Coordination Among Autonomous Planners
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Coordination Among Autonomous Planners
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Author : Jeroen Valk
language : en
Publisher:
Release Date : 2005
Coordination Among Autonomous Planners written by Jeroen Valk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Business planning categories.
A Factored Planner For The Temporal Coordination Of Autonomous Systems
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Author : David Cheng-Ping Wang
language : en
Publisher:
Release Date : 2015
A Factored Planner For The Temporal Coordination Of Autonomous Systems written by David Cheng-Ping Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Embedded devices are being composed into ever more complex networked systems, including Earth observing systems and transportation networks. The complexity of these systems require automated coordination, but planning for and controlling these systems pose unique challenges. Devices can exhibit stateful, timed, and periodic behavior. A washing machine transitions automatically between its wash cycles while locking its door accordingly. The interaction between devices can cause indirect effects and require concurrency. A UAV with a simple GPS-based auto pilot may refuse to take off until it has a GPS fix, and may further require that fix be maintained while flying its route. While many planners exist that support some of these features, to our knowledge, no planner can support them all, and none can handle automatic timed transitions. In this thesis, we present tBurton, a domain-independent temporal planner for complex networked systems. tBurton can generate a plan that meets deadlines and maintains durative goals. Furthermore, the plan it generates is temporally least-commitment, affording some flexibility during plan execution. tBurton uses a divide and conquer approach: dividing the problem into a directed acyclic graph of factors via causal-graph decomposition and conquering each factor with heuristic forward search. Planning is guided by the DAG structure of the causal graph, and consists of a recursive element. All of the sub-goals for a particular factor are gathered before generating its plan and regressing its sub-goals to parent factors. Key to this approach is a process we call unification, which exploits the locality of information afforded by factoring to efficiently prune unachievable sub-goal orderings before the computationally expensive task of planning. The contributions of this thesis are three fold: First, we introduce a planner for networked devices that supports a set of features never before found in one planner. Second, we introduce a new approach to factored planning based on timeline-based regression and heuristic forward search. Third, we demonstrate the effectiveness of this approach on both existing planning benchmarks, as well as a set of newly developed benchmarks that model networked devices.
Coordinating Plans Of Autonomous Agents
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Author : Frank von Martial
language : en
Publisher:
Release Date : 1992
Coordinating Plans Of Autonomous Agents written by Frank von Martial and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Distributed artificial intelligence categories.
Coordinating Plans Of Autonomous Agents
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Author : Frank v. Martial
language : en
Publisher: Lecture Notes in Artificial Intelligence
Release Date : 1992-06-24
Coordinating Plans Of Autonomous Agents written by Frank v. Martial and has been published by Lecture Notes in Artificial Intelligence this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992-06-24 with Business & Economics categories.
This book deals with an important topic in distributed AI: the coordination of autonomous agents' activities. It provides a framework for modelling agents with planning and communicative competence. Important issues in the book are: - How to recognize and reconcile conflicting intentions among a collection of agents. - How to recognize and take advantage of favorable interactions. - How to enable individual agents to represent and reason about the actions, plans, and knowledge of other agents in order to coordinate with them. - When to call a set of plans coordinated and what operations are possible to transform uncoordinated plans into coordinated ones. - How to enable agents to communicate and interact: what communication languages or protocols to use, and what and when to communicate. The book is clearly written with many examples and background material.
Using Continuous Planning Techniques To Achieve Autonomy And Coordination Among Multiple Unmanned Aerial Vehicles
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Author :
language : en
Publisher:
Release Date : 2001
Using Continuous Planning Techniques To Achieve Autonomy And Coordination Among Multiple Unmanned Aerial Vehicles written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Existing Unmanned Aerial Vehicle (UAV) systems exhibit shortcomings in providing continuous, responsive, timely, and detailed information and targeting support to Army tactical commander's combat operations in an Army XXI battlespace. To synchronize Tactical UAV (TUAV) missions with supported operations, time is the critical element. Anything that can reduce TUAV planning time, while maintaining plan effectiveness, will expedite execution of a TUAV1s mission. Autonomous flight with some ability to avoid and evade certain threats would increase survivability further. This paper begins by presenting some of the recent successes achieved by artificial intelligence (Al) planners and schedulers on complex real-world problems. It then attempts to show how NASA's demonstrated utility of a dynamic Al planning system prototype for conducting autonomous distributed planning and execution for a team of rovers engaged in missions to achieve science goals during planetary operations can be generalized and applied to a team of TUAVs. Last, it discusses some data collection opportunities that should appear due to the ability to place increasingly more processing and data storage capabilities onboard TUAVs and some of the key challenges to use those capabilities to produce more timely and immediately usable interpretations.
