12136431
Training Teams with Collaborative Agents
Training teams is an activity that is expensive, time-consuming, hazardous in some cases, and can be limited by availability of equipment and personnel. In team training, the focus is on optimizing interactions, such as efficiency of communication, conflict resolution and prioritization, group situation awareness, resource distribution and load balancing, etc. This paper presents an agent-based approach to designing intelligent team training systems. We envision a computer-based training system in which teams are trained by putting them through scenarios, which allow them to practice their team skills. There are two important roles that intelligent agents can play; these are as virtual team members and as coach. To carry out these functions, these agents must be equipped with an understanding of the task domain, the team structure, the selected decision-making process and their belief about other team members' mental states.
2078197
Collaborative Agents for C2 Teamwork Simulation
Existing team training software often requires that trainees be organized as physical teams and the members of the same team be trained at the same time. To demonstrate that team training software can be made more flexible, this paper presents an approach to incorporating software agents into the distributed command-and-control (C2) simulation software DDD, which supports only human players. Based on the multi-agent architecture CAST, the software agents are designed as a team to perform C2 tasks in the DDD. By associating the agents with the same basic capabilities as the DDD provides its human users, we illustrate how to specify the teamwork knowledge for the agent team, and how to make agents efficiently reason about the dynamic, partially observable environment. Different methods of communication and coordination among agents are also briefly described.
6192829
Clutter invariant ATR
One of the central problems in automated target recognition is to accommodate the infinite variety of clutter in real military environments. The principle focus of our paper is on the construction of metric spaces where the metric measures the distance between objects of interest invariant to the infinite variety of clutter. Such metrics are formulated using second-order random field models. Our results indicate that this approach significantly improves detection/classification rates of targets in clutter.
13019795
Large deformation diffeomorphic metric mapping of fiber orientations
This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the large deformation diffeomorphic metric-mapping framework to this type of dataset, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two images. Two different distance function of vector fields are considered. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart and brain images
32918943
Bounds on the number of isolates in sum graph labeling
A simple undirected graph H is called a sum graph if there is a labeling L of the vertices of H into distinct positive integers such that any two vertices u and v of H are adjacent if and only if there is a vertex w with label L(w )= L(u )+ L(v). The sum number � (G) of a graph G =( V; E) is the least integer r such that the graph H consisting of G and r isolated vertices is a sum graph. It is clear that � (G)6|E|. In this paper, we discuss general upper and lower bounds on the sum number. In particular, we prove that, over all graphs G =( V; E) with 5xed |V |?3 and |E|, the average of � (G) is at least |E |− 3|V |(log|V |)=[log(( |V | 2 )=|E|)] −| V |− 1. In other words, for most graphs, � (G) ∈ (|E|). c � 2001 Elsevier Science B.V. All rights reserved.
41548682
Antimagic Labelings of Generalized Petersen Graphs That Are Plane
1363737
Power save mechanisms for multi-hop wireless networks
In this paper, we discuss power save mechanisms that allow hosts to go to sleep to conserve energy. When sleeping hosts need to receive packets from other hosts, these hosts must somehow wake up the sleeping hosts. The paper discusses a busy-tone mechanism for this purpose, and an approach to improve the mechanism by using multiple busy tones. We generalize this protocol to develop the notion of multi-level power save mechanisms, and present some examples to illustrate this notion.