306 research outputs found
Adaptive Agent Architecture for Real-time Human-Agent Teaming
Teamwork is a set of interrelated reasoning, actions and behaviors of team
members that facilitate common objectives. Teamwork theory and experiments have
resulted in a set of states and processes for team effectiveness in both
human-human and agent-agent teams. However, human-agent teaming is less well
studied because it is so new and involves asymmetry in policy and intent not
present in human teams. To optimize team performance in human-agent teaming, it
is critical that agents infer human intent and adapt their polices for smooth
coordination. Most literature in human-agent teaming builds agents referencing
a learned human model. Though these agents are guaranteed to perform well with
the learned model, they lay heavy assumptions on human policy such as
optimality and consistency, which is unlikely in many real-world scenarios. In
this paper, we propose a novel adaptive agent architecture in human-model-free
setting on a two-player cooperative game, namely Team Space Fortress (TSF).
Previous human-human team research have shown complementary policies in TSF
game and diversity in human players' skill, which encourages us to relax the
assumptions on human policy. Therefore, we discard learning human models from
human data, and instead use an adaptation strategy on a pre-trained library of
exemplar policies composed of RL algorithms or rule-based methods with minimal
assumptions of human behavior. The adaptation strategy relies on a novel
similarity metric to infer human policy and then selects the most complementary
policy in our library to maximize the team performance. The adaptive agent
architecture can be deployed in real-time and generalize to any off-the-shelf
static agents. We conducted human-agent experiments to evaluate the proposed
adaptive agent framework, and demonstrated the suboptimality, diversity, and
adaptability of human policies in human-agent teams.Comment: The first three authors contributed equally. In AAAI 2021 Workshop on
Plan, Activity, and Intent Recognitio
Explainable Action Advising for Multi-Agent Reinforcement Learning
Action advising is a knowledge transfer technique for reinforcement learning
based on the teacher-student paradigm. An expert teacher provides advice to a
student during training in order to improve the student's sample efficiency and
policy performance. Such advice is commonly given in the form of state-action
pairs. However, it makes it difficult for the student to reason with and apply
to novel states. We introduce Explainable Action Advising, in which the teacher
provides action advice as well as associated explanations indicating why the
action was chosen. This allows the student to self-reflect on what it has
learned, enabling advice generalization and leading to improved sample
efficiency and learning performance - even in environments where the teacher is
sub-optimal. We empirically show that our framework is effective in both
single-agent and multi-agent scenarios, yielding improved policy returns and
convergence rates when compared to state-of-the-art methodsComment: This work has been accepted to ICRA 202
Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey
Abstract
There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.</jats:p
Multiagent Learning Through Indirect Encoding
Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other. In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent’s location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. iii The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(λ) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding
Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions
Explainable Artificial Intelligence (XAI) is transforming the field of
Artificial Intelligence (AI) by enhancing the trust of end-users in machines.
As the number of connected devices keeps on growing, the Internet of Things
(IoT) market needs to be trustworthy for the end-users. However, existing
literature still lacks a systematic and comprehensive survey work on the use of
XAI for IoT. To bridge this lacking, in this paper, we address the XAI
frameworks with a focus on their characteristics and support for IoT. We
illustrate the widely-used XAI services for IoT applications, such as security
enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and
Internet of City Things (IoCT). We also suggest the implementation choice of
XAI models over IoT systems in these applications with appropriate examples and
summarize the key inferences for future works. Moreover, we present the
cutting-edge development in edge XAI structures and the support of
sixth-generation (6G) communication services for IoT applications, along with
key inferences. In a nutshell, this paper constitutes the first holistic
compilation on the development of XAI-based frameworks tailored for the demands
of future IoT use cases.Comment: 29 pages, 7 figures, 2 tables. IEEE Open Journal of the
Communications Society (2022
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
Foundations of Trusted Autonomy
Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie
Human-Robot Collaborations in Industrial Automation
Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations
Collaborative autonomy in heterogeneous multi-robot systems
As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition.
This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems.
Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots
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