Austin Tate

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.

O-Plan: the open planning architecture

Abstract O-Plan is an AI planner based on previous experience with the Nonlin planner and its derivatives. Nonlin and other similar planning systems had limited control architectures and were only partially successful at limiting their search spaces. O-Plan is a design and implementation of a more flexible system aimed at supporting planning research and development, opening up new planning methods and supporting strong search control heuristics. O-Plan takes an engineering approach to the construction of an efficient domain-independent planning system which includes a mixture of AI and numerical techniques from operations research. The main contributions of the work are centred around the control of search within the O-Plan planning framework, and this paper outlines the search control heuristics employed within the planner. These involve the use of condition typing, time and resource constraints and domain constraints to allow knowledge about an application domain to be used to prune the search for a solution. The paper also describes aspects of the O-Plan user interface, domain description language (Task Formalism or TF) and the domains to which O-Plan has been applied.

AI planning: systems and techniques

This article reviews research in the development of plan generation systems. Our goal is to familiarize the reader with some of the important problems that have arisen in the design of planning systems and to discuss some of the many solutions that have been developed in the over 30 years of research in this area. In this article, we broadly cover the major ideas in the field of AI planning and show the direction in which some current research is going. We define some of the terms commonly used in the planning literature, describe some of the basic issues coming from the design of planning systems, and survey results in the area. Because such tasks are virtually never ending, and thus, any finite document must be incomplete, we provide references to connect each idea to the appropriate literature and allow readers access to the work most relevant to their own research or applications.

The Process Interchange Format and Framework

This document provides the specification of the Process Interchange Format (PIF) version 1.2. The goal of this work is to develop an interchange format to help automatically exchange process descriptions among a wide variety of business process modelling and support systems such as workflow software, flow charting tools, planners, process simulation systems and process repositories. Instead of having to write ad hoc translators for each pair of such systems each system will only need to have a single translator for converting process descriptions in that system into and out of the common PIF format. Then any system will be able to automatically exchange basic process descriptions with any other system. This document describes the PIF-CORE 1.2, i.e. the core set of object types (such as activities, agents and prerequisite relations) that can be used to describe the basic elements of any process. The document also describes a framework for extending the core set of object types to include additional information needed in specific applications. These extended descriptions are exchanged in such a way that the common elements are interpretable by any PIF translator, and the additional elements are interpretable by any translator that knows about the extensions. The PIF format was developed by a working group including representatives from several universities and companies, and has been used for experimental automatic translations among systems developed independently at three of these sites. This document is being distributed in the hopes that other groups will comment upon the interchange format proposed here, and that this format (or future versions of it) may be useful to other groups as well. The PIF Document 1.0 was released in December 1994, and the current document reports the revised PIF that incorporate the feedback received since then.

Constraints and AI planning

Tackling real-world planning problems often requires considering various types of constraints, which can range from simple numerical comparators to complex resources. This article provides an overview of techniques to deal with such constraints by expressing planning within general constraint-solving frameworks. Our goal here is to explore the interplay of constraints and planning, highlighting the differences between propositional satisfiability (SAT), integer programming (IP), and constraint programming (CP), and discuss their potential in expressing and solving AI planning problems.

Roots of SPAR — Shared Planning and Activity Representation

The Defense Advanced Research Projects Agency (DARPA) and US Air Force Research Laboratory Planning Initiative (ARPI) has initiated a project to draw on the range of previous work in planning and activity ontologies to create a practically useful Shared Planning and Activity Representation (SPAR) for use in technology and applications projects within their communities. This article describes the previous work which has been used to create the initial SPAR representation. Key examples of the work drawn upon are published in this issue. The paper provides a comprehensive bibliography and related world wide web resources for work in the area of plan, process and activity representation. SPAR is now being subjected to refinement during several review cycles by a number of expert and user panels.

FireGrid: An e-infrastructure for next-generation emergency response support

The FireGrid project aims to harness the potential of advanced forms of computation to support the response to large-scale emergencies (with an initial focus on the response to fires in the built environment). Computational models of physical phenomena are developed, and then deployed and computed on High Performance Computing resources to infer incident conditions by assimilating live sensor data from an emergency in real time-or, in the case of predictive models, faster-than-real time. The results of these models are then interpreted by a knowledge-based reasoning scheme to provide decision support information in appropriate terms for the emergency responder. These models are accessed over a Grid from an agent-based system, of which the human responders form an integral part. This paper proposes a novel FireGrid architecture, and describes the rationale behind this architecture and the research results of its application to a large-scale fire experiment.

Coalition Agents Experiment: multiagent cooperation in international coalitions

The Coalition Agents Experiment aims to show that multi-agent systems offer effective tools for dealing with complex real-world problems by enabling agile and robust coalition operations and interoperability between heterogeneous military systems.

Collaboration in the Semantic Grid: A Basis for e-Learning

The CoAKTinG project aims to advance the state of the art in collaborative mediated spaces for the Semantic Grid. This paper presents an overview of the hypertext and knowledge-based tools that have been deployed to augment exiting collaborative environments, and the ontology that is used to exchange structure, promote enhanced process tracking, and aid navigation of resources before, after, and during a collaboration. While the primary focus of the project has been supporting e-Science, this paper also explores the similarities and application of CoAKTinG technologies as part of a human-centered design approach to e-Learning.

Putting ontologies to use

Interest in the nature, development and use of ontologies is becoming increasingly widespread. Since the early nineties, numerous workshops have been held. Representatives from historically separate disciplines concerned with philosophical issues, knowledge acquisition and representation, planning, process management, database schema integration, natural language processing and enterprise modelling, came together to identify a common core of issues of interest. There was highly varied and inconsistent usage of a wide variety of terms, most notably, “ontology”, rendering cross-discipline communication difficult. However, progress was made toward understanding the commonality among the disciplines. Subsequent workshops addressed various aspects of the field, including theoretical issues, methodologies for building ontologies, as well as specific applications in government and industry.