The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, weather forecasts, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources are usually formatted for use by people (e.g., the relevant content is embedded in HTML pages), so extracting their content is difficult. Wrappers are often used for this purpose. A wrapper is a procedure for extracting a particular resource's content. Unfortunately, hand-coding wrappers is tedious. We introduce wrapper induction, a technique for automatically constructing wrappers. Our techniques can be described in terms of three main contributions. First, we pose the problem of wrapper construction as one of inductive learn...

The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, event listings, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources are usually formatted for use by people (e.g., the relevant content is embedded in HTML pages), so extracting their content is difficult. Most systems use customized wrapper procedures to perform this extraction task. Unfortunately, writing wrappers is tedious and error-prone. As an alternative, we advocate wrapper induction, a technique for automatically constructing wrappers. In this article, we describe six wrapper classes, and use a combination of empirical and analytical techniques to evaluate the computational tradeoffs among them. We first consider expressiveness: how well the classes can handle actual Internet resources, and the extent to which wrappers in one class can mimic those in another. We then...

The Intrusion Detection System architectures commonly used in commercial and research systems have a number of problems that limit their congurability, scalability or efficiency. The most common shortcoming in the existing architectures is that they are built around a single monolithic entity that does most of the data collection and processing. In this paper, we review our architecture for a distributed Intrusion Detection System based on multiple independent entities working collectively. We call these entities Autonomous Agents. This approach solves some of the problems previously mentioned. We present the motivation and description of the approach, partial results obtained from an early prototype, a discussion of design and implementation issues, and directions for future work.

In this paper we define the concept of conversation policies: declarative specifications that govern communications between software agents using an agent communication language. We discuss the role that conversation policies play in agent communication, and suggest several subtypes of conversation policy. Our reasoning suggests, contrary to current transition net approaches to specifying conversation policies that conversation policies are best modeled as sets of fine-grained constraints on ACL usage. These constraints then define the computational process models that are implemented in agents.

Software agents help people with time consuming activities. One increasingly popular application for software agents is electronic commerce, namely having agents buy and sell goods and services on behalf of users. We recently conducted a real-life experiment in creating an agent marketplace, using a slightly modified version of the Kasbah system [Chavez96]. Approximately 200 participants intensively interacted with the system over a one-day, six-hour period. This paper describes the setup of the experiment, the architecture of the electronic market and the behaviors of the agents. We discuss the rationale behind the design decisions and analyze the results obtained. We conclude with a discussion of current experiments involving thousands of users interacting with the agent marketplace over a long period of time, and speculate on the long-range impact of this technology upon society and the economy. 1. Introduction Software agents help people with time consuming activities [Maes95]. In...

This paper focuses on intrusion detection and countermeasures with respect to widely-used operating systems and networks. The design and architecture of an intrusion detection system built from distributed agents is proposed to implement an intelligent system on which data mining can be performed to provide global, temporal views of an entire networked system. A starting point for agent intelligence in our system is the research into the use of machine learning over system call traces from the privileged sendmail program on UNIX. We use a rule learning algorithm to classify the system call traces for intrusion detection purposes and show the results.

Many agent-based modeling and simulation researchers and practitioners have called for varying levels of simulation interoperability ranging from shared software architectures to common agent communications languages. These calls have been at least partially answered by several specifications and technologies. In fact, Tanenbaum [1988] has remarked that the “nice thing about standards is that there are so many to choose from. ” Tanenbaum goes on to say that “if you do not like any of them, you can just wait for next year’s model. ” This article does not seek to introduce next year’s model. Rather, the goal is to contribute to the larger simulation community the authors’ accumulated experiences from developing several implementations of an agent-based simulation toolkit. As such, this article focuses on the implementation of simulation architectures rather than agent communications languages. It is hoped that ongoing architecture standards efforts will benefit from this new knowledge and use it to produce architecture standards with increased robustness.

This paper sets out, ambitiously, to present a brief reappraisal of software agents research. Evidently, software agent technology has promised much. However some five years after the word `agent' came into vogue in the popular computing press, it is perhaps time the efforts in this fledgling area are thoroughly evaluated with a view to refocusing future efforts. We do not pretend to have done this in this paper -- but we hope we have sown the first seeds towards a thorough first 5-year report of the software agents area. The paper contains some strong views not necessarily widely accepted by the agent community.

In open environments there is no central control over agent behaviors. On the contrary, agents in such systems can be assumed to be primarily driven by self interests. Under the assumption that agents remain in the system for significant time periods, or that the agent composition changes only slowly, we have previously presented a prescriptive strategy for promoting and sustaining cooperation among self-interested agents. The adaptive, probabilistic policy we have prescribed promotes reciprocative cooperation that improves both individual and group performance in the long run. In the short run, however, selfish agents could still exploit reciprocative agents. In this paper, we evaluate the hypothesis that the exploitative tendencies of selfish agents can be effectively curbed if reciprocative agents share their "opinions" of other agents. Since the true nature of agents are not known a priori and is learned from experience, believing others can also pose other hazards. We provide a learned trust-based evaluation function that is shown to resist both individual and concerted deception on the part of selfish agents. 1

Distributed Knowledge Networks (DKN) provide some of the key enabling technologies for translating recent advances in automated data acquisition, digital storage, computers and communications into fundamental advances in organizational decision support, data analysis, and related applications. DKN include computational tools for accessing, organizing, transforming, and analyzing the contents of heterogeneous, distributed data and knowledge sources and for distributed problem solving and decision making under tight time, resource, and performance constraints. This paper presents an overview of the DKN project in the Iowa State University Artificial Intelligence Laboratory. I. Introduction Advanced scientific research (e.g., the genome project), military applications (e.g., intelligence data handling, situation assessment, command and control) , law enforcement (e.g., terrorism prevention), crisis management, design and manufacturing systems, and medical information infrastructure, pow...