scholarly journals Learning and Planning Under Uncertainty for Green Security

2021 ◽  
Author(s):  
Lily Xu
Keyword(s):  
Environmental Conservation ◽  
Coast Guard ◽  
Planning Under Uncertainty ◽  
Illegal Logging ◽  
Complex Environments ◽  
Uncertain Environments ◽  
Scarce Resources ◽  
Stochastic Environments ◽  
Adversarial Behavior ◽  
Effective Development

Green security concerns the protection of the world's wildlife, forests, and fisheries from poaching, illegal logging, and illegal fishing. Unfortunately, conservation efforts in green security domains are constrained by the limited availability of defenders, who must patrol vast areas to protect from attackers. Artificial intelligence (AI) techniques have been developed for green security and other security settings, such as US Coast Guard patrols and airport screenings, but effective deployment of AI in these settings requires learning adversarial behavior and planning in complex environments where the true dynamics may be unknown. My research develops novel techniques in machine learning and game theory to enable the effective development and deployment of AI in these resource-constrained settings. Notably, my work has spanned the pipeline from learning in a supervised setting, planning in stochastic environments, sequential planning in uncertain environments, and deployment in the real world. The overarching goal is to optimally allocate scarce resources under uncertainty for environmental conservation.

An interactive identification key for Atlantic Forest tree species based on macroscopic wood anatomy

Rodriguésia ◽  
2021 ◽  
Vol 72 ◽  
Author(s):  
Arno Fritz das Neves Brandes ◽  
Bruno Quiroga Novello ◽  
Thaís Siston ◽  
Leonardo Bona do Nascimento ◽  
Neusa Tamaio ◽  
...  
Keyword(s):  
Atlantic Forest ◽  
Tree Species ◽  
Wood Anatomy ◽  
Forest Tree ◽  
Environmental Conservation ◽  
Identification Key ◽  
Illegal Logging ◽  
Forest Tree Species ◽  
Commercial Logging ◽  
Industrial Forestry

Abstract The Atlantic Forest is considered a biodiversity hotspot because of its exceptional species richness, endemism, and habitat losses. Commercial logging, industrial forestry, and agriculture represent threats to the Atlantic Forest, and even though it has been protected by law since 2006, forest suppression continues and large volumes of Atlantic Forest wood are traded every year. To promote environmental conservation and prevent illegal logging, the verification of wood species’ identifications is fundamental throughout several stages of the wood supply chain by supervisory bodies, traders, and even consumers. Macroscopic wood anatomy analysis has been shown to be an efficient method for screening, although tools to streamline the efficiency of that process are necessary. We introduce here an interactive identification key for Atlantic Forest tree species, based on standard wood macroscopic features that is now available online at http://gbg.sites.uff.br/lamad/.

scholarly journals Two-Volume Contingency Plans in Alaska Provide Focused Information for Responders & Regulators

2014 ◽  
Vol 2014 (1) ◽  
pp. 299651
Author(s):  
Lydia Miner ◽  
Robert Klieforth ◽  
Eppie Hogan
Keyword(s):  
Environmental Protection Agency ◽  
Action Plan ◽  
Regulatory Compliance ◽  
Environmental Conservation ◽  
Coast Guard ◽  
Specific Response ◽  
Response Planning ◽  
Contingency Plans ◽  
Volume Response ◽  
Department Of Environmental Conservation

Oil discharge prevention and contingency plans (ODPCPs) have been required under Alaska statutes and regulations for oil exploration, production, storage, and transportation facilities since 1992. BP Exploration (Alaska) Inc. (BPXA) has prepared and submitted their North Slope ODPCPs (Milne Point, Endicott, Greater Prudhoe Bay, and Northstar) as a single volume for each facility under these requirements. However, in 2011, when the four plans were renewed, BPXA elected to present their ODPCPs in two volumes for each facility. The purpose of this organizational change from one to two volumes was to focus information in each volume; the first volume is a stand-alone Emergency Action Plan for spill responders, dedicated to spill response planning and preparedness, and the second volume is dedicated to spill prevention requirements and procedures. The 2-volume edition allows BPXA's plan writers, operators, and regulators to concentrate on specific response or prevention topics and regulatory compliance. The 2-volume plan is easier to use and revise through the amendment process. This approach is allowed under Alaska regulations and was embraced by the Alaska Department of Environmental Conservation. Federal regulators (Bureau of Safety and Environmental Enforcement, US Environmental Protection Agency, US Coast Guard, and US Department of Transportation) have reviewed and approved the 2-volume response plans as well. According to regulators, with such large ODPCPs, the effort to maintain publication efficiency during public review creates a potential risk of confusion or lack of sufficient detail, which may lead to comments that focus on form or style, rather than content. Working with two volumes circumvented this potential problem. Due to the size and lengthy history of the facilities, an comprehensive Alaska regulations governing the contents of ODPCPs, two volumes allowed BPXA to include all of the necessary information for the plans without creating a storage or ergonomic problem for the reviewers. Regular users of the ODPCPs at the BPXA facilities have found that working with a smaller, more focused volume is more efficient.

