2010 — 2016 |
Pratt, Stephen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Cif: Large: Collaborative Research: Cooperation and Learning Over Cognitive Networks @ Arizona State University
Cooperation and Learning over Cognitive Networks Studies on herding and self-organization in economics and the social and biological sciences have observed that coordination among multiple agents leads to regular patterns of behavior and swarm intelligence, even when each group member shows limited behavioral complexity. In ant colonies, for example, individual ants cannot capture rich spatial information from their environment because of their limited sensing ability. Nevertheless, when the ants coordinate their activities within a colony, the group ends up exhibiting better sensing abilities. Using signal processing and communications techniques, the research studies how and why such manifestations of rational and organized behavior arise at the group level from local interactions among agents with limited abilities, what communication topologies enable such behavior, and what type of signal processing enables such formations.
This research seeks to understand and reverse-engineer the distributed intelligence encountered in socio-economic-biological networks, by investigating relations with learning and rationality over cognitive networks. The latter are adaptive networks that avoid centralized information processing and perform in-network inference and control decisions. Cognitive networks contrast with networks that rely on centralized and parallel information fusion, which are not scalable, are hard to adapt to changing topologies, and suffer from points of vulnerability and information bottlenecks. The research considers large scale networks of agents and studies how global (rational or irrational) patterns of behavior emerge, including herds, contagions and bubbles in economics. An understanding of how the biotic environment influences collective behavior in animal societies provides a real world guide to good cognitive networks, which can be used in turn to design engineered systems. Cognitive networks have applications in areas ranging from precision agriculture, to environmental monitoring, disaster relief management, and smart spaces.
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2010 — 2014 |
Pratt, Stephen |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Automating the Large-Scale Measurement of Insect Behavior @ Arizona State University
The Georgia Institute of Technology and Arizona State University are awarded grants to develop an integrated approach to automating measurements of insect behavior from video records. The study of insect behavior plays a fundamental role in biology, but progress is limited by the rate at which data can be gathered. Researchers have relied largely on direct observation or time-consuming manual annotation of video records. This project will create an automated solution that combines theory, algorithms, software modules, and databases of behavior measurements. These tools will be widely applicable to studies of animal behavior, but development will focus on the particularly rich and challenging problems offered by ants, where multiple interacting animals must be simultaneously tracked. Current multi-tracking technologies are limited in their ability to deal with the huge degree of target interaction in this context, including significant periods of occlusion of one target by another. This project will generate a novel approach that applies the graph-cut optimization method to video object segmentation. This method will be able to identify which portions of the video correspond to distinct targets even when they overlap. Accurate target segmentation will also facilitate more accurate adaptation to changes in appearance due to lighting or other environmental effects. The project will also develop novel behavior recognition methods that infer behavior from target configuration and appearance. Unlike traditional methods this approach will not rely on the state of the tracker and thus will avoid the compounding of recognition errors by tracking errors.
Two cross-cutting themes inform this project. The first is a focus on algorithms and methods compatible with modular software tools, thus allowing biologists to develop a customized solution to a wide range of sensing tasks. The second theme is the utilization of state-of-the-art ultra-high resolution imaging sensors to obtain more information about ant behavior and identity than is currently possible. These capabilities will enable insect biologists to frame and answer research questions that exceed the limited data collection capabilities of current methods. Algorithms and software modules will be widely disseminated, to maximize their power to transform biology in a more general setting. For more information visit the project website at http://www.kinetrack.org/
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2016 — 2019 |
Pratt, Stephen Walker, Sara [⬀] Pavlic, Theodore (co-PI) [⬀] Kim, Hyunju (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Emergent Computation in Collective Decision Making by the Crevice-Dwelling Rock Ant Temnothorax Rugatulus @ Arizona State University
From the level of chemical reaction networks within cells to the social structures of higher organisms, biological systems seem to take advantage of distributed computation to perform a myriad of complex functions. However, rigorous quantification of how life stores, processes and propagates information at the various levels of organization observed in biological systems has remained elusive. In this project the PIs will utilize recently developed information-theoretic tools from complex systems research, typically applied to artificial life systems, to assess how a real biological system manages distributed information to perform a collective computational task. This research will provide new applications of mathematical and computational tools for use by scientists and will provide important insights in issues of broader concern such as colony collapse disorder observed in honeybees. The results will be disseminated to the general public through a variety of media as part of the outreach efforts of the PI and Co-Is.
Specifically, the PIs focus on characterizing information storage, processing, and propagation in colonial decision making as a rigorous case-study for understanding the physics of collective computation in living systems. The aim is to address several outstanding questions in insect behavior, by applying a novel theoretical framework treating collective decision making as a computation to the problem of collective nest site choice by the ant Temnothorax rugatulus. To this end, the investigators will implement a novel synthesis of two levels of theoretical investigation, agent-based modeling and information-theoretic analysis of experimental and simulated data sets, with experimental manipulation to determine the mechanisms of information processing among individuals that lead to global, emergent computation in Temnothorax rugatulus colonies. With this novel framework, the PIs will address several important outstanding questions about the physics governing collective choice, including the role of negative feedback and under what conditions collective wins over individual rationality. The research will provide one of the first detailed studies expanding the wealth of knowledge on emergent computation in complex systems to the biological realm. This study will inform our understanding of fundamental aspects of biological computation that will provide new perspectives on the physics of living systems by illuminating how life stores, processes, and propagates information. Due to the ease of manipulation of individuals within eusocial-insect societies, the PIs will be able to probe the minimal set of individual-level features necessary for spontaneous distributed computation.
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