scholarly journals Engineering Emotionally Intelligent Agents

2011 ◽  
pp. 99-117
Author(s):  
Penny Baillie ◽  
Mark Toleman ◽  
Dickson Lukose
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Emotional State ◽  
Psychological Theory ◽  
Emotional States ◽  
Changing Environment ◽  
Emotionally Intelligent ◽  
Over Time ◽  
The Way

Interacting with intelligence in an ever-changing environment calls for exceptional performances from artificial beings. One mechanism explored to produce intuitive-like behavior in artificial intelligence applications is emotion. This chapter examines the engineering of a mechanism that synthesizes and processes an artificial agent’s internal emotional states: the Affective Space. Through use of the affective space, an agent can predict the effect certain behaviors will have on its emotional state and, in turn, decide how to behave. Furthermore, an agent can use the emotions produced from its behavior to update its beliefs about particular entities and events. This chapter explores the psychological theory used to structure the affective space, the way in which the strength of emotional states can be diminished over time, how emotions influence an agent’s perception, and the way in which an agent can migrate from one emotional state to another.

scholarly journals Engineering Emotionally Intelligent Agents

2011 ◽  
pp. 2323-2337
Author(s):  
Penny Baillie ◽  
Mark Toleman ◽  
Dickson Lukose
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Agents ◽  
Emotional State ◽  
Psychological Theory ◽  
Emotional States ◽  
Changing Environment ◽  
Emotionally Intelligent ◽  
Over Time ◽  
The Way

Interacting with intelligence in an ever-changing environment calls for exceptional performances from artificial beings. One mechanism explored to produce intuitive-like behavior in artificial intelligence applications is emotion. This chapter examines the engineering of a mechanism that synthesizes and processes an artificial agent’s internal emotional states: the Affective Space. Through use of the affective space, an agent can predict the effect certain behaviors will have on its emotional state and, in turn, decide how to behave. Furthermore, an agent can use the emotions produced from its behavior to update its beliefs about particular entities and events. This chapter explores the psychological theory used to structure the affective space, the way in which the strength of emotional states can be diminished over time, how emotions influence an agent’s perception, and the way in which an agent can migrate from one emotional state to another.

scholarly journals DAMAGE RESULTING FROM ARTIFICIAL INTELLIGENCE - THE CASE OF AUTONOMOUS VEHICLES

2021 ◽  
Vol 14 (1) ◽  
pp. 105-124
Author(s):  
Cassiano Highton
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Technological Revolution ◽  
The Subject ◽  
The Law ◽  
Over Time ◽  
The Way

Abstract The way of understanding the law has changed substantially over time and the law of Torts as we have studied and dealt with it until now has evidently become outdated, the legal reality has moved away from the factual reality, we are facing the new paradigms of the digital and technological revolution, with an evident and clear distancing from the classical theories of the law of Torts, a context that requires a specific and updated approach to the subject.

Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale

2018 ◽  
Vol 49 (6) ◽  
pp. 647-683 ◽  
Author(s):  
Jesse Hoey ◽  
Tobias Schröder ◽  
Jonathan Morgan ◽  
Kimberly B. Rogers ◽  
Deepak Rishi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Group Dynamics ◽  
Intelligent Agents ◽  
Behavioral Science ◽  
Psychological Theory ◽  
Study Groups ◽  
Ongoing Research ◽  
Massive Scale ◽  
Groups And Teams

Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams.

Economics of the Ed Tech Revolution

2017 ◽  
Vol 59 (4) ◽  
pp. 49-55 ◽  
Author(s):  
Richard K. Lyons
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Network Effects ◽  
Education Technology ◽  
Increasing Returns ◽  
Durable Good ◽  
Rapid Changes ◽  
Over Time ◽  
The Way

Rapid changes in technology—including advances in augmented and artificial intelligence, machine learning, and mobile—are paving the way for significant changes not only in the channels through which education is delivered but in how education itself is structured. This article identifies eight ways in which education technology can change how learning is facilitated and who will facilitate that learning. Basic economic principles then provide a framework for thinking about how these changes will be embedded over time as education increasingly becomes a durable good providing increasing returns through network effects.

Who matters most? Social support, social strain, and emotions

2018 ◽  
Vol 36 (10) ◽  
pp. 3273-3292
Author(s):  
Kayla D. R. Pierce ◽  
Christopher S. Quiroz
Keyword(s):  
Social Support ◽  
Longitudinal Data ◽  
Causal Effect ◽  
Relative Impact ◽  
Emotional States ◽  
Network Ties ◽  
Social Strain ◽  
Different Sources ◽  
Over Time ◽  
The Way

This research investigates the way in which social support and social strain stemming from spouses, children, and friends have different impacts on emotional states. While previous studies have compared the relative impact of different sources, our research builds upon these studies by (1) comparing various close network ties and (2) leveraging longitudinal data to investigate the causal links between support and strain from different sources and emotional states over time. We analyze individuals who have a spouse, a child, and friends across three waves of the Americans’ Changing Lives data. Although we find that social support and strain from all three sources are associated with emotional states, this relationship is not always causal. In the majority of cases, the same people who experience support or strain in their relationships are also more likely to experience more positive or negative emotional states, respectively. Only spousal interactions and child-based strain have a direct causal effect on emotional states.

