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.
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.
1404695
Designing behaviors for information agents
To facilitate the rapid development and open system interoperability of autonomous agents we need to carefully specify and effectively implement various classes of agent behaviors. Our current focus is on the behaviors and underlying architecture of WWW-based autonomous software agents that collect and supply information to humans and other computational agents. This paper discusses a set of architectural building blocks that support the specification of behaviors for these information agents in a way that allows periodic actions, interleaving of planning and execution, and the concurrent activation of multiple behaviors with asynchronous components. We present an initial set of information agent behaviors, including responding to repetitive queries, monitoring information sources, advertising capabilities, and self cloning. We have implemented and tested these behaviors on the WWW in the context of WARREN, an open multi-agent organization for financial portfolio management.
440209
Benefits of Learning in Negotiation
Negotiation has been extensively discussed in game-theoretic, economic, and management science literatures for decades. Recent growing interest in electronic commerce has given increased importance to automated negotiation. Evidence both from theoretical analysis and from observations of human interactions suggests that if decision makers can somehow take into consideration what other agents are thinking and furthermore learn during their interactions how other agents behave, their payoff might increase. In this paper, we propose a sequential decision making model of negotiation, called Bazaar. Within the proposed negotiation framework, we model learning as a Bayesian belief update process. In this paper, we explore the hypothesis that learning is beneficial in sequential negotiation and present initial experimental results.
1410914
Collective problem solving through coordinated reaction
We present a methodology, called Constraint Partition and Coordinated Reaction (CP&CR), for collective, evolutionary problem solving. Problem solving is viewed as an emergent functionality from the evolving process of a group of diverse, interacting, and well-coordinated reactive agents. Cheap and effective search knowledge is extracted from local interactions and embedded in the coordination mechanism. Our domain of problem solving is constraint satisfaction problems. We have applied the methodology to job shop scheduling, an NP-complete constraint satisfaction problem. Experimental results on a benchmark suite of problems show that CP&CR outperformed three other state-of-the-art direct search scheduling techniques, in both efficiency and number of problems solved. In addition, CP&CR was experimentally tested on problems of larger sizes and showed favorable scaling-up characteristics.<<ETX>>
79982
Research Directions for Service-Oriented Multiagent Systems
Today's service-oriented systems realize many ideas from the research conducted a decade or so ago in multiagent systems. Because these two fields are so deeply connected, further advances in multiagent systems could feed into tomorrow's successful service-oriented computing approaches. This article describes a 15-year roadmap for service-oriented multiagent system research.
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.
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.