scholarly journals Inferring Team Task Plans from Human Meetings: A Generative Modeling Approach with Logic-Based Prior

2015 ◽  
Vol 52 ◽  
pp. 361-398 ◽  
Author(s):  
Been Kim ◽  
Caleb M. Chacha ◽  
Julie A. Shah
Keyword(s):  
Disaster Response ◽  
Autonomous Systems ◽  
Hybrid Approach ◽  
Solution Space ◽  
Large Solution ◽  
Team Planning ◽  
Generative Modeling ◽  
Processed Form ◽  
Plan Inference ◽  
Time Critical

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed-upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our hybrid approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans, enabling us to overcome the challenge of performing inference over a large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentations and show that it is able to infer a human team's final plan with 86% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work to integrate a logical planning technique within a generative model to perform plan inference.

scholarly journals Summarizing the solution space in tumor phylogeny inference by multiple consensus trees

2019 ◽  
Vol 35 (14) ◽  
pp. i408-i416 ◽  
Author(s):  
Nuraini Aguse ◽  
Yuanyuan Qi ◽  
Mohammed El-Kebir
Keyword(s):  
Solution Space ◽  
Simulated Data ◽  
Exact Algorithm ◽  
Real Data ◽  
Supplementary Information ◽  
Mixed Integer ◽  
Consensus Tree ◽  
Large Solution ◽  
Consensus Trees ◽  
Topological Features

Abstract Motivation Cancer phylogenies are key to studying tumorigenesis and have clinical implications. Due to the heterogeneous nature of cancer and limitations in current sequencing technology, current cancer phylogeny inference methods identify a large solution space of plausible phylogenies. To facilitate further downstream analyses, methods that accurately summarize such a set T of cancer phylogenies are imperative. However, current summary methods are limited to a single consensus tree or graph and may miss important topological features that are present in different subsets of candidate trees. Results We introduce the Multiple Consensus Tree (MCT) problem to simultaneously cluster T and infer a consensus tree for each cluster. We show that MCT is NP-hard, and present an exact algorithm based on mixed integer linear programming (MILP). In addition, we introduce a heuristic algorithm that efficiently identifies high-quality consensus trees, recovering all optimal solutions identified by the MILP in simulated data at a fraction of the time. We demonstrate the applicability of our methods on both simulated and real data, showing that our approach selects the number of clusters depending on the complexity of the solution space T. Availability and implementation https://github.com/elkebir-group/MCT. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Solid Rocket Motor Design Using Hybrid Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Kevin Albarado ◽  
Roy Hartfield ◽  
Wade Hurston ◽  
Rhonald Jenkins
Keyword(s):  
Hybrid Approach ◽  
Optimal Solution ◽  
Solution Space ◽  
Axial Direction ◽  
Pattern Search ◽  
Rocket Motor ◽  
Solid Rocket Motor ◽  
Thin Sections ◽  
Solid Rocket

A particle swarm/pattern search hybrid optimizer was used to drive a solid rocket motor modeling code to an optimal solution. The solid motor code models tapered motor geometries using analytical burn back methods by slicing the grain into thin sections along the axial direction. Grains with circular perforated stars, wagon wheels, and dog bones can be considered and multiple tapered sections can be constructed. The hybrid approach to optimization is capable of exploring large areas of the solution space through particle swarming, but is also able to climb “hills” of optimality through gradient based pattern searching. A preliminary method for designing tapered internal geometry as well as tapered outer mold-line geometry is presented. A total of four optimization cases were performed. The first two case studies examines designing motors to match a given regressive-progressive-regressive burn profile. The third case study studies designing a neutrally burning right circular perforated grain (utilizing inner and external geometry tapering). The final case study studies designing a linearly regressive burning profile for right circular perforated (tapered) grains.

Analysis of Pavement Management Activities Programming by Genetic Algorithms

1998 ◽  
Vol 1643 (1) ◽  
pp. 1-6 ◽  
Author(s):  
T. F. Fwa ◽  
W. T. Chan ◽  
K. Z. Hoque
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Solution Space ◽  
Pavement Management ◽  
Practical Significance ◽  
Optimization Analysis ◽  
Large Solution ◽  
Pavement Management System ◽  
Pavement Maintenance ◽  
Operational Characteristics

The application of genetic algorithms to programming of pavement maintenance activities at the network level is demonstrated. The operational characteristics of the genetic algorithm technique and its relevance to solving the programming problem in a Pavement Management System (PMS) are discussed. The robust search capability of genetic algorithms enables them to effectively handle the highly constrained problem of pavement management activities programming, which has an extremely large solution space of astronomical scale. Examples are presented to highlight the versatility of genetic algorithms in accommodating different objective function forms. This versatility makes the algorithms an effective tool for planning in PMS. It is also demonstrated that composite objective functions that combine two or more different objectives can be easily considered without having to reformulate the genetic algorithm computer program. Another useful feature of genetic algorithm solutions is the availability of near-optimal solutions besides the "best" solution. This has practical significance as it gives the users the flexibility to examine the suitability of each solution when practical constraints and factors not included in the optimization analysis are considered.