Judge v Robot? Artificial Intelligence and Judicial Decision-Making

2018 ◽  
Vol 41 (4) ◽  
Author(s):  
Tania Sourdin
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Technological Change ◽  
Human Activity ◽  
Human Activities ◽  
Judicial Decision ◽  
Judicial Decision Making ◽  
And Function ◽  
Over Time ◽  
The Way

As technology continues to change the way in which we work and function, there are predictions that many aspects of human activity will be replaced or supported by newer technologies. Whilst many human activities have changed over time as a result of human advances, more recent shifts in the context of technological change are likely to have a broader impact on some human functions that have previously been largely undisturbed. In this regard, technology is already changing the practice of law and may for example, reshape the process of judging by either replacing, supporting or supplementing the judicial role. Such changes may limit the extent to which humans are engaged in judging with an increasing emphasis on artificial intelligence to deal with smaller civil disputes and the more routine use of related technologies in more complex disputes.

scholarly journals Trends in AI Research for the Visual Surveillance of Populations

2022 ◽  
Author(s):  
Ashwin Acharya ◽  
Max Langenkamp ◽  
James Dunham
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Bibliometric Analysis ◽  
Vision Research ◽  
Visual Surveillance ◽  
Surveillance Systems ◽  
Recent Trends ◽  
Over Time ◽  
The Way

Progress in artificial intelligence has led to growing concern about the capabilities of AI-powered surveillance systems. This data brief uses bibliometric analysis to chart recent trends in visual surveillance research — what share of overall computer vision research it comprises, which countries are leading the way, and how things have varied over time.

The Integration and Control of Behaviour

2011 ◽  
pp. 176-203 ◽  
Author(s):  
David W. Glasspool
Keyword(s):  
Intelligent Agents ◽  
Large Scale ◽  
Mobile Robotics ◽  
Psychological Theory ◽  
Theoretical Work ◽  
Human Mind ◽  
Scale Model ◽  
Large Scale Model ◽  
And Control ◽  
The Way

Clues to the way behaviour is integrated and controlled in the human mind have emerged from cognitive psychology and neuroscience. The picture that is emerging mirrors solutions (driven primarily by engineering concerns) to similar problems in the rather different domains of mobile robotics and intelligent agents in artificial intelligence (AI). This chapter looks in detail at the relationship between a psychological theory of willed and automatic control of behaviour, the Norman and Shallice framework, and three types of engineering-based theory in AI. As well as being a promising basis for a large-scale model of cognition, the Norman and Shallice framework presents an interesting example both of apparent theoretical convergence between AI and empirical psychology, and of the way in which theoretical work in both fields can benefit from interaction between them.

scholarly journals Exploring the Nuances of Designing (with/for) Artificial Intelligence

Design Issues ◽  
2020 ◽  
Vol 36 (4) ◽  
pp. 45-55
Author(s):  
Niya Stoimenova ◽  
Rebecca Price
Keyword(s):  
Artificial Intelligence ◽  
Fundamental Shift ◽  
Shed Light ◽  
Over Time ◽  
The Way

A fundamental shift in the way society operates is approaching driven by advances in the field of artificial intelligence (AI). Yet, there is a comparative lack of discourse across the design discipline regarding this topic. While there are fragments of methodological readiness for designing (with/for) AI, the nuances of such need to be further explored. The aim of this article is to shed light on these and suggest a possible way forward for design that can ensure AI-powered artifacts remain safe even as their utility evolves over time.

Effect of Emotions in a Lexical Decision Task

2017 ◽  
Vol 76 (2) ◽  
pp. 71-79 ◽  
Author(s):  
Hélène Maire ◽  
Renaud Brochard ◽  
Jean-Luc Kop ◽  
Vivien Dioux ◽  
Daniel Zagar
Keyword(s):  
Lexical Decision ◽  
Lexical Decision Task ◽  
Emotional State ◽  
Control Group ◽  
Decision Task ◽  
Attentional Orienting ◽  
Emotional States ◽  
Sequence Effect ◽  
Emotion Induction ◽  
Before And After

Abstract. This study measured the effect of emotional states on lexical decision task performance and investigated which underlying components (physiological, attentional orienting, executive, lexical, and/or strategic) are affected. We did this by assessing participants’ performance on a lexical decision task, which they completed before and after an emotional state induction task. The sequence effect, usually produced when participants repeat a task, was significantly smaller in participants who had received one of the three emotion inductions (happiness, sadness, embarrassment) than in control group participants (neutral induction). Using the diffusion model ( Ratcliff, 1978 ) to resolve the data into meaningful parameters that correspond to specific psychological components, we found that emotion induction only modulated the parameter reflecting the physiological and/or attentional orienting components, whereas the executive, lexical, and strategic components were not altered. These results suggest that emotional states have an impact on the low-level mechanisms underlying mental chronometric tasks.

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