Modularity And Coordination For Planning And Reinforcement Learning
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Author : Jayesh Kumar Gupta
language : en
Publisher:
Release Date : 2020
Modularity And Coordination For Planning And Reinforcement Learning written by Jayesh Kumar Gupta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The foundational objective of the field of artificial intelligence is to build autonomous systems that can perceive their environment and take actions that maximize their ability to achieve their goals. Decision making under uncertainty is a fundamental requirement for such intelligent behavior. Various real world problems of interest like autonomous driving, virtual assistants, and disaster response are sequential decision making problems. Planning and reinforcement learning are abstractions for studying optimal sequential decision making in natural and artificial systems. Combining these ideas with deep neural network function approximation (*"deep reinforcement learning"*) has allowed scaling these abstractions to a variety of complex problems and has led to super-human performance, especially in game playing. These successes are still limited to virtual worlds with fast simulators where massive amounts of training data can be generated given enough computational resources. However, decision making in the real world requires solutions that are data efficient, capable of utilizing domain knowledge when available, and generalize to related problems. Moreover, often decision making requires decentralized execution for scalability. The concept of modularity has proven effective in a large number of fields to deal with complex systems. The key ideas driving a modular system are 1) information encapsulation and 2) coordination for integrated function. Modularity allows breaking down a complex problem into manageable units. This dissertation explores how, as designers of complex decision making systems, the principles of modular design can allow us to provide structural inductive biases and define appropriate coordination mechanisms. In the first part, we explore the concept of functional modularity in the form of agents, and how they can inform the design of large multi-agent decision making systems. In the second part, we explore the concept of temporal modularity in the form of subtasks in complicated tasks and how we can learn decomposed solutions that show improved transfer performance to related tasks. Finally, in the last part, we explore the concept of architectural modularity; how known physics can inform our neural network models of mechanical systems allowing reliable planning and efficient reinforcement learning. We find that these design principles lead to enormous data efficiency improvements and lower costs for learning and inference. Moreover, we find solutions that generalize better to related problems.
Understanding Autonomous Cooperation And Control In Logistics
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Author : Michael Hülsmann
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-06-30
Understanding Autonomous Cooperation And Control In Logistics written by Michael Hülsmann and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-06-30 with Technology & Engineering categories.
One of the great challenges in flexible production and supply chains is the availability of necessary information at any time and place. Autonomous logistics processes can bring about fast and flexible adaptations to change. This book identifies autonomous logistics processes and details how they differ from conventionally managed processes. Coverage also describes the changes that autonomy will cause in order processing.
Proceedings Of 2022 International Conference On Autonomous Unmanned Systems Icaus 2022
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Author : Wenxing Fu
language : en
Publisher: Springer Nature
Release Date : 2023-03-10
Proceedings Of 2022 International Conference On Autonomous Unmanned Systems Icaus 2022 written by Wenxing Fu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-10 with Technology & Engineering categories.
This book includes original, peer-reviewed research papers from the ICAUS 2022, which offers a unique and interesting platform for scientists, engineers and practitioners throughout the world to present and share their most recent research and innovative ideas. The aim of the ICAUS 2022 is to stimulate researchers active in the areas pertinent to intelligent unmanned systems. The topics covered include but are not limited to Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and Its Application in Unmanned Systems. The papers showcased here share the latest findings on Unmanned Systems, Robotics, Automation, Intelligent Systems, Control Systems, Integrated Networks, Modeling and Simulation. It makes the book a valuable asset for researchers, engineers, and university students alike.
