How the OCEAN Personality Model Affects the Perception of Crowds

Cataloged from PDF version of article.A personality model named High-Density Autonomous Crowds (HiDAC) simulation system provides individual differences by assigning each person different psychological and physiological traits. Users normally set these parameters to model a crowd's nonuniformity and diversity. The approach creates plausible variations in the crowd and enables novice users to dictate these variations by combining a standard personality model with a high-density crowd simulation. HiDAC addresses the simulation of local behaviors and the global wayfinding of crowds in a dynamically changing environment. It directs autonomous agents' behavior by combining geometric and psychological rules. HiDAC handles collisions through avoidance and response forces. Over long distances, the system applies collision avoidance so that agents can steer around obstacles. HiDAC assigns people specific behaviors. The number of actions they complete depends on their curiosity

Creating crowd variation with the OCEAN personality model

Most current crowd simulators animate homogeneous crowds, but include underlying parameters that can be tuned to create variations within the crowd. These parameters, however, are specific to the crowd models and may be difficult for an animator or naive user to use. We propose mapping these parameters to personality traits. In this paper, we extend the HiDAC (High-Density Autonomous Crowds) system by providing each agent with a personality model in order to examine how the emergent behavior of the crowd is affected. We use the OCEAN personality model as a basis for agent psychology. To each personality trait we associate nominal behaviors; thus, specifying personality for an agent leads to an automation of the low-level parameter tuning process. We describe a plausible mapping from personality traits to existing behavior types and analyze the overall emergent crowd behaviors. Copyright © 2008, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

Arguing with behavior influence: A model for web-based group decision support systems

In this work, we propose an argumentation-based dialogue model designed for Web-based Group Decision Support Systems, that considers the decision-makers' intentions. The intentions are modeled as behavior styles which allow agents to interact with each other as humans would in face-to-face meetings. In addition, we propose a set of arguments that can be used by the agents to perform and evaluate requests, while considering the agents' behavior style. The inclusion of decision-makers' intentions intends to create a more reliable and realistic process. Our model proved, in different contexts, that higher levels of consensus and satisfaction are achieved when using agents modeled with behavior styles compared to agents without any features to represent the decision-makers' intentions.- (undefined

How the ocean personality model affects the perception of crowds

A personality model named High-Density Autonomous Crowds (HiDAC) simulation system provides individual differences by assigning each person different psychological and physiological traits. Users normally set these parameters to model a crowd's nonuniformity and diversity. The approach creates plausible variations in the crowd and enables novice users to dictate these variations by combining a standard personality model with a high-density crowd simulation. HiDAC addresses the simulation of local behaviors and the global wayfinding of crowds in a dynamically changing environment. It directs autonomous agents' behavior by combining geometric and psychological rules. HiDAC handles collisions through avoidance and response forces. Over long distances, the system applies collision avoidance so that agents can steer around obstacles. HiDAC assigns people specific behaviors. The number of actions they complete depends on their curiosity

Individualized Gesturing Outperforms Average Gesturing – Evaluating Gesture Production in Virtual Humans

Bergmann K, Kopp S, Eyssel FA. Individualized Gesturing Outperforms Average Gesturing – Evaluating Gesture Production in Virtual Humans. In: Allbeck J, Badler N, Bickmore T, Pelachaud C, Safonova A, eds. Intelligent Virtual Agents. Proceedings of the 10th International Conference on Intelligent Virtual Agents. Lecture Notes in Computer Science. Vol 6356. Berlin/Heidelberg, Germany: Springer; 2010: 104-117.How does a virtual agent’s gesturing behavior influence the user’s perception of communication quality and the agent’s personality? This question was investigated in an evaluation study of co-verbal iconic gestures produced with the Bayesian network-based production model GNetIc. A network learned from a corpus of several speakers was compared with networks learned from individual speaker data, as well as two control conditions. Results showed that automatically GNetIc-generated gestures increased the perceived quality of an object description given by a virtual human. Moreover, the virtual agent showing gesturing behavior generated with individual speaker networks was rated more positively in terms of likeability, competence and human-likeness

Implementing distinctive behavior for conversational agents

We aim to define conversational agents exhibiting distinctive behavior. To this aim we provide a small set of parameters to allow one to define behavior profiles and then leave to the system the task of animating the agents. Our approach is to manipulate the behavior tendency of the agents depending on their communicative intention and emotional state. In this paper we define the concepts of Baseline and Dynamicline. The Baseline of an agent is defined as a set of fixed parameters that represent the personalized agent behavior, while the Dynamicline is a set of parameters that derive both from the Baseline and the current communicative intention and emotional state. \ua9 2009 Springer Berlin Heidelberg