Steve Munroe

Publications of Steve Munroe

• The provenance of electronic data.

  Authors: Luc Moreau, Paul T. Groth, Simon Miles, Javier Vázquez-Salceda, John Ibbotson, Sheng Jiang, Steve Munroe, Omer F. Rana, Andreas Schreiber, Victor Tan, László Zsolt Varga

  Commun. ACM. 01/2008; 51:52-58.

• Extracting causal graphs from an open provenance data model.

  Authors: Simon Miles, Paul T. Groth, Steve Munroe, Sheng Jiang, Thibaut Assandri, Luc Moreau

  Concurrency and Computation: Practice and Experience. 01/2008; 20:577-586.

• Special Issue: The First Provenance Challenge.

  Authors: Luc Moreau, Bertram Ludäscher, Ilkay Altintas, Roger S. Barga, Shawn Bowers, Steven P. Callahan, George Chin Jr, Ben Clifford, Shirley Cohen, Sarah Cohen Boulakia [......] Yogesh L. Simmhan, Cláudio T. Silva, Peter Slaughter, Eric G. Stephan, Robert Stevens, Daniele Turi, Huy T. Vo, Michael Wilde, Jun Zhao, Yong Zhao

  Concurrency and Computation: Practice and Experience. 01/2008; 20:409-418.

• Crossing the agent technology chasm: Lessons, experiences and challenges in commercial applications of agents.

  Authors: Steve Munroe, Tim Miller, Roxana Belecheanu, Michal Pechoucek, Peter McBurney, Michael Luck

  Knowledge Eng. Review. 01/2006; 21:345-392.

• Motivation-Based Selection of Negotiation Partners

  Authors: Steve Munroe, Michael Luck

  07/2004;

  Negotiation is key to resolving conflicts, allocating resources and establishing cooperation in systems of selfinterested agents. Often, an agent may have to select between different potentialNegotiation is key to resolving conflicts, allocating resources and establishing cooperation in systems of selfinterested agents. Often, an agent may have to select between different potential negotiation partners, and identifying which offers the best chance of a successful negotiation is a challenging task. However, poor selection of partners can result in failure or in inefficient outcomes. To that end, this paper describes a motivation-based mechanism to evaluate and select between negotiation candidates. This is achieved by a twofold process: first, acceptable candidates are identified using motivation-based thresholds on objective scoring measures; second, the importance of issues is considered, and expected performance measures are evaluated accordingly. The mechanism is described and empirically evaluated.

• Trust and norms for interaction.

  Authors: Michael Luck, Steve Munroe, Ronald Ashri, Fabiola López y López

  Proceedings of the IEEE International Conference on Systems, Man & Cybernetics: The Hague, Netherlands, 10-13 October 2004; 01/2004

• Unknown

  Authors: Michael Luck, Steve Munroe

  05/2003;

  In the paper we discuss variable and generative forms of autonomy. Variable autonomy is discussed in terms of the practicalities in designing autonomous agents, dealing as it does with the notion ofIn the paper we discuss variable and generative forms of autonomy. Variable autonomy is discussed in terms of the practicalities in designing autonomous agents, dealing as it does with the notion of degrees of autonomy and hence issues of agent control. The major part of the paper discusses an absolute, theoretically grounded notion of autonomy: the ability to generate one's own goals. This theoretical account of autonomy is embedded in the larger SMART framework and is intimately linked with the issue of motivation. Autonomous agents are motivated agents in that for the generation of goals an agent needs a set of higher order, non-derivative sources of action, or in our terminology, motivations. Autonomous agents in the SMART framework form the basis and source of action in multi-agent systems, which can thus propagate through the other entities in the system, such as non-autonomous agents and objects. We conclude with a discussion regarding the situations an autonomous agent would be willing to relinquish its autonomy thus linking the generative and variable notions of autonomy.

• Towards a Motivation-Based Approach for Evaluating Goals

  Authors: Steve Munroe, Michael Luck, Mark d'Inverno

  Traditional goal-oriented approaches to building intelligent agents only consider absolute satisfaction of goals. However, in continuous domains there may be many instances in which a goal stateTraditional goal-oriented approaches to building intelligent agents only consider absolute satisfaction of goals. However, in continuous domains there may be many instances in which a goal state can only be partially satisfied. In these situations the traditional symbolic goal representation needs modifying in order that an agent can determine a worth value of a goal state and also of any state approximating the goal. In our work we use the concept of worth in two ways. First, we propose a mechanism by which the worth of a goal is dynamically set as a function of the intensity of an underlying motivation. Second, we determine the worth of any state in relation to a goal through the use of a metric by which we can measure the proximity of an environmental state to a goal. In this way, it is possible to make judgements about the relative satisfaction an environmental state offers in regard to a goal.

• Agent Autonomy Through the 3M Motivational Taxonomy

  Authors: Steve Munroe, Michael Luck

• Trust and Norms for Interaction

  Authors: Michael Luck, Steve Munroe, F Lopez y Lopez, Ronald Ashri

• An Architecture for Provenance Systems Executive Summary

  Authors: Paul Groth, Sheng Jiang, Simon Miles, Steve Munroe, Victor Tan, Sofia Tsasakou, Luc Moreau

• Principles of High Quality Documentation for Provenance: A Philosophical Discussion

  Authors: Paul Groth, Simon Miles, Steve Munroe

  Computer technology enables the creation of detailed documentation about the processes that create or affect entities (data, objects, etc.). Such documentation of the past can be used to answerComputer technology enables the creation of detailed documentation about the processes that create or affect entities (data, objects, etc.). Such documentation of the past can be used to answer various kinds of questions regarding the processes that led to the creation or modification of a particular entity. The answer to such questions are known as an entity’s provenance. In this paper, we derive a number of principles for documenting the past, grounded in work from philosophy and history, which allow for provenance questions to be answered within a computational context. These principles lead us to argue that an interaction-based model is particularly suited for representing high quality documentation of the past.