Reliability-Based Multi-Vehicle Path Planning Under Uncertainty Using a Bio-Inspired Approach

2021 ◽  
pp. 1-44
Author(s):  
Yixuan Liu ◽  
Chen Jiang ◽  
Xiaoge Zhang ◽  
Zissimos P. Mourelatos ◽  
Dakota Barthlow ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Dynamics Simulation ◽  
Computational Time ◽  
Soil Conditions ◽  
Planning Under Uncertainty ◽  
Uncertain Environments ◽  
Multiple Vehicles ◽  
Single Vehicle ◽  
Vehicle Path

Abstract Identifying a reliable path in uncertain environments is essential for designing reliable off-road autonomous ground vehicles (AGV) considering post-design operations. This paper presents a novel bio-inspired approach for model-based multi-vehicle mission planning under uncertainty for off-road AGVs subjected to mobility reliability constraints in dynamic environments. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility (i.e., maximum attainable speed) for any given terrain and soil conditions. Based on physics-based simulations, the vehicle state mobility reliability in operation is then analyzed using an adaptive surrogate modeling method to overcome the computational challenges in mobility reliability analysis by adaptively constructing a surrogate. Subsequently, a bio-inspired approach called Physarum-based algorithm is used in conjunction with a navigation mesh to identify an optimal path satisfying a specific mobility reliability requirement. The developed Physarum-based framework is applied to reliability-based path planning for both a single-vehicle and multiple-vehicle scenarios. A case study is used to demonstrate the efficacy of the proposed methods and algorithms. The results show that the proposed framework can effectively identify optimal paths for both scenarios of a single and multiple vehicles. The required computational time is less than the widely used Dijkstra-based method.

scholarly journals Applications of DEC-MDPs in Multi-Robot Systems

Robotics ◽  
2013 ◽  
pp. 143-165
Author(s):  
Aurélie Beynier ◽  
Abdel-Illah Mouaddib
Keyword(s):  
Decentralized Control ◽  
Decision Processes ◽  
Decision Problems ◽  
Large Set ◽  
Complex Environments ◽  
Uncertain Environments ◽  
Robot Systems ◽  
Markov Decision ◽  
Partially Observable ◽  
Multi Robot

Optimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision problems are encountered while developing exploration rovers, teams of patrolling robots, rescue-robot colonies, mine-clearance robots, et cetera. In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We first describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decentralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs.

scholarly journals ROLE OF CAP GREENING IN FARMERS` DECISION-MAKING

2019 ◽  
Vol 17 (Suppl.1) ◽  
pp. 600-605
Author(s):  
Monika Kabadzhova
Keyword(s):  
Decision Making ◽  
Agricultural Practices ◽  
Environmentally Friendly ◽  
Environmental Conservation ◽  
The Sustainable Development ◽  
Scarce Resources ◽  
Ecological Focus Areas ◽  
Resources Conservation ◽  
Cross Compliance

CAP Greening is an important part of the sustainable development of scarce resources, conservation of habitats and species diversity. The aim of the study is to examine the role of mandatory requirements resulting from the CAP greening for farm development and environmental conservation. Analysis of available literature data on the CAP greening direct payments was conducted in order to highlight environmentally friendly agricultural practices. The results show that climate and environmentally friendly agrucultural practices are set out in three groups as follows: crop diversification, maintenance of permanent grassland and maintaining minimum 5% of the farming's area as ecological focus areas. In conclusion, farmers should consider these activities during the decision-making process at farm level and have to comply with the mandatory requirements of cross-compliance.

