6,371 research outputs found
Robust Agent Teams via Socially-Attentive Monitoring
Agents in dynamic multi-agent environments must monitor their peers to
execute individual and group plans. A key open question is how much monitoring
of other agents' states is required to be effective: The Monitoring Selectivity
Problem. We investigate this question in the context of detecting failures in
teams of cooperating agents, via Socially-Attentive Monitoring, which focuses
on monitoring for failures in the social relationships between the agents. We
empirically and analytically explore a family of socially-attentive teamwork
monitoring algorithms in two dynamic, complex, multi-agent domains, under
varying conditions of task distribution and uncertainty. We show that a
centralized scheme using a complex algorithm trades correctness for
completeness and requires monitoring all teammates. In contrast, a simple
distributed teamwork monitoring algorithm results in correct and complete
detection of teamwork failures, despite relying on limited, uncertain
knowledge, and monitoring only key agents in a team. In addition, we report on
the design of a socially-attentive monitoring system and demonstrate its
generality in monitoring several coordination relationships, diagnosing
detected failures, and both on-line and off-line applications
Improving Trajectory Prediction in Dynamic Multi-Agent Environment by Dropping Waypoints
The inherently diverse and uncertain nature of trajectories presents a
formidable challenge in accurately modeling them. Motion prediction systems
must effectively learn spatial and temporal information from the past to
forecast the future trajectories of the agent. Many existing methods learn
temporal motion via separate components within stacked models to capture
temporal features. Furthermore, prediction methods often operate under the
assumption that observed trajectory waypoint sequences are complete,
disregarding scenarios where missing values may occur, which can influence
their performance. Moreover, these models may be biased toward particular
waypoint sequences when making predictions. We propose a novel approach called
Temporal Waypoint Dropping (TWD) that explicitly incorporates temporal
dependencies during the training of a trajectory prediction model. By
stochastically dropping waypoints from past observed trajectories, the model is
forced to learn the underlying temporal representation from the remaining
waypoints, resulting in an improved model. Incorporating stochastic temporal
waypoint dropping into the model learning process significantly enhances its
performance in scenarios with missing values. Experimental results demonstrate
our approach's substantial improvement in trajectory prediction capabilities.
Our approach can complement existing trajectory prediction methods to improve
their prediction accuracy. We evaluate our proposed approach on three datasets:
NBA Sports VU, ETH-UCY, and TrajNet++.Comment: Under Revie
Internet collaboration and service composition as a loose form of teamwork
This paper describes Web service composition as a form of teamwork, where the Web services are team members in a loose collaboration. We argue that newer hierarchical teamwork models are more appropriate for Web service composition than the traditional models involving joint beliefs and joint intentions. We describe our system for developing and executing Web service compositions as team plans in JACK Teams,((TM) 1) and discuss the relationships between this approach and service orchestration languages such as Business Process Execution Language for Web Services (BPEL4WS). We discuss briefly how the use of Al planning can also be incorporated into this model, and identify some of the research issues involved. Incorporating Web service compositions into a mature Belief Desire Intention (BDI) agent team framework allows for integration of Web services seamlessly into a powerful application execution paradigm that supports sophisticated reasoning
Predicting opponent actions in the RoboSoccer
Proceeding of: IEEE International Conference on Systems, Man, and Cybernetics (SMC-2002), 6-9 Oct. 2002, Hammamet, TunezA very important issue in multi-agent systems is that of adaptability to other agents, be it to cooperate or to compete. In competitive domains, the knowledge about the opponent can give any player a clear advantage. In previous work, we acquired models of another agent (the opponent) based only on the observation of its inputs and outputs (its behavior) by formulating the problem as a classification task. In this paper we extend this previous work to the RoboCup domain. However, we have found that models based on a single classifier have bad accuracy, To solve this problem, In this paper we propose to decompose the learning task into two tasks: learning the action name (i.e. kick or dash) and learning the parameter of that action. By using this hierarchical learning approach accuracy results improve, and at worst, the agent can know what action the opponent will carry out, even if there is no high accuracy on the action parameter.Publicad
Four Essays in Microeconomics: Social Norms and Social Preferences
The four essays deal with social motivators for human behavior in economics, namely social norms and social preferences. The first three essays present and analyze a particular social preference model, socially attentive preferences. The fourth essay gives a review of the theoretical economic literature on social norms
Ethical and practical considerations for interventional HIV cure-related research at the end-of-life: A qualitative study with key stakeholders in the United States
Background A unique window of opportunity currently exists to generate ethical and practical considerations presented by interventional HIV cure-related research at the end-of-life (EOL). Because participants would enroll in these studies for almost completely altruistic reasons, they are owed the highest ethical standards, safeguards, and protections. This qualitative empirical ethics study sought to identify ethical and practical considerations for interventional HIV cure-related research at the EOL. Methods and findings We conducted 20 in-depth interviews and three virtual focus groups (N = 36) with four key stakeholder groups in the United States: 1) bioethicists, 2) people with HIV, 3) HIV care providers, and 4) HIV cure researchers. This study produced six key themes to guide the ethical implementation of interventional HIV cure-related research at the EOL: 1) all stakeholder groups supported this research conditioned upon a clearly delineated respect for participant contribution and autonomy, participant understanding and comprehension of the risks associated with the specific intervention(s) to be tested, and broad community support for testing of the proposed intervention(s); 2) to ensure acceptable benefit-risk profiles, researchers should focus on limiting the risks of unintended effects and minimizing undue pain and suffering at the EOL; 3) only well-vetted interventions that are supported by solid pre-clinical data should be tested in the EOL translational research model; 4) the informed consent process must be robust and include process consent; 5) research protocols should be flexible and adopt a patient/participant centered approach to minimize burdens and ensure their overall comfort and safety; and 6) a participant’s next-of-kin/loved ones should be a major focus of EOL research but only if the participant consents to such involvement
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Robot Autonomy for Surgery
Autonomous surgery involves having surgical tasks performed by a robot
operating under its own will, with partial or no human involvement. There are
several important advantages of automation in surgery, which include increasing
precision of care due to sub-millimeter robot control, real-time utilization of
biosignals for interventional care, improvements to surgical efficiency and
execution, and computer-aided guidance under various medical imaging and
sensing modalities. While these methods may displace some tasks of surgical
teams and individual surgeons, they also present new capabilities in
interventions that are too difficult or go beyond the skills of a human. In
this chapter, we provide an overview of robot autonomy in commercial use and in
research, and present some of the challenges faced in developing autonomous
surgical robots
- …