1,093 research outputs found
Overview on agent-based social modelling and the use of formal languages
Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft
Classification of Facial Emotions using Guided Particle Swarm Optimization I
This paper presents a novel approach to facial emotion detection using a modified Particle Swarm Optimization algorithm, which we called Guided Particle Swarm Optimization (GPSO). The approach involves tracking the movements of 10 Action Units (AUs) placed at appropriate points on the face of a subject and captured in video clips. Two dimensional rectangular domains are defined around each of the AUs and Particles are then defined to have a component in each domain, effectively creating a 10- dimensional search space within which particles fly in search of a solution. Since there are more than one possible target emotions at any point in time, multiple swarms are used, with each swarm having a specific emotion as its target. At each frame in the video clip, the solution of the swarm that is nearest to its target is accepted as the solution. Our results so far show the approach to have a promising success rate
Social Influence and the Collective Dynamics of Opinion Formation
Social influence is the process by which individuals adapt their opinion,
revise their beliefs, or change their behavior as a result of social
interactions with other people. In our strongly interconnected society, social
influence plays a prominent role in many self-organized phenomena such as
herding in cultural markets, the spread of ideas and innovations, and the
amplification of fears during epidemics. Yet, the mechanisms of opinion
formation remain poorly understood, and existing physics-based models lack
systematic empirical validation. Here, we report two controlled experiments
showing how participants answering factual questions revise their initial
judgments after being exposed to the opinion and confidence level of others.
Based on the observation of 59 experimental subjects exposed to peer-opinion
for 15 different items, we draw an influence map that describes the strength of
peer influence during interactions. A simple process model derived from our
observations demonstrates how opinions in a group of interacting people can
converge or split over repeated interactions. In particular, we identify two
major attractors of opinion: (i) the expert effect, induced by the presence of
a highly confident individual in the group, and (ii) the majority effect,
caused by the presence of a critical mass of laypeople sharing similar
opinions. Additional simulations reveal the existence of a tipping point at
which one attractor will dominate over the other, driving collective opinion in
a given direction. These findings have implications for understanding the
mechanisms of public opinion formation and managing conflicting situations in
which self-confident and better informed minorities challenge the views of a
large uninformed majority.Comment: Published Nov 05, 2013. Open access at:
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.007843
A Survey of Multi-Agent Human-Robot Interaction Systems
This article presents a survey of literature in the area of Human-Robot
Interaction (HRI), specifically on systems containing more than two agents
(i.e., having multiple humans and/or multiple robots). We identify three core
aspects of ``Multi-agent" HRI systems that are useful for understanding how
these systems differ from dyadic systems and from one another. These are the
Team structure, Interaction style among agents, and the system's Computational
characteristics. Under these core aspects, we present five attributes of HRI
systems, namely Team size, Team composition, Interaction model, Communication
modalities, and Robot control. These attributes are used to characterize and
distinguish one system from another. We populate resulting categories with
examples from recent literature along with a brief discussion of their
applications and analyze how these attributes differ from the case of dyadic
human-robot systems. We summarize key observations from the current literature,
and identify challenges and promising areas for future research in this domain.
In order to realize the vision of robots being part of the society and
interacting seamlessly with humans, there is a need to expand research on
multi-human -- multi-robot systems. Not only do these systems require
coordination among several agents, they also involve multi-agent and indirect
interactions which are absent from dyadic HRI systems. Adding multiple agents
in HRI systems requires advanced interaction schemes, behavior understanding
and control methods to allow natural interactions among humans and robots. In
addition, research on human behavioral understanding in mixed human-robot teams
also requires more attention. This will help formulate and implement effective
robot control policies in HRI systems with large numbers of heterogeneous
robots and humans; a team composition reflecting many real-world scenarios.Comment: 23 pages, 7 figure
Stress Propagation in Human-Robot Teams Based on Computational Logic Model
Mission teams are exposed to the emotional toll of life and death decisions.
These are small groups of specially trained people supported by intelligent
machines for dealing with stressful environments and scenarios. We developed a
composite model for stress monitoring in such teams of human and autonomous
machines. This modelling aims to identify the conditions that may contribute to
mission failure. The proposed model is composed of three parts: 1) a
computational logic part that statically describes the stress states of
teammates; 2) a decision part that manifests the mission status at any time; 3)
a stress propagation part based on standard Susceptible-Infected-Susceptible
(SIS) paradigm. In contrast to the approaches such as agent-based, random-walk
and game models, the proposed model combines various mechanisms to satisfy the
conditions of stress propagation in small groups. Our core approach involves
data structures such as decision tables and decision diagrams. These tools are
adaptable to human-machine teaming as well.Comment: Submitted to IEEE Aerospace 2023 conferenc
- …