Evaluating a Computational Model of Emotion

Gratch, Jonathan; Marsella, Stacy

doi:10.1007/s10458-005-1081-1

Cited by 114 publications

(30 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The subjective approach for the evaluation of behavior similarity with human beings has been widely used in the domain of IVAs [11,14]. It consists in evaluating immersion quality through questionnaires.…”

Section: Subjective Approachmentioning

confidence: 99%

Agents Behavior Semi-automatic Analysis through Their Comparison to Human Behavior Clustering

Darty¹

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. This paper presents a generic method to evaluate virtual agents that aim at reproducing humans behaviors in an immersive virtual environment. We first use automated clustering of simulation logs to extract humans behaviors. We then propose an aggregation of the agents logs into those clusters to analyze the credibility of agents behaviors in terms of capacities, lacks, and errors by comparing them to humans ones. We complete this analysis with a subjective evaluation based on a questionnaire filled by human annotators to draw categories of users, making their behaviors explicit. We illustrate this method in the context of immersive driving simulation.

show abstract

Section: Subjective Approachmentioning

confidence: 99%

Agents Behavior Semi-automatic Analysis through Their Comparison to Human Behavior Clustering

Darty¹

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…As Picard suggests, 6 we represent decay where K  and K  are the sum of the positive and negative emotions, respectively, felt in the past n periods, and n can be parameterized by the simulator user.…”

Section: Decaymentioning

confidence: 99%

Context-Aware Emotion-Based Model for Group Decision Making

et al. 2010

View full text Add to dashboard Cite

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans.In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don't always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don't like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm.Combining both optimization-and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks.We've developed a context-aware emotionbased model to design intelligent agents for group decision-making processes. To evaluate this model, we've incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion-and argumentbased factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

show abstract

“…Numerous implementations of the theory exist, aimed at producing agents with a broad set of capabilities, including goal-directed and reactive behaviour, emotional state and social knowledge. These begin with the Affective Reasoner architecture [26] and the Em component [27] of the Hap architecture [28], with later implementations including EMA [29], FAtiMA (FearNot! Affective Mind Architecture) [30], and many more.…”

Section: Related Workmentioning

confidence: 99%

Creating adaptive affective autonomous NPCs

Lim

Dias

Aylett

et al. 2010

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

Abstract. The paper reports work to increase the believability of Non Player Characters in Video games in general and educational role play games in particular. It aims to increase their ability to respond appropriately to the player's actions both cognitively and emotionally. We discuss the modelling of adaptive affective autonomous characters based on a biologically-inspired theory of human action regulation taking into account perception, motivation, emotions, memory, learning and planning. These agents populate an educational Role Playing Game, ORI-ENT (Overcoming Refugee Integration with Empathic Novel Technology) dealing with the cultural-awareness problem for children aged 13 to 14.

show abstract

Evaluating a Computational Model of Emotion

Cited by 114 publications

References 45 publications

Agents Behavior Semi-automatic Analysis through Their Comparison to Human Behavior Clustering

Agents Behavior Semi-automatic Analysis through Their Comparison to Human Behavior Clustering

Context-Aware Emotion-Based Model for Group Decision Making

Creating adaptive affective autonomous NPCs

Contact Info

Product

Resources

About