A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence

Effects of display design on signal detection in flash flood forecasting

The Flooded Locations and Simulated Hydrographs (FLASH) project is a suite of tools that use weather radar-based rainfall estimates to force hydrologic models to predict flash floods in real-time. However, early evaluation of FLASH tools in a series of simulated forecasting operations, it was believed that the data aggregation and visualization methods might have contributed to forecasting a large number of false alarms. The present study addresses the question of how two alternative data aggregation and visualization methods affect signal detection of flash floods. A sample of 30 participants viewed a series of stimuli created from FLASH images and were asked to judge whether or not they predicted significant or insignificant amounts of flash flooding. Analyses revealed that choice of aggregation method did affect probability of detection. Additional visual indicators such as geographic scale of the stimuli and threat level affected the odds of interpreting the model predictions correctly as well as congruence in responses between national and local scale model outputs

Collaborative geographic visualization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative visualization purposes. Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment

Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

Informavores: Active information foraging and human cognition

Just as the body survives by ingesting negative entropy, so the mind survives by ingesting information. In a very general sense, all higher organisms are informavores. The study of active information search is in the midst of a renaissance. Psychological research from diverse areas ranging from developmental psychology This symposium aims to bring together leading experts in this area to discuss how active information foraging can be understood from a diverse set of perspectives within cognitive science. Key themes include how prior knowledge influences search (Markant & Gureckis), how information and reward interact to determine choice (Meder & Nelson), developmental patterns in information seeking behavior (Nelson et al.), information foraging in complex sensemaking tasks (Pirolli), and the allocation of attention during statistical word learning (Yu). While each represents a distinct area of research, all discussants in the symposium share a core approach of applying computational models to understand information search in humans. The symposium should appeal to a broad set of attendees including educators, developmental psychologists, cognitive modelers, and computer scientists. The influence of priors on sequential search decisions - Doug Markant and Todd Gureckis Normative models of information acquisition predict that people's search decisions should be strongly influenced by their prior beliefs, which capture the set of alternative hypotheses they are considering. In the present experiments we tested whether people adjusted their information search behavior in response to sequential changes in the prior. Participants played a search game in which they had to identify the shape and location of multiple hidden targets in a display (similar to the board game Battleship). During the task they were told that the set of possible shapes had changed, and the key question was whether they would adjust their search decisions according to the predictions of a normative model. Manipulations of the prior included changes in the frequency of certain classes of targets as well as the introduction of higherorder constraints (e.g., that all targets would have the same shape). The results showed that an individual's prior could be recovered from their sequences of search decisions, but that there were notable differences in their ability to adjust to certain changes in the hypothesis space, an effect that is not predicted by the normative model. We discuss the implications of these findings for how people generate and represent hypotheses during the course of information foraging. Is people's information search behavior sensitive to different reward structures? -Björn Meder and Jonathan Nelson In situations where humans actively acquire information for classification, information search preferentially maximizes accurac

Integrating Citizen Science and GIS for Wildlife Habitat Assessment

With the rapid advancement and popularity of geospatial technologies such as location-aware smartphones, mobile maps, etc., average citizens nowadays can easily contribute georeferenced wildlife data (e.g., wildlife sightings). Due to the wide spread of human settlements and lengthy living histories of citizens in their local areas, citizen-contributed wildlife data could cover large geographic areas over long time spans. Citizen science thus provides great opportunities for collecting wildlife data of extensive spatiotemporal coverage for wildlife habitat assessment. However, citizen-contributed wildlife data may be subject to data quality issues, for example, imprecise spatial position and biased spatial coverage. These issues need to be accounted for when using citizen-contributed data for wildlife habitat assessment. Geovisualization and geospatial analysis capabilities provisioned by geographic information systems (GISs) can be adopted to tackle such data quality issues. This chapter offers an overview of citizen science as a means of collecting wildlife data, the roles of GIS to tackle the data quality issues, and the integration of citizen science and GIS for wildlife habitat assessment. A case study of habitat assessment for the black-and-white snub-nosed monkey (Rhinopithecus bieti) using R. bieti sightings elicited from local villagers in Yunnan, China, is presented as a demonstration

Assessing the Impact of Restored Wetlands on Bat Foraging Activity Over Nearby Farmland

Up to 87% of the world’s wetlands have been destroyed, considerably reducing ecosystem services these wetlands once provided. More recently, many wetlands are being restored in an attempt to regain their ecosystem service. This study seeks to determine the effects of restored wetlands on local bat habitat use. Bat activity was found to be significantly higher around the wetlands when compared to distant grassy fields; however, no significant difference was found among the restored wetlands and a remote cattle farm containing multiple water features. Geospatial models of bat distribution and bat foraging were produced using machine learning that showed higher habitat suitability and foraging activity around restored wetlands than around distant grassy fields, suggesting that wetlands provide vital habitat for insectivorous bats. This study demonstrates that restored wetlands promote bat activity and bat foraging, and restoring wetlands may be a useful means of increasing natural pest control over nearby farmlands