12890989
Distributed Intelligent Agents
In Retsina, the authors have developed a distributed collection of software agents that cooperate asynchronously to perform goal-directed information retrieval and integration for supporting a variety of decision-making tasks. Examples for everyday organizational decision making and financial portfolio management demonstrate its effectiveness.
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.
53957
Exploiting Problem Structure for Distributed Constraint Optimization
Distributed constraint optimization imposes considerable complexity in agents’ coordinated search for an optimal solution. However, in many application domains, problems often exhibit special structures that can be exploited to facilitate more efficient problem solving. One of the most recurrent structures involves disparity among subpmblems. We present a coordination mechanism, Anchor&Ascend, for distributed constraint optimization that takes advantage of disparity among subpmblems to efficiently guide distributed local search for global optimality. The coordination mechanism assigns different overlapping subpmblems to agents who must interact and iteratively converge on a solution. In particular, an anchor agent who conducts local best first search to optimize its subsolution interacts with the rest of the agents who perform distributed constraint satisfaction to enforce problem constraints and constraints imposed by the anchor agent. We focus our study on the well-known NP-complete job shop scheduling problem. We define and study two problem structure measures, disparity ratio and disparity composition ratio. We experimentally evaluated the effectiveness of the Anchor&Ascend mechanism on a suite of job shop scheduling problems over a wide range of values of disparity composition. Our experimental results show that (1) considerable advantage can be obtained by explicitly exploiting disparity (2) disparity composition ratio plays a more important role than disparity ratio in finding high quality solution with little computational cost.
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.
1816609
DAML-S: Semantic Markup for Web Services
The Semantic Web should enable greater access not only to content but also to services on the Web. Users and software agents should be able to discover, invoke, compose, and monitor Web resources offering particular services and having particular properties. As part of the DARPA Agent Markup Language program, we have begun to develop an ontology of services, called DAML-S, that will make these functionalities possible. In this paper we describe the overall structure of the ontology, the service profile for advertising services, and the process model for the detailed description of the operation of services. We also compare DAML-S with several industry efforts to define standards for characterizing services on the Web.
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.
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.
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.