PLACES OF REFUGE GUIDELINES

2005 ◽  
Vol 2005 (1) ◽  
pp. 311-315
Author(s):  
John Bauer ◽  
Jean Cameron ◽  
Larry Iwamoto
Keyword(s):  
Decision Making ◽  
Oil Spill ◽  
Task Force ◽  
Environmental Conservation ◽  
Coast Guard ◽  
Federal State ◽  
The Us ◽  
Tribal Governments ◽  
Us Coast Guard ◽  
Department Of Environmental Conservation

ABSTRACT The Pacific States/British Columbia Oil Spill Task Force (Oil Spill Task Force) and the Alaska Regional Response Team (RRT) are collaborating to develop decision-making and planning guidelines which “operationalize” the International Maritime Organization's Places of Refuge guidelines. These guidelines will incorporate the authorities of the US and Canadian Coast Guards, state, provincial, local, and tribal governments, and resource agencies. The decision-making section of the guidelines provides step-by-step procedures and checklists to analyze the risks of allowing a ship in need of assistance to proceed to a place of refuge. The planning section of the guidelines provides a process to pre-identify information necessary for responding to requests for places of refuge and identifying potential places of refuge prior to an incident. The Oil Spill Task Force effort involves a workgroup of regional stakeholders co-chaired by the Task Force agencies and the US Coast Guard, Pacific Area. The separate Alaska initiative is being accomplished by a workgroup of the Alaska RRT co-chaired by the Alaska Department of Environmental Conservation (ADEC) and US Coast Guard, District 17. Both projects are developing concurrently and include persons serving as liaisons between the two efforts in order to promote consistency and share information. The Oil Spill Task Force Guidelines provide a template for member states and the province to use in developing decisionmaking and pre-incident plans tailored to their area. The Alaska guidelines were drafted concurrently with the Oil Spill Task Force process, and sections of their guidelines were modified to reflect area-wide conditions. The Oil Spill Task Force's final guidelines are to be used as a planning annex to US Area Contingency Plans on the West Coast. Alaska will include their guidelines in the Federal/State Unified Plan and subarea plans. Transport Canada and Canadian Coast Guard authorities will adapt the guidelines as appropriate for Canada.

scholarly journals Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder

2019 ◽  
Vol 9 (2) ◽  
pp. 323 ◽  
Author(s):  
Junjie Zeng ◽  
Long Qin ◽  
Yue Hu ◽  
Cong Hu ◽  
Quanjun Yin
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Large Scale ◽  
Complex Environments ◽  
Uncertain Environments ◽  
Simulation Experiments ◽  
Planning Framework ◽  
Optimal Motion Planning ◽  
Path Lengths ◽  
Reward Functions

In this paper, we present a hierarchical path planning framework called SG–RL (subgoal graphs–reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By “rational”, we mean (1) efficient path planning to eliminate first-move lags; (2) collision-free and smooth for agents with kinematic constraints satisfied. SG–RL works in a two-level manner. At the first level, SG–RL uses a geometric path-planning method, i.e., simple subgoal graphs (SSGs), to efficiently find optimal abstract paths, also called subgoal sequences. At the second level, SG–RL uses an RL method, i.e., least-squares policy iteration (LSPI), to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments. The second advantage is that, when the environment changes slightly (i.e., unexpected obstacles appearing), SG–RL does not need to reconstruct subgoal graphs and replan subgoal sequences using SSGs, since LSPI can deal with uncertainties by exploiting its generalization ability to handle changes in environments. Simulation experiments in representative scenarios demonstrate that, compared with existing methods, SG–RL can work well on large-scale maps with relatively low action-switching frequencies and shorter path lengths, and SG–RL can deal with small changes in environments. We further demonstrate that the design of reward functions and the types of training environments are important factors for learning feasible policies.

