scholarly journals Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach

2002 ◽  
Vol 17 ◽  
pp. 83-135 ◽  
Author(s):  
G. A. Kaminka ◽  
D. V. Pynadath ◽  
M. Tambe
Keyword(s):  
Monitoring System ◽  
Scale Up ◽  
Empirical Evaluation ◽  
Recognition Algorithm ◽  
Plan Recognition ◽  
Task Execution ◽  
Team Members ◽  
Human Expert ◽  
Dynamic Task ◽  
Cooperating Agents

Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by 'overhearing', where the monitored team's state is inferred (via plan-recognition) from team-members' *routine* communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities to be represented in a single coherent entity. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task.

Design of Gait Recognition System Based on FPGA and DSP

2014 ◽  
Vol 687-691 ◽  
pp. 3861-3868
Author(s):  
Zheng Hong Deng ◽  
Li Tao Jiao ◽  
Li Yan Liu ◽  
Shan Shan Zhao
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Zernike Moment ◽  
Intelligent Monitoring ◽  
The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

scholarly journals Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search

AI Magazine ◽  
2015 ◽  
Vol 36 (2) ◽  
pp. 10-21 ◽  
Author(s):  
Oriel Uzan ◽  
Reuth Dekel ◽  
Or Seri ◽  
Ya’akov (Kobi) Gal
Keyword(s):  
Learning Environments ◽  
Educational Software ◽  
State Of The Art ◽  
Recognition Algorithm ◽  
Plan Recognition ◽  
Exploratory Learning ◽  
Domain Expert ◽  
Teaching Chemistry ◽  
Exploratory Learning Environments ◽  
New Algorithms

This article presents new algorithms for inferring users’ activities in a class of flexible and open-ended educational software called exploratory learning environments (ELE). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students’ progress and to assess their performance. This article presents techniques for recognizing students activities in ELEs and visualizing these activities to students. It describes a new plan recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries. It was able to outperform the state-of-the-art plan recognition algorithms when compared to a gold-standard that was obtained by a domain-expert. We also show that visualizing students’ plans improves their performance on new problems when compared to an alternative visualization that consists of a step-by-step list of actions.

scholarly journals A multi-responsibility–oriented coalition formation framework for dynamic task allocation in mobile–distributed multi-agent systems

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881303 ◽  
Author(s):  
Bing Xie ◽  
Xueqiang Gu ◽  
Jing Chen ◽  
LinCheng Shen
Keyword(s):  
Coalition Formation ◽  
Task Allocation ◽  
Game Model ◽  
Multi Agent Systems ◽  
Coalition Game ◽  
Task Execution ◽  
Agent Systems ◽  
Dynamic Task ◽  
Dynamic Task Allocation ◽  
Multi Agent

In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).

ARM-based Behavior Tracking and Identification System for Grouphoused Pigs

2019 ◽  
Vol 12 (6) ◽  
pp. 554-565
Author(s):  
Xingqiao Liu ◽  
Jun Xuan ◽  
Fida Hussain ◽  
Chen Chong ◽  
Pengyu Li
Keyword(s):  
Monitoring System ◽  
Complete Information ◽  
Recognition Algorithm ◽  
Detection Methods ◽  
Identification System ◽  
Monitoring Method ◽  
Embedded Platform ◽  
Food And Water Intake ◽  
Behavior Tracking

Background: A smart monitoring system is essential to improve the quality of pig farming. A real-time monitoring system provides growth, health and food information of pigs while the manual monitoring method is inefficient and produces stress on pigs, and the direct contact between human and pig body increases diseases. Methods: In this paper, an ARM-based embedded platform and image recognition algorithms are proposed to monitor the abnormality of pigs. The proposed approach provides complete information on in-house pigs throughout the day such as eating, drinking, and excretion behaviors. The system records in detail each pig's time to eat and drink, and the amount of food and water intake. Results: The experimental results show that the accuracy of the proposed method is about 85%, and the effect of the technique has a significant advantage over traditional behavior detection methods. Conclusion: Therefore, the ARM-based behavior recognition algorithm has certain reference significance for the fine group aquaculture industry. The proposed approach can be used for a central monitoring system.

scholarly journals A probabilistic plan recognition algorithm based on plan tree grammars

2009 ◽  
Vol 173 (11) ◽  
pp. 1101-1132 ◽  
Author(s):  
Christopher W. Geib ◽  
Robert P. Goldman
Keyword(s):  
Recognition Algorithm ◽  
Plan Recognition

scholarly journals Should We Scale-Up? A Mixed Methods Process Evaluation of an Intervention Targeting Sedentary Office Workers Using the RE-AIM QuEST Framework

2019 ◽  
Vol 17 (1) ◽  
pp. 239
Author(s):  
Bradley MacDonald ◽  
Ann-Marie Gibson ◽  
Xanne Janssen ◽  
Jasmin Hutchinson ◽  
Samuel Headley ◽  
...  
Keyword(s):  
Focus Groups ◽  
Sedentary Behaviour ◽  
Workplace Health Promotion ◽  
Scale Up ◽  
Office Workers ◽  
Sitting Time ◽  
Management Support ◽  
Intervention Participant ◽  
Cultural Changes ◽  
Team Members