Modeling Individuals’ Activity-Travel Rescheduling Heuristics: Theory and Numerical Experiments

2002 ◽  
Vol 1807 (1) ◽  
pp. 16-25 ◽  
Author(s):  
Chang-Hyeon Joh ◽  
Theo Arentze ◽  
Harry Timmermans
Keyword(s):  
Decision Making ◽  
Numerical Experiments ◽  
Solution Space ◽  
Combinatorial Problem ◽  
Face Validity ◽  
Decision Making Process ◽  
Large Solution ◽  
Numerical Illustration ◽  
The Face ◽  
Actual Decision

Previously, a theory of activity-travel rescheduling decisions was developed. This theory left open the problem of how individuals deal with the combinatorial problem of a very large solution space. Based on the argument that an appropriate algorithm should also be interpreted as a representation of an actual decision-making process, such an algorithm for activity-travel rescheduling is proposed here. Details are described, and a numerical illustration is provided to explore the face validity of the proposed algorithm.

scholarly journals A VERSATILE UAV NEAR REAL-TIME MAPPING SOLUTION FOR DISASTER RESPONSE – CONCEPT, IDEAS AND IMPLEMENTATION

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 429-435
Author(s):  
P. Fanta-Jende ◽  
D. Steininger ◽  
F. Bruckmüller ◽  
C. Sulzbachner
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
Cost Effective ◽  
Semantic Segmentation ◽  
Sensor Technology ◽  
Mapping System ◽  
Stakeholder Perspective ◽  
Time Critical ◽  
Mapping Solution ◽  
Further Development

Abstract. In recent years, the proliferation and further development of unmanned aerial vehicles (UAVs) led to a great number of key technologies, advances and opportunities especially in the realm of time-critical applications. UAVs as a platform provide a unique combination of flexibility, affordability and sensor technology which enables the design of cost-effective and intriguing services particularly for disaster response. This contribution presents a concept for UAV-based near real-time mapping system for disaster relief to provide decision-making support for first responders particularly for possible disaster scenarios in Austria. We outline our system concept and its respective architecture, discuss requirements from a stakeholder perspective as well as legal regulations and initiatives at an EU level. In the methodology section of this paper, the preliminary data processing pipeline with respect to the near real-time orthomosaic generation and the semantic segmentation network are presented. Lastly, first experimental results of the pipeline are shown, and further advances are discussed.

scholarly journals DDPG-Based Energy-Efficient Flow Scheduling Algorithm in Software-Defined Data Centers

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zan Yao ◽  
Ying Wang ◽  
Luoming Meng ◽  
Xuesong Qiu ◽  
Peng Yu
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Network Performance ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Solution Space ◽  
Large Solution ◽  
Scheduling Scheme ◽  
Saving Effect

With the rapid development of data centers, the energy consumption brought by more and more data centers cannot be underestimated. How to intelligently manage software-defined data center networks to reduce network energy consumption and improve network performance is becoming an important research subject. In this paper, for the flows with deadline requirements, we study how to design the rate-variable flow scheduling scheme to realize energy-saving and minimize the mean completion time (MCT) of flows based on meeting the deadline requirement. The flow scheduling optimization problem can be modeled as a Markov decision process (MDP). To cope with a large solution space, we design a DDPG-EEFS algorithm to find the optimal scheduling scheme for flows. The simulation result reveals that the DDPG-EEFS algorithm only trains part of the states and gets a good energy-saving effect and network performance. When the traffic intensity is small, the transmission time performance can be improved by sacrificing a little energy efficiency.

scholarly journals Review: Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for Disaster Response, Civil Security and Public Safety

Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 59 ◽  
Author(s):  
Hanno Hildmann ◽  
Ernö Kovacs
Keyword(s):  
Disaster Response ◽  
Autonomous Systems ◽  
Cost Effective ◽  
Mobile Sensing ◽  
Sensors And Actuators ◽  
Wide Range ◽  
Near Term ◽  
Commercial Applications ◽  
Rich Data ◽  
Future Work

