2746951
Cost-Sensitive Access Control for Illegitimate Confidential Access by Insiders
In many organizations, it is common to control access to confidential information based on the need-to-know principle; The requests for access are authorized only if the content of the requested information is relevant to the requester’s current information analysis project. We formulate such content-based authorization, i.e. whether to accept or reject access requests as a binary classification problem. In contrast to the conventional error-minimizing classification, we handle this problem in a cost-sensitive learning framework in which the cost caused by incorrect decision is different according to the relative importance of the requested information. In particular, the cost (i.e., damaging effect) for a false positive (i.e., accepting an illegitimate request) is more expensive than that of false negative (i.e., rejecting a valid request). The former is a serious security problem because confidential information, which should not be revealed, can be accessed. From the comparison of the cost-sensitive classifiers with error-minimizing classifiers, we found that the costing with a logistic regression showed the best performance, in terms of the smallest cost paid, the lowest false positive rate, and the relatively low false negative rate.
11307805
A constraint optimization framework for fractured robot teams
In dangerous and uncertain environments initial plans must be revised. Communication failures hamper this replanning. We introduce fractured subteams as a novel formalism for modeling breakdowns in communication. We present a hybrid approach that employs distributed coordination mechanisms to provide robustness to these communication breakdowns and exploits opportunistic centralization. By modeling the problem as a mixed integer linear programming problem, we are able to apply constraint optimization techniques to efficiently find optimal or near optimal solutions to the difficult class of time critical tight coordination team planning problems. We then demonstrate that explicitly reasoning about communication failures through the incorporation of selective disruption minimization can improve team performance.
2246359
Simultaneous Team Assignment and Behavior Recognition from Spatio-Temporal Agent Traces
This paper addresses the problem of activity recognition for physically-embodied agent teams. We define team activity recognition as the process of identifying team behaviors from traces of agent positions over time; for many physical domains, military or athletic, coordinated team behaviors create distinctive spatio-temporal patterns that can be used to identify low-level action sequences. This paper focuses on the novel problem of recovering agent-to-team assignments for complex team tasks where team composition, the mapping of agents into teams, changes over time. Without a priori knowledge of current team assignments, the behavior recognition problem is challenging since behaviors are characterized by the aggregate motion of the entire team and cannot generally be determined by observing the movements of a single agent in isolation. To handle this problem, we introduce a new algorithm, Simultaneous Team Assignment and Behavior Recognition (STABR), that generates behavior annotations from spatio-temporal agent traces. The proposed approach is able to perform accurate team behavior recognition without an exhaustive search over the combinatorial space of potential team assignments, as demonstrated on several scenarios of simulated military maneuvers.
6706379
Modeling teams of specialists
Often, complex decisions must be made by a group of specialists rather than a single decision maker. To make an effective decision, the combination of the group's expertise must be brought to bear on the situation. Fusing expertise where individuals have very detailed knowledge in their own areas and much weaker understanding of others is characterized by difficulties: agents cannot communicate their expertise in an intelligible way to non-experts because of differences in vocabulary and conceptual content; the process allows for incorrect inferences; and no one knows what anyone else needs to know. Measures and models developed for single agent decision making do not address the complications posed by the fusion of heterogeneous expertise nor the resource limitations which must be considered in evaluating group decisions. We propose the Team of Specialists (TOS) group decision model which partitions agents' knowledge into expert and naive models to address these inadequacies. TOS models group decision making as a process of model refinement linking communications among agents to modifications of the naive portions of their models. The TOS model gives both a normative reference for evaluating group decisions and a characterization of the process by which they evolve. The model of process and criterion for ordering outcomes provided by TOS are the essential prerequisites for devising decision aids.<<ETX>>
2904474
Heterogeneity, stability, and efficiency in distributed systems
This paper explores the increasing the heterogeneity of an agent population to stabilize decentralized systems by adding bias terms to each agent's expected payoffs. Two approaches are evaluated, corresponding to heterogeneous preferences and heterogeneous transaction costs; empirically, the transaction cost case provides stability with near optimal payoffs under certain conditions. Theoretically, in the idealized case of an infinite number of agents, it is proven that the system with added heterogeneous preferences has a fired point different from that of the unbiased system, guaranteeing suboptimal perfomance, while the transaction cast case is demonstrated to have a fixed point identical to that of the unbiased system, and it is further shown to be a contraction mapping, guaranteeing convergence. This contraction mapping allows us to conceptualize the model with heterogeneous transaction costs as a decentralized root finding system.
8965477
Configuration management for multi-agent systems
As heterogeneous distributed systems, multi-agent systems present some challenging configuration management issues. There are the problems of knowing how to allocate agents to computers, launch them on remote hosts, and once the agents have been launched, how to monitor their runtime status so as to manage computing resources effectively.
In this paper, we present the RETSINA Configuration Manager, \emph{RECoMa}. We describe its architecture, how it uses agent infrastructure such as service discovery, to assist the multi-agent system administrator in allocating, launching, and monitoring a heterogeneous distributed agent system in a distributed and networked computing environment.\footnote{The authors would like to acknowledge the significant contributions of Matthew W. Easterday in his earlier and exhaustive implementations of configuration management programs and CM design proposals. This research has been sponsored in part by DARPA Grant F-30602-98-2-0138 and the Office of Naval Research Grant N-00014-96-16-1-1222.}