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.
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.
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.
10197051
Dynamic service matchmaking among agents in open information environments
The amount of services and deployed software agents in the most famous o spring of the Internet, the World Wide Web, is exponentially increasing. In addition, the Internet is an open environment, where information sources, communication links and agents themselves may appear and disappear unpredictably. Thus, an e ective, automated search and selection of relevant services or agents is essential for human users and agents as well. We distinguish three general agent categories in the Cyberspace, service providers, service requester, and middle agents. Service providers provide some type of service, such as nding information, or performing some particular domain speci c problem solving. Requester agents need provider agents to perform some service for them. Agents that help locate others are called middle agents[2]. Matchmaking is the process of nding an appropriate provider for a requester through a middle agent, and has the following general form: (1) Provider agents advertise their capabilities to middle agents, (2) middle agents store these advertisements, (3) a requester asks some middle agent whether it knows of providers with desired capabilities, and (4) the middle agent matches the request against the stored advertisements and returns the result, a subset of the stored advertisements. While this process at rst glance seems very simple, it is complicated by the fact that not only local information sources but even providers and requesters in the Cyberspace are usually heterogeneous and incapable of understanding each other.
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.
13216385
Multiagent Systems
Agent-based systems technology has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. This promise is particularly attractive for creating software that operates in environments that are distributed and open, such as the internet Currently, the great majority of agent-based systems consist of a single agent However as the technology matures and addresses increasingly complex applications, the need for systems that consist of multiple agents that communicate in a peer-to-peer fashion is becoming apparent. Central to the design and effective operation of such multiagent systems (MASs) are a core set of issues and research questions that have been studied over the years by the distributed AI community. In this article, I present some of the critical notions in MASs and the research work that has addressed themI organize these notions around the concept of problem-solving coherence, which I believe is one of the most critical overall characteristics that an MAS should exhibit