240115
Conversational Case-Based Planning for Agent Team Coordination
This paper describes a prototype in which a conversational case-based reasoner, NaCoDAE, was agentified and inserted in the RETSINA multi-agent system. Its task was to determine agent roles within a heterogeneous society of agents, where the agents may use capability-based or team-oriented agent coordination strategies. There were three reasons for assigning this task to NaCoDAE: (1) to relieve the agents of the overhead of determining, for themselves, if they should be involved in the task, or not; (2) to convert seemingly unrelated data into contextually relevant knowledge -- as a case-based reasoning system, NaCo-DAE is particularly suited for applying apparently incoherent data to a wide variety of domain-specific situations; and (3) as a conversational CBR system, to both unobtrusively listen to human statements and to proactively dialogue with other agents in a more goal-directed approach to gathering relevant information. The cases maintained by NaCoDAE have question and answer components, which were originally intended to maintain the textual representations of questions and answers for humans. By associating agent capability descriptions and queries with the case questions, NaCoDAE also assumed the team role of a capability-based coordinator. By encoding fragments of HTN plan objectives in its case actions, we were able to convert NaCoDAE into a conversational case-based planner that served compositionally-generated HTN plan objectives, already populated with situation-relevant knowledge, for use by the RETSINA team-oriented agents.
31490698
Cloning for Intelligent Adaptive Information Agents
Adaptation in open, multi-agent information gathering systems is important for several reasons. These reasons include the inability to accurately predict future problem-solving workloads, future changes in existing information requests, future failures and additions of agents and data supply resources, and other future task environment characteristic changes that require system reorganization. We are developing a multi-agent financial portfolio management system that must deal with all of these problems. This paper will briefly describe our approaches and solutions at several different levels within the agents: adaptation at the organizational, planning, scheduling, and execution levels. We discuss our solution for execution-level adaptation (“cloning”) in detail, and present empirical evidence backing up the theory behind this execution-level solution.
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.}
331230
Intelligent Adaptive Information Agents
Adaptation in open, multi-agent information gathering systems isimportant for several reasons. These reasons include the inability toaccurately predict future problem-solving workloads, future changes inexisting information requests, future failures and additions of agents anddata supply resources, and other future task environment characteristicchanges that require system reorganization. We have developed a multi-agentdistributed system infrastructure, RETSINA (REusable Task Structure-based Intelligent Network Agents) that handles adaptation in an open Internetenvironment. Adaptation occurs both at the individual agent level as well asat the overall agent organization level. The RETSINA system has three typesof agents. Interface agents interact with the userreceiving user specifications and delivering results. They acquire, model,and utilize user preferences to guide system coordination in support of theuser‘s tasks. Task agents help users perform tasks byformulating problem solving plans and carrying out these plans throughquerying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneouscollection of information sources. In this paper, we concentrate on theadaptive architecture of the information agents. We use as the domain ofapplication WARREN, a multi-agent financial portfolio management system thatwe have implemented within the RETSINA framework.
9235136
Strategies for Querying Information Agents
In a simple cooperative MAS model where a collection of “querying agents” can send queries to a collection of “information agents”, we formalize the problem of designing strategies so that the expected completion time of the queries is minimized, when every querying agent uses the same strategy. We devise a provably optimal strategy for the static case with no query arrivals, and show via simulations that the same strategy performs well when queries arrive with a certain probability. We also consider issues such as whether or not the expected completion time can be reduced by sending multiple copies of queries, or by aborting copies of answered queries.
2588941
Browsing Schedules - An Agent-Based Approach to Navigating the Semantic Web
The Semantic Web promises to change the way agents navigate, harvest and utilize information on the internet. By providing a structured, distributed representation for expressing concepts and relationships defined by multiple ontologies, it is now possible for agents to read and reason about published knowledge, without the need for scrapers, information agents, and centralized ontologies. Agents can utilize this knowledge to seek and invoke other agents and web services, thus supporting navigation across the Semantic Web. We demonstrate how agents support enhanced navigation within a conference-schedule domain, and present three agent-based services: the RETSINA Calendar Agent, which reasons about schedules marked up on the Semantic Web; the DMA2ICal Translation Agent which provides translation services between schedules grounded in different ontologies, and a Conference Agent that invokes the Calendar Agent.
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.
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.
1115801
Authorization and privacy for semantic Web services
Web services will soon handle users' private information. They'll need to provide privacy guarantees to prevent this delicate information from ending up in the wrong hands. More generally, Web services will need to reason about their users' policies that specify who can access private information and under what conditions. These requirements are even more stringent for semantic Web services that exploit the semantic Web to automate their discovery and interaction because they must autonomously decide what information to exchange and how. In our previous work, we proposed ontologies for modeling the high-level security requirements and capabilities of Web services and clients.1 This modeling helps to match a client's request with appropriate services-those based on security criteria as well as functional descriptions.