Renia Jeffers

Applying KAoS services to ensure policy compliance for semantic web services workflow composition and enactment

In this paper we describe our experience in applying KAoS services to ensure policy compliance for Semantic Web Services workflow composition and enactment. We are developing these capabilities within the context of two applications: Coalition Search and Rescue (CoSAR-TS) and Semantic Firewall (SFW). We describe how this work has uncovered requirements for increasing the expressivity of policy beyond what can be done with description logic (e.g., role-value-maps), and how we are extending our representation and reasoning mechanisms in a carefully controlled manner to that end. Since KAoS employs OWL for policy representation, it fits naturally with the use of OWL-S workflow descriptions generated by the AIAI I-X planning system in the CoSAR-TS application. The advanced reasoning mechanisms of KAoS are based on the JTP inference engine and enable the analysis of classes and instances of processes from a policy perspective. As the result of analysis, KAoS concludes whether a particular workflow step is allowed by policy and whether the performance of this step would incur additional policy-generated obligations. Issues in the representation of processes within OWL-S are described. Besides what is done during workflow composition, aspects of policy compliance can be checked at runtime when a workflow is enacted. We illustrate these capabilities through two application examples. Finally, we outline plans for future work.

KAoS policy and domain services: toward a description-logic approach to policy representation, deconfliction, and enforcement

We describe our initial implementation of the KAoS policy and domain services. While primarily oriented to the dynamic and complex requirements of software agent applications, the services are also being adapted to general-purpose grid computing and Web services environments as well. The KAoS services rely on a DAML description-logic-based ontology of the computational environment, application context, and the policies themselves that enables runtime extensibility and adaptability of the system, as well as the ability to analyze policies relating to entities described at different levels of abstraction.

Semantic web languages for policy representation and reasoning: a comparison of KAoS, Rei, and Ponder

Policies are being increasingly used for automated system management and controlling the behavior of complex systems. The use of policies allows administrators to modify system behavior without changing source code or requiring the consent or cooperation of the components being governed. Early approaches to policy representation have been restrictive in many ways. However semantically-rich policy representations can reduce human error, simplify policy analysis, reduce policy conflicts, and facilitate interoperability. In this paper, we compare three approaches to policy representation, reasoning, and enforcement. We highlight similarities and differences between Ponder, KAoS, and Rei, and sketch out some general criteria and properties for more adequate approaches to policy semantics in the future.

Strong Mobility and Fine-Grained Resource Control in NOMADS

NOMADS is a Java-based agent system that supports strong mobility (i.e., the ability to capture and transfer the full execution state of migrating agents) and safe agent execution (i.e., the ability to control resources consumed by agents, facilitating guarantees of quality of service while protecting against denial of service attacks). The NOMADS environment is composed of two parts: an agent execution environment called Oasis and a new Java-compatible Virtual Machine (VM) called Aroma. The combination of Oasis and the Aroma VM provides key enhancements over today’s Java agent environments.

Adjustable Autonomy and Human-Agent Teamwork in Practice: An Interim Report on Space Applications

We give a preliminary perspective on the basic principles and pitfalls of adjustable autonomy and human-centered teamwork. We then summarize the interim results of our study on the problem of work practice modeling and human-agent collaboration in space applications, the development of a broad model of human-agent teamwork grounded in practice, and the integration of the Brahms, KAoS, and NOMADS agent frameworks We hope our work will benefit those who plan and participate in work activities in a wide variety of space applications, as well as those who are interested in design and execution tools for teams of robots that can function as effective assistants to humans.

Mobile-Agent versus Client/Server Performance: Scalability in an Information-Retrieval Task

Building applications with mobile agents often reduces the bandwidth required for the application, and improves performance. The cost is increased server workload. There are, however, few studies of the scalability of mobile-agent systems. We present scalability experiments that compare four mobile-agent platforms with a traditional client/server approach. The four mobile-agent platforms have similar behavior, but their absolute performance varies with underlying implementation choices. Our experiments demonstrate the complex interaction between environmental, application, and system parameters.

NOMADS: toward a strong and safe mobile agent system

NOMADS is a mobile agent system that supports strong mobility (i.e., the ability to capture and transfer the full execution state of mobile agents) and safe Java agent execution (i.e., the ability to control resources consumed by agents, facilitating guarantees of quality of service while protecting against denial of service attacks). The NOMADS environment is composed of two parts: an agent execution environment called Oasis and a new Javacompatible Virtual Machine (VM) called Aroma. The combination of Oasis and the Aroma VM provides key enhancements over todays Java agent environments.

DAML-based policy enforcement for semantic data transformation and filtering in multi-agent systems

This paper describes an approach to runtime policy-based control over information exchange that allows a far more fine-grained control of these dynamically discovered agent interactions. The DARPA Agent Markup Language (DAML) is used to represent policies that may either filter messages based on their semantic content or transform the messages to make them suitable to be released. Policy definition, management, and enforcement are realized as part of the KAoS architecture. The solutions presented have been tested in the Coalition Agents Experiment (CoAX) - an experiment involving coalition military operations.

Terraforming cyberspace

Like preterraformed Mars, cyberspace currently offers a lonely, dangerous, and relatively impoverished environment for software agents. Although promoted as collaborative, agents do not easily sustain rich, long-term, peer-to-peer relationships, let alone any semblance of meaningful community involvement. Rather than just building smarter and stronger agents, researchers must transform the wasteland of cyberspace itself, making it a safe and habitable environment for both agents and humans. The paper discusses how the basic infrastructure for beginning a terraforming effort is becoming more available. Designed specifically to exploit next-generation Internet capabilities, grid-based approaches provide a universal source of dynamically pluggable, pervasive, and dependable computing power, while guaranteeing levels of security and duality of service that will make new kinds of applications possible.

Toward DAML-based policy enforcement for semantic data transformation and filtering in multi-agent systems

This paper describes an approach to runtime policy-based control over information exchange that allows a far more fine-grained control of these dynamically discovered agent interactions. The DARPA Agent Markup Language (DAML) is used to represent policies that may either filter messages based on their semantic content or transform the messages to make them suitable to be released. Policy definition, management, and enforcement are realized as part of the KAoS architecture. The solutions presented have been tested in the Coalition Agents Experiment (CoAX)—an experiment involving coalition military operations.