Psychological and Socio-Psychological Factors in Behavioral Simulation of Human Crowds

This paper aims to synthesis existing research efforts to provide an integrated view of behavioral model designs and relevant theoretical frameworks of heterogeneous agents for crowd simulations. Most existing studies considered only limited parameters by including a few selected personalities traits, emotion, and group characteristics for specific scenarios and applications. Most often, these factors are implemented with limited reference to theoretical psychology and cognitive models. This study attempts to synthesis existing research effort and outlines opportunities, challenges, and promising areas for future research for integrating psychological and socio-psychological factors in crowd behavior simulations

Constraint Aware Behavior in Multi-Robot Systems

In this work we present a behavioral modeling framework that accounts for a battery constraint. This framework allows for a user to model robot teams of varying configuration performing com- mon robotic tasks such as exploration or going to user specified goals. The focus of this work is on how to model a constraint aware behavior and how assistance can be requested by and provided from a robot team. We show experimental results in simulated environments and identify trends that can be seen given a robot team configuration. We also discuss how this system can be adapted to different environments and different constraints. Our system can be setup to allow for differ- ent number of workers and helpers. The charging station, battery level and the behaviors of these agents can also be varied. We discuss the affect of these different policies on the performance of the workers. The performance is measured by the number of times the environment area is covered. In conclusion we would measure the performance based on the number of times the environment is covered by the agents

A Task Hand-Off Framework for Multi-Robot Systems

Multi-robot systems have many uses such as cleaning, exploration, search and rescue. These robots operate under constraints such as communication, battery etc. In this thesis, we provide a method by which the robots can hand-off their current task to a new robot so that the given task can be continued without interruption. It is assumed that the task can be handed off to any other robot without losing the progress on the task. In the task hand-off framework, the robots complete as much of the task as possible before trying to replenish their resources (e.g., refuel). The robots must also make sure that the task is handed over to another robot before they go back to refuel. We demonstrate the task hand-off framework in the context of a battery constraint. The robots hand-off their current task once they are low on battery. The robots are divided into helpers and workers. The workers are the ones that perform the given task while the helpers wait at charging locations. Once a worker determines it is running out of battery it calls for help and switches behaviors with a helper. The new worker then takes over the task. This framework allows a user to model robot teams performing common robotic tasks such as exploration, coverage or any other task where the task can be easily handed-off without losing any progress on the task. We also present a simple priority based inter-robot contention resolution algorithm using motion replanning to avoid inter-robot collisions. Each robot is assigned a priority. Whenever the robots are close to each other, the lower priority robots halt and the highest priority robot replans a path around the robots by considering them as additional robots. We demonstrate the task hand-off framework approach using a physics based simulator that is built on top of a physics engine and also using physical hardware. The physical hardware consists of multiple iRobot Create robots with an onboard ASUS Netbook. We provide results from room 407 of the Harvey Bum Bright Building at Texas A&M University. We show that the tasks get completed faster with task hand-off than when task hand-off was not allowed

Techniques for modeling and analyzing RNA and protein folding energy landscapes

RNA and protein molecules undergo a dynamic folding process that is important to their function. Computational methods are critical for studying this folding pro- cess because it is difficult to observe experimentally. In this work, we introduce new computational techniques to study RNA and protein energy landscapes, includ- ing a method to approximate an RNA energy landscape with a coarse graph (map) and new tools for analyzing graph-based approximations of RNA and protein energy landscapes. These analysis techniques can be used to study RNA and protein fold- ing kinetics such as population kinetics, folding rates, and the folding of particular subsequences. In particular, a map-based Master Equation (MME) method can be used to analyze the population kinetics of the maps, while another map analysis tool, map-based Monte Carlo (MMC) simulation, can extract stochastic folding pathways from the map. To validate the results, I compared our methods with other computational meth- ods and with experimental studies of RNA and protein. I first compared our MMC and MME methods for RNA with other computational methods working on the com- plete energy landscape and show that the approximate map captures the major fea- tures of a much larger (e.g., by orders of magnitude) complete energy landscape. Moreover, I show that the methods scale well to large molecules, e.g., RNA with 200+ nucleotides. Then, I correlate the computational results with experimental findings. I present comparisons with two experimental cases to show how I can pre- dict kinetics-based functional rates of ColE1 RNAII and MS2 phage RNA and their mutants using our MME and MMC tools respectively. I also show that the MME and MMC tools can be applied to map-based approximations of protein energy energy landscapes and present kinetics analysis results for several proteins

Major Subject: Computer ScienceTECHNIQUES FOR MODELING AND ANALYZING RNA AND PROTEIN FOLDING ENERGY LANDSCAPES