Background: Interventions targeting a reduction in sedentary behaviour in office workers need to be scaled-up to have impact. In this study, the RE-AIM QuEST framework was used to evaluate the potential for further implementation and scale-up of a consultation based workplace intervention which targeted both the reduction, and breaking up of sitting time. Methods: To evaluate the Springfield College sedentary behaviour intervention across multiple RE-AIM QuEST indicators; intervention participant, non-participant (employees who did not participate) and key informant (consultation delivery team; members of the research team and stakeholders in workplace health promotion) data were collected using interviews, focus groups and questionnaires. Questionnaires were summarized using descriptive statistics and interviews and focus groups were transcribed verbatim, and thematically analysed. Results: Barriers to scale-up were: participant burden of activity monitoring; lack of management support; influence of policy; flexibility (scheduling/locations); time and cost. Facilitators to scale up were: visible leadership; social and cultural changes in the workplace; high acceptability; existing health and wellbeing programmes; culture and philosophy of the participating college. Conclusions: There is potential for scale-up, however adaptations will need to be made to address the barriers to scale-up. Future interventions in office workers should evaluate for scalability during the pilot phases of research.

Team Situation Awareness in Human-Autonomy Teaming: A Systems Level Approach

2018 ◽  
Vol 62 (1) ◽  
pp. 149-149 ◽  
Author(s):  
David A. Grimm ◽  
Mustafa Demir ◽  
Jamie C. Gorman ◽  
Nancy J. Cooke
Keyword(s):  
Team Performance ◽  
Situation Awareness ◽  
Information Entropy ◽  
Timely Manner ◽  
Team Members ◽  
Dynamic Task ◽  
Remotely Piloted Aircraft ◽  
Human Autonomy ◽  
Team Situation Awareness ◽  
Communication Control

Project overview. The current study focuses on analyzing team flexibility by measuring entropy (where higher values correspond to system reorganization and lower values correspond to more stable system organization) across all-human teams and Human-Autonomy Teams (HAT). We analyzed teams in the context of a fully-fledged synthetic agent that acts as a pilot for a three-agent Remotely Piloted Aircraft System (RPAS) ground crew. The synthetic agent must be able to communicate and coordinate with human teammates in a constructive and timely manner to be effective. This study involved three heterogeneous team members who had to take photographs of target waypoints and communicate via a text-based communication system. The three team members’ roles were: 1) navigator provides information about flight plan with speed and altitude restrictions at each waypoint; 2) pilot adjusts altitude and airspeed to control the Remotely Piloted Aircraft (RPA), and negotiates with the photographer about the current altitude and airspeed to take good photos for the targets; and 3) photographer screens camera settings, and sends feedback to other team members regarding the target photograph status. The three conditions differed based on the manipulation of the pilot role: 1) Synthetic – the pilot was the synthetic agent, 2) Control – the pilot was a randomly assigned participant, and 3) Experimenter – the pilot was a well-trained experimenter who focused on sending and receiving information in a timely manner. The goal of this study is to examine how overall RPAS flexibility across HATs and all-human teams are associated with Team Situation Awareness (TSA). Method. There were 30 teams (10-teams per condition): control teams consisted of three participants randomly assigned to each role; synthetic and experimenter teams included two participants randomly assigned to the navigator and photographer roles. The experiment took place over five 40-minute missions, and the goal was to take as many “good” photos of ground targets as possible while avoiding alarms and rule violations. We obtained several measures, including mission and target level team performance scores, team process measures (situation awareness, process ratings, communication and coordination), and other measures (teamwork knowledge, workload, and demographics). We first estimated amount of system reorganization of the RPAS via an information entropy measure, i.e., the number of arrangements the system occupied over a given period of time (Shannon & Weaver, 1975). Based on information entropy, we defined four layers to represent the RPAS (Gorman, Demir, Cooke, & Grimm, In Review): 1) communications - the chat-based communication among team members; 2) vehicle - the RPA itself, e.g., speed, altitude; 3) control - interface between the RPA and the user; and system - the overall activity of the sub-layers. Then, we looked at the relationship between layers and TSA, which was based on successfully overcoming and completing ad hoc embedded target waypoints. Results and conclusion. Overall, the experimenter teams adapted to more roadblocks than the synthetic teams, who were equivalent to control teams (Demir, McNeese, & Cooke, 2016). The findings indicate that: 1) synthetic teams demonstrated rigid systems level activity, which consisted of less reorganization of communication, control and vehicle layers as conditions changed, which also resulted in less adaptation to roadblocks; 2) control teams demonstrated less communication reorganization, but more control and vehicle reorganization, which also resulted in less adaptation to roadblocks; and 3) experimenter teams demonstrated more reorganization across communication, control and vehicle layers, which resulted in better adaptation to roadblocks. Thus, the ability of a system to reorganize across human and technical layers as situations change is needed to adapt to novel conditions of team performance in a dynamic task

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