Impacts of nutrients and related environmental factors on distribution and size structure of Noctiluca scintillans populations of the Eutrophic Tha Chin Estuary, Thailand

2012 ◽  
Vol 65 (11) ◽  
pp. 1994-2002
Author(s):  
Wansiri Chuenniyom ◽  
Charumas Meksumpun ◽  
Shettapong Meksumpun
Keyword(s):  
Total Population ◽  
Size Structure ◽  
Red Tide ◽  
Inorganic Nitrogen ◽  
Outer Part ◽  
Environmental Conservation ◽  
Water Quality Standards ◽  
Noctiluca Scintillans ◽  
Field Surveys ◽  
Effective Development

This study aimed to analyze the impacts of nutrients and related aquatic factors on changes in the Noctiluca population of the Tha Chin estuary, a nutrient-rich estuary located in the inner Gulf of Thailand. Field surveys were carried out at 30 stations during November 2009 to August 2010. The results indicated high levels of dissolved inorganic nitrogen (DIN; 13.89–46.99 μmol/L) and PO43−-P (0.20–3.05 μmol/L) where the Noctiluca red tide occurred, particularly during the high-loading period. Dense populations were usually found in the outer part of the estuary with comparatively high salinity (25–29 psu). The highest Noctiluca density was 72,333 cells L−1 and the cell diameters ranged between 360 and 460 μm. Proportions of small-sized cells (Ps; less than 300 μm) varied over time. In this study, Ps showed a positive correlation with levels of PO43−-P, while the total population density was significantly affected by levels of NH4+-N and DIN (p < 0.05). Overall, PO43−-P influenced the development of the Noctiluca red tide, with the limitation of PO43−-P levels to below 1 μmol/L suggested for controlling Noctiluca red tide outbreaks at their origin. To support environmental conservation and maintain sustainable production in the estuary, the levels of PO43−-P should be considered for the further effective development of water quality standards in estuarine zones.

scholarly journals Collective learning from individual experiences and information transfer during group foraging

2019 ◽  
Vol 16 (151) ◽  
pp. 20180803 ◽  
Author(s):  
Andrea Falcón-Cortés ◽  
Denis Boyer ◽  
Gabriel Ramos-Fernández
Keyword(s):  
Information Transfer ◽  
Collective Motion ◽  
Empirical Studies ◽  
Collective Learning ◽  
Complex Environments ◽  
Uncertain Environments ◽  
Modelling Framework ◽  
Current State ◽  
Individual Memory ◽  
State Of The Environment

Living in groups brings benefits to many animals, such as protection against predators and an improved capacity for sensing and making decisions while searching for resources in uncertain environments. A body of studies has shown how collective behaviours within animal groups on the move can be useful for pooling information about the current state of the environment. The effects of interactions on collective motion have been mostly studied in models of agents with no memory. Thus, whether coordinated behaviours can emerge from individuals with memory and different foraging experiences is still poorly understood. By means of an agent-based model, we quantify how individual memory and information fluxes can contribute to improving the foraging success of a group in complex environments. In this context, we define collective learning as a coordinated change of behaviour within a group resulting from individual experiences and information transfer. We show that an initially scattered population of foragers visiting dispersed resources can gradually achieve cohesion and become selectively localized in space around the most salient resource sites. Coordination is lost when memory or information transfer among individuals is suppressed. The present modelling framework provides predictions for empirical studies of collective learning and could also find applications in swarm robotics and motivate new search algorithms based on reinforcement.

A HISTORY OF THE DEVELOPMENT OF OIL DISPERSANT GUIDELINES FOR ALASKA

1987 ◽  
Vol 1987 (1) ◽  
pp. 189-191
Author(s):  
Michael A. Conway
Keyword(s):  
Environmental Protection Agency ◽  
Control Method ◽  
Environmental Conservation ◽  
Coast Guard ◽  
Inlet Section ◽  
Private Industry ◽  
Cooperative Effort ◽  
Oil Dispersant ◽  
Spill Control ◽  
Department Of Environmental Conservation

ABSTRACT The Oil Dispersant Guidelines for Alaska, Cook Inlet Section, were implemented on August 6, 1986, when the U.S. Environmental Protection Agency, U.S. Coast Guard, and Alaska Department of Environmental Conservation signed a Memorandum of Agreement. State and federal agencies, private industry, commercial fishermen, and environmentalists had to work together toward this achievement. Without this cooperative effort, there would be no planning for effective dispersant use in Alaska as a spill control method.

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