Robust Distributed Planning Strategies For Autonomous Multi Agent Teams
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Author : Sameera S. Ponda
language : en
Publisher:
Release Date : 2012
Robust Distributed Planning Strategies For Autonomous Multi Agent Teams written by Sameera S. Ponda and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
The increased use of autonomous robotic agents, such as unmanned aerial vehicles (UAVs) and ground rovers, for complex missions has motivated the development of autonomous task allocation and planning methods that ensure spatial and temporal coordination for teams of cooperating agents. The basic problem can be formulated as a combinatorial optimization (mixed-integer program) involving nonlinear and time-varying system dynamics. For most problems of interest, optimal solution methods are computationally intractable (NP-Hard), and centralized planning approaches, which usually require high bandwidth connections with a ground station (e.g. to transmit received sensor data, and to dispense agent plans), are resource intensive and react slowly to local changes in dynamic environments. Distributed approximate algorithms, where agents plan individually and coordinate with each other locally through consensus protocols, can alleviate many of these issues and have been successfully used to develop real-time conflict-free solutions for heterogeneous networked teams. An important issue associated with autonomous planning is that many of the algorithms rely on underlying system models and parameters which are often subject to uncertainty. This uncertainty can result from many sources including: inaccurate modeling due to simplifications, assumptions, and/or parameter errors; fundamentally nondeterministic processes (e.g. sensor readings, stochastic dynamics); and dynamic local information changes. As discrepancies between the planner models and the actual system dynamics increase, mission performance typically degrades. The impact of these discrepancies on the overall quality of the plan is usually hard to quantify in advance due to nonlinear effects, coupling between tasks and agents, and interdependencies between system constraints. However, if uncertainty models of planning parameters are available, they can be leveraged to create robust plans that explicitly hedge against the inherent uncertainty given allowable risk thresholds. This thesis presents real-time robust distributed planning strategies that can be used to plan for multi-agent networked teams operating in stochastic and dynamic environments. One class of distributed combinatorial planning algorithms involves using auction algorithms augmented with consensus protocols to allocate tasks amongst a team of agents while resolving conflicting assignments locally between the agents. A particular algorithm in this class is the Consensus-Based Bundle Algorithm (CBBA), a distributed auction protocol that guarantees conflict-free solutions despite inconsistencies in situational awareness across the team. CBBA runs in polynomial time, demonstrating good scalability with increasing numbers of agents and tasks. This thesis builds upon the CBBA framework to address many realistic considerations associated with planning for networked teams, including time-critical mission constraints, limited communication between agents, and stochastic operating environments. A particular focus of this work is a robust extension to CBBA that handles distributed planning in stochastic environments given probabilistic parameter models and different stochastic metrics. The Robust CBBA algorithm proposed in this thesis provides a distributed real-time framework which can leverage different stochastic metrics to hedge against parameter uncertainty. In mission scenarios where low probability of failure is required, a chance-constrained stochastic metric can be used to provide probabilistic guarantees on achievable mission performance given allowable risk thresholds. This thesis proposes a distributed chance-constrained approximation that can be used within the Robust CBBA framework, and derives constraints on individual risk allocations to guarantee equivalence between the centralized chance-constrained optimization and the distributed approximation. Different risk allocation strategies for homogeneous and heterogeneous teams are proposed that approximate the agent and mission score distributions a priori, and results are provided showing improved performance in time-critical mission scenarios given allowable risk thresholds.
Towards Autonomous Robotic Systems
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Author : Charles Fox
language : en
Publisher: Springer Nature
Release Date : 2021-10-30
Towards Autonomous Robotic Systems written by Charles Fox and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-30 with Computers categories.
The volume LNAI 13054 constitutes the refereed proceedings of the 22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021, held in Lincoln, UK, in September 2021.*The 45 full papers were carefully reviewed and selected from 66 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: artificial intelligence; mechatronics; image processing and computer vision; special purpose and application-based systems; user interfaces and human computer interaction. * The conference was held virtually due to the COVID-19 pandemic.