The use of UAVs in areas ranging from agriculture over urban services to entertainment or simply as a hobby has rapidly grown over the last years. Regarding serious/commercial applications, UAVs have been considered in the literature, especially as mobile sensing/actuation platforms (i.e., as a delivery platform for an increasingly wide range of sensors and actuators). With regard to timely, cost-effective and very rich data acquisition, both, NEC Research as well as TNO are pursuing investigations into the use of UAVs and swarms of UAVs for scenarios where high-resolution requirements, prohibiting environments or tight time constraints render traditional approaches ineffective. In this review article, we provide a brief overview of safety and security-focused application areas that we identified as main targets for industrial and commercial projects, especially in the context of intelligent autonomous systems and autonomous/semi-autonomously operating swarms. We discuss a number of challenges related to the deployment of UAVs in general and to their deployment within the identified application areas in particular. As such, this article is meant to serve as a review and overview of the literature and the state-of-the-art, but also to offer an outlook over our possible (near-term) future work and the challenges that we will face there.

scholarly journals An Algorithm for the Broad Evaluation of Potential Matches between Actuator Concepts and Heavy-Duty Mobile Applications

Actuators ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 111
Author(s):  
David Fassbender ◽  
Tatiana Minav
Keyword(s):  
Energy Efficiency ◽  
Solution Space ◽  
Heavy Duty ◽  
Large Solution ◽  
Evaluation Algorithm ◽  
Actuator Application ◽  
Novel Concept ◽  
Hydraulic Valve ◽  
Performance Demonstration ◽  
Modular Nature

In recent years, a variety of novel actuator concepts for the implements of heavy-duty mobile machines (HDMMs) has been proposed by industry and academia. Mostly, novel concepts aim at improving the typically low energy efficiency of state-of-the-art hydraulic valve-controlled actuators. However, besides energy-efficiency, many aspects that are crucial for a successful concept integration are often neglected in studies. Furthermore, most of the time, a specific HDMM is focused as an application while other HDMM types can show very different properties that might make a novel concept less suitable. In order to take more aspects and HDMM types into account when evaluating actuator concepts, this paper proposes a novel evaluation algorithm, which calculates so-called mismatch values for each potential actuator-application match, based on different problem aspects that can indicate a potential mismatch between a certain actuator concept and an HDMM. The lower the mismatch value, which depends on actuator characteristics as well as HDMM attributes, the more potential is the match. At the same time, the modular nature of the algorithm allows to evaluate a large number of possible matches at once, with low effort. For the performance demonstration of the algorithm, 36 potential matches formed out of six actuator concepts and six HDMM types are exemplarily evaluated. The resulting actuator concept ratings for the six different HDMMs are in line with general reasoning and confirm that the evaluation algorithm is a powerful tool to get a first, quick overview of a large solution space of actuator-HDMM matches. However, analyzing the limitations of the algorithm also shows that it cannot replace conventional requirements engineering and simulation studies if detailed and reliable results are required.

scholarly journals AN ACCURATE REAL-TIME UAV MAPPING SOLUTION FOR THE GENERATION OF ORTHOMOSAICS AND SURFACE MODELS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 165-171
Author(s):  
A. Kern ◽  
P. Fanta-Jende ◽  
P. Glira ◽  
F. Bruckmüller ◽  
C. Sulzbachner
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Disaster Response ◽  
Time Data ◽  
Surface Models ◽  
Indispensable Tool ◽  
Real Time Data Processing ◽  
Time Critical ◽  
Mapping Solution ◽  
Processing Solution

Abstract. UAVs have become an indispensable tool for a variety of mapping applications. Not only in the area of surveying, infrastructure planning and environmental monitoring tasks but also in time-critical applications, such as emergency and disaster response. Although UAVs enable rapid data acquisition per se, data processing usually relies on offline workflows. This contribution presents an accurate real-time data processing solution for UAV mapping applications as well as an extensive experimental and comparative study to the commercial offline solution Pix4D on the absolute accuracy of orthomosaics and digital surface models. We show that our procedure achieves an absolute horizontal and vertical accuracy of about 1 m without the use of ground control. The code will be made publicly available.

scholarly journals Mobile Mapping Technologies

2021 ◽  
pp. 439-465
Author(s):  
Kai Wei Chiang ◽  
Guang-Je Tsai ◽  
Jhih Cing Zeng
Keyword(s):  
Sensor Fusion ◽  
Disaster Response ◽  
Autonomous Systems ◽  
High Definition ◽  
Mobile Mapping ◽  
Potential Applications ◽  
Historic Development ◽  
Mobile Mapping Systems ◽  
Indoor And Outdoor ◽  
Mapping Systems

AbstractThis chapter introduces the historic development as well as the latest progress of mobile mapping systems. First, mobile mapping technologies, including the introduction of positioning and mapping sensors, and how they can be integrated together, are briefly reviewed. Then the development of land-based, aerial, marine, and mobile portable mapping platforms is presented. The latest progress in mobile-mapping technologies is further discussed, along with sensor fusion schemes, seamless indoor and outdoor mapping strategies, and disaster response applications. In addition, this chapter explores future and potential applications, such as high-definition (HD) maps and autonomous mapping with autonomous systems.

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