technology are primary decision factors

The structural characteristics of women-endorsers in advertising messages and their influence on consumers´ responses

Participation of women-endorsers in advertising is key element in solving some of the psychological problems of the advertising industry: to involve consumers into the message of the brand, to evoke desire to possess advertised product and to form positive attitude towards brand. Researchers claim that consumers build their attitude towards brand messages based on their first impression. Human images are the first sources which consumers pay attention to in advertising. Negative reaction to these images has strong impact on consumers’ attention towards the advertising message, their interest towards the product and brand which may lead to disregarding of advertising. Study shows that image of endorsers may influence not only on attention or interest of consumers to brand but also may form their attitude towards the product and intention to purchase (Bjerke and Polegato, 2006; Chi, Yeh, Huang, 2009; Goldsmith, Laffery and Newell, 2000; Maheswaran, Durajraj and Sternhall, 1990; Solomon and Michael, 2004). On the other hand, for the consumers, the endorsers used in adverting are the most powerful vehicles for the brands’ messages because they are capable of forming the opinion of consumers through a large number of psychological effects (Bahram and Zahra, 2010; DeBono, Kenneth, Harnish and Richard, 1988; Goldsmith, Laffery and Newell, 2000). Basic modalities represented by endorsers, such as speech, emotional expression, behavior, etc., are dependent on several external and internal factors (social, cultural, political, religious, economic, and others), and therefore, they tend to vary in space and time. This complexity makes then elusive to scientific study. The purpose of this study is to define how participants’ attitudes towards different characteristics of women-endorsers influence on their assessments of brands and their messages. How participants’ assessments of these characteristics influence on responses of participants in the form of subjective feedback (such as attention, interest, positive attitude towards advertising message, brand and intention to purchase). This study shows to what extent participants’ assessments of women-endorsers are subject to influence of brand attitude in consumers and how these assessments correlate

Building and Designing Expressive Speech Synthesis

We know there is something special about speech. Our voices are not just a means of communicating. They also give a deep impression of who we are and what we might know. They can betray our upbringing, our emotional state, our state of health. They can be used to persuade and convince, to calm and to excite. As speech systems enter the social domain they are required to interact, support and mediate our social relationships with 1) each other, 2) with digital information, and, increasingly, 3) with AI-based algorithms and processes. Socially Interactive Agents (SIAs) are at the fore- front of research and innovation in this area. There is an assumption that in the future “spoken language will provide a natural conversational interface between human beings and so-called intelligent systems.” [Moore 2017, p. 283]. A considerable amount of previous research work has tested this assumption with mixed results. However, as pointed out “voice interfaces have become notorious for fostering frustration and failure” [Nass and Brave 2005, p.6]. It is within this context, between our exceptional and intelligent human use of speech to communicate and interact with other humans, and our desire to leverage this means of communication for artificial systems, that the technology, often termed expressive speech synthesis uncomfortably falls. Uncomfortably, because it is often overshadowed by issues in interactivity and the underlying intelligence of the system which is something that emerges from the interaction of many of the components in a SIA. This is especially true of what we might term conversational speech, where decoupling how things are spoken, from when and to whom they are spoken, can seem an impossible task. This is an even greater challenge in evaluation and in characterising full systems which have made use of expressive speech. Furthermore when designing an interaction with a SIA, we must not only consider how SIAs should speak but how much, and whether they should even speak at all. These considerations cannot be ignored. Any speech synthesis that is used in the context of an artificial agent will have a perceived accent, a vocal style, an underlying emotion and an intonational model. Dimensions like accent and personality (cross speaker parameters) as well as vocal style, emotion and intonation during an interaction (within-speaker parameters) need to be built in the design of a synthetic voice. Even a default or neutral voice has to consider these same expressive speech synthesis components. Such design parameters have a strong influence on how effectively a system will interact, how it is perceived and its assumed ability to perform a task or function. To ignore these is to blindly accept a set of design decisions that ignores the complex effect speech has on the user’s successful interaction with a system. Thus expressive speech synthesis is a key design component in SIAs. This chapter explores the world of expressive speech synthesis, aiming to act as a starting point for those interested in the design, building and evaluation of such artificial speech. The debates and literature within this topic are vast and are fundamentally multidisciplinary in focus, covering a wide range of disciplines such as linguistics, pragmatics, psychology, speech and language technology, robotics and human-computer interaction (HCI), to name a few. It is not our aim to synthesise these areas but to give a scaffold and a starting point for the reader by exploring the critical dimensions and decisions they may need to consider when choosing to use expressive speech. To do this, the chapter explores the building of expressive synthesis, highlighting key decisions and parameters as well as emphasising future challenges in expressive speech research and development. Yet, before these are expanded upon we must first try and define what we actually mean by expressive speech