Major Subject: Computer Scienceiii Techniques for Modeling and Analyzing RNA and Protein Folding Energ

Dynamics of pastoral traditional ecological knowledge : a global state-of-the-art review

Traditional ecological knowledge enables pastoralists to cope with social-ecological changes, thereby increasing thesustainability of their practices and fostering social-ecological resilience. Yet, there is a significant knowledge gap concerning the extentto which pastoral traditional ecological knowledge has changed over time at the global level. We aim to fill this gap through a systematicliterature review of 288 scientific studies on pastoral traditional ecological knowledge. We reviewed 152 papers in detail (selectedrandomly from the 288) for their content, and focused specifically on 61 papers that explicitly mentioned one of the four types ofknowledge transition (i.e., retention, erosion, adaptation, or hybridization). Studies on pastoral traditional knowledge represent lessthan 3% of all the scholarly literature on traditional ecological knowledge. Geographical distribution of the 288 case studies was largelybiased. Knowledge domains of pastoral knowledge such as herd and livestock management, forage and medicinal plants, and landscapeand wildlife were relatively equally covered; however, climate-related knowledge was less often studied. Of the 63 papers that explicitlymentioned transition of pastoral traditional ecological knowledge, 52 reported erosion, and only 11 studies documented explicitlyknowledge retention, adaptation, or hybridization of traditional knowledge. Thus, adaptation and hybridization was understudied,although some case studies showed that adaptation and hybridization of knowledge can efficiently help pastoralists navigate amongsocial-ecological changes. Based on the review, we found 13 drivers which were mentioned as the main reasons for knowledge transitionamong which social-cultural changes, formal schooling, abandonment of pastoral activities, and transition to a market economy weremost often reported. We conclude that future research should focus more on the diverse dynamics of pastoral traditional knowledge,be more careful in distinguishing the four knowledge transition types, and analyze how changes in knowledge impact change in pastoralpractices and lifestyles. Understanding these phenomena could help pastoralists' adaptations and support their stewardship of theirrangeland ecosystems and biocultural diversity.Peer reviewe

Techniques for modeling and analyzing RNA and protein folding energy landscapes

RNA and protein molecules undergo a dynamic folding process that is important to their function. Computational methods are critical for studying this folding pro- cess because it is difficult to observe experimentally. In this work, we introduce new computational techniques to study RNA and protein energy landscapes, includ- ing a method to approximate an RNA energy landscape with a coarse graph (map) and new tools for analyzing graph-based approximations of RNA and protein energy landscapes. These analysis techniques can be used to study RNA and protein fold- ing kinetics such as population kinetics, folding rates, and the folding of particular subsequences. In particular, a map-based Master Equation (MME) method can be used to analyze the population kinetics of the maps, while another map analysis tool, map-based Monte Carlo (MMC) simulation, can extract stochastic folding pathways from the map. To validate the results, I compared our methods with other computational meth- ods and with experimental studies of RNA and protein. I first compared our MMC and MME methods for RNA with other computational methods working on the com- plete energy landscape and show that the approximate map captures the major fea- tures of a much larger (e.g., by orders of magnitude) complete energy landscape. Moreover, I show that the methods scale well to large molecules, e.g., RNA with 200+ nucleotides. Then, I correlate the computational results with experimental findings. I present comparisons with two experimental cases to show how I can pre- dict kinetics-based functional rates of ColE1 RNAII and MS2 phage RNA and their mutants using our MME and MMC tools respectively. I also show that the MME and MMC tools can be applied to map-based approximations of protein energy energy landscapes and present kinetics analysis results for several proteins

Swarming behavior using probabilistic roadmap techniques

Abstract. While techniques exist for simulating swarming behaviors, these methods usually provide only simplistic navigation and planning capabilities. In this review, we explore the benefits of integrating roadmapbased path planning methods with flocking techniques to achieve different behaviors. We show how group behaviors such as exploring can be facilitated by using dynamic roadmaps (e.g., modifying edge weights) as an implicit means of communication between flock members. Extending ideas from cognitive modeling, we embed behavior rules in individual flock members and in the roadmap. These behavior rules enable the flock members to modify their actions based on their current location and state. We propose new techniques for several distinct group behaviors: homing, exploring (covering and goal searching), passing through narrow areas and shepherding. We present results that show that our methods provide significant improvement over methods that utilize purely local knowledge and moreover, that we achieve performance approaching that which could be obtained by an ideal method that has complete global knowledge. Animations of these behaviors can be viewed on our webpages.