Emotions: from psychological theories to logical formalization and implementation in a BDI agent

This thesis is about emotions, and more particularly about their logical formalization. The first part is dedicated to the state of the art, from the point of view of both psychology (history of theories of emotions) and computer science (presentation of emotional agents and their applications).The second part is dedicated to the logical formalisation of emotions. It introduces our logical framework, exposes and argues the formal definitions of twenty emotions, and proves some of their properties. Finally the last part is dedicated to practical applications and continuation prospects of this work. Such a work offers interesting contributions : it offers to the agent community a formal model of a great number of emotions; it shows the interest of BDI logics; and it opens research prospects about the dynamics of emotions and their influence on the behaviour of agents, a field not much explored for now

The use of computer science practices and methods for developing social simulations to stimulate changes in travellers’ mode choice

In this thesis, Computer Science practices and methods including Software Engineering and Artificial Intelligence techniques are explored to incorporate Human Factors and Psychology knowledge in a structured way into agent-based models to model modal shift in a social system. Observations of peoples’ behaviours in social systems regarding choice-making suggest that they tend to have preferences among the available alternatives in many situations. Experts in the domain of Psychology have been interested in the relationships that exist between the psychological processes (factors) and peoples’ behaviours. Human Factors’ experts are concerned with, among other things, the study of factors and development of tools that improve users’ experiences. The findings from the literature suggest that the two groups have been working from the perspective of their domains without much collaboration. Also, no known framework or methodology offers the required collaborative modelling support and techniques to model people’s emotion as they traverse the system. The aim of this thesis is, therefore, to provide modelling techniques that better support the use of Human Factors and Psychology knowledge in understanding factors that influence travellers’ decision-making in travel mode choice so as to stimulate changes in their behaviours. The support also provides collaboration among relevant stakeholders to work on modal shift project in the transport system. The method adopted in carrying out the research reported in this thesis is informed by the descriptive, developmental, and exploratory nature of the objectives of the research. Our novel methodology which includes a framework is named MOdal SHift (MOSH) methodology. Its development process involves the use of design principles that include encapsulation, data abstraction, inheritance, and polymorphism in defining and integrating the Human Factors and Psychology practices into the methodology. The structures and behaviours of the system components are described and documented using the Unified Modelling Language (UML) as a standard specification language to promote uniform communication among a group of experts. The decision variable decomposition module and techniques for deriving travellers’ emotions that correspond to their context involved the use of the Fuzzy sets system. The methodology contains guides that include the process map diagram showing the major stages in the methodology as well as the step-by-step development guidelines. To verify and to validate the methodology, two case studies in the transport domain are selected. The first case study aims at demonstrating the use of the framework included in the methodology for policy formulation. The second case study has the goal of demonstrating the use of the methodology for understanding individuals’ abilities to satisfy travel requirements. Data Science methods including both supervised and unsupervised learning algorithms are applied at relevant stages of the case studies. The reflection from the cases investigated with the MOSH methodology reveals its novelty in modelling interdependencies among the transport system’s constraints and in modelling travellers’ emotional state as they traverse the transport system’s environment. In addition, the adoption of the standard specification language in the design of the methodology provides the means for easy communication and transfer of knowledge among stakeholders. The use of Software Engineering tools and methods in conjunction with the agent-based modelling paradigm in the MOSH methodology design and development phases promotes the separation of concerns for the interrelated and non-linear levels of organisation within a sociotechnical system. It also promotes extensibility of various aspect of the methodology as a result of the independence among the components and makes reusability of relevant aspects possible when there are needs to use the same functionality in a new project. The agent-based modelling paradigm provides opportunities for investigating the interactions among the agents and the environment as well as providing insights into the various complex interrelated behaviours

Sentiment Analysis in Digital Spaces: An Overview of Reviews

Sentiment analysis (SA) is commonly applied to digital textual data, revealing insight into opinions and feelings. Many systematic reviews have summarized existing work, but often overlook discussions of validity and scientific practices. Here, we present an overview of reviews, synthesizing 38 systematic reviews, containing 2,275 primary studies. We devise a bespoke quality assessment framework designed to assess the rigor and quality of systematic review methodologies and reporting standards. Our findings show diverse applications and methods, limited reporting rigor, and challenges over time. We discuss how future research and practitioners can address these issues and highlight their importance across numerous applications.Comment: 44 pages, 4 figures, 6 tables, 3 appendice