• The First Provenance Challenge

  Authors: Luc Moreau, Bertram Ludaescher, Ilkay Altintas, Roger S. Barga, Shawn Bowers, Steven Callahan, George Chin Jr, Ben Clifford, Shirley Cohen, Sarah Cohen-Boulakia [......] Margo Seltzer, Yogesh L. Simmhan, Claudio Silva, Peter Slaughter, Eric Stephan, Robert Stevens, Daniele Turi, Huy Vo, Mike Wilde, Jun Zhao

  The first Provenance Challenge was set up in order to provide a forum for the community to help understand the capabilities of different provenance systems and the expressiveness of their provenanceThe first Provenance Challenge was set up in order to provide a forum for the community to help understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarise the participants contributions.

• PrIMe: A Methodology for Developing Provenance-Aware Applications

  Authors: Simon Miles, Paul Groth, Steve Munroe, Luc Moreau

  Provenance refers to the past processes that brought about a given (version of an) object, item or entity. By knowing the provenance of data, users can often better understand, trust, reproduce, andProvenance refers to the past processes that brought about a given (version of an) object, item or entity. By knowing the provenance of data, users can often better understand, trust, reproduce, and validate it. A provenance-aware application has the functionality to answer questions regard- ing the provenance of the data it produces, by using documentation of past processes. PrIMe is a software engineering technique for adapting application designs to enable them to interact with a provenance middleware layer, thereby making them provenance-aware. In this article, we specify the steps involved in applying PrIMe, analyse its effectiveness, and illustrate its use with two case studies, in bioinformatics and medicine.

• An Architecture for Provenance Systems

  Authors: Paul Groth, Sheng Jiang, Simon Miles, Steve Munroe, Victor Tan, Sofia Tsasakou, Luc Moreau

  This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discussesThis document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies.

• Commercial Applications of Agents: Lessons, Experiences and Challenges

  Authors: Roxana Belecheanu, Steve Munroe, Michael Luck, Terry R. Payne, Tim Miller, Peter McBurney, Michal Pechoucek

• The Provenance of Electronic Data

  Authors: Luc Moreau, Paul Groth, Simon Miles, Javier Vazquez, John Ibbotson, Sheng Jiang, Steve Munroe, Omer Rana, Andreas Schreiber, Victor Tan, Laszlo Varga

  In the study of fine art, provenance refers to the documented history of some art object. Given that documented history, the object attains an authority that allows scholars to appreciate itsIn the study of fine art, provenance refers to the documented history of some art object. Given that documented history, the object attains an authority that allows scholars to appreciate its importance with respect to other works, whereas, in the absence of such history, the object may be treated with some skepticism. Our IT landscape is evolving as illustrated by applications that are open, composed dynamically, and that discover results and services on the fly. Against this challenging background, it is crucial for users to be able to have confidence in the results produced by such applications. If the provenance of data produced by computer systems could be determined as it can for some works of art, then users, in their daily applications, would be able to interpret and judge the quality of data better. We introduce a provenance lifecycle and advocate an open approach based on two key principles to support a notion of provenance in computer systems: documentation of execution and user-tailored provenance queries.

• AgentPrIMe: Adapting MAS Designs to Build Confidence

  Authors: Simon Miles, Paul Groth, Steve Munroe, Michael Luck, Luc Moreau

  The products of systems cannot always be judged at face value: the process by which they were obtained is also important. For instance, the rigour of a scientific experiment, the ethics with which anThe products of systems cannot always be judged at face value: the process by which they were obtained is also important. For instance, the rigour of a scientific experiment, the ethics with which an item was manufactured and the use of services with particular licens- ing all affect how the results of those processes are valued. However, in systems of autonomous agents, and particularly those with multiple independent contributory organisations, the ability of agents to choose how their goals or responsibilities are achieved can hide such process qualities from users. The issue of ensuring that users are able to check these process qualities is a software engineering one: the developer must decide to ensure that adequate data is recorded regarding processes and safeguards implemented to ensure accuracy. In this paper, we describe AgentPrIMe, an adjunct to existing agent-oriented methodologies that allows system designs to be adapted to give users confidence in the results they produce. It does this by adaptations to the design for documenta- tion, corroboration, independent storage and accountability.

• Modelling the Provenance of Data in Autonomous Systems

  Authors: Simon Miles, Steve Munroe, Michael Luck, Luc Moreau

  Determining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrablyDetermining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrably rigorous for the result to be deemed reliable. A provenance system supports applications in recording adequate documentation about process executions to answer queries regarding provenance, and provides functionality to perform those queries. Several provenance systems are being developed, but all focus on systems in which the components are reactive, for example Web Services that act on the basis of a request, job submission system, etc. This limitation means that questions regarding the motives of autonomous actors, or agents, in such systems remain unanswerable in the general case. Such questions include: who was ultimately responsible for a given effect, what was their reason for initiating the process and does the effect of a process match what was intended to occur by those initiating the process? In this paper, we address this limitation by integrating two solutions: a generic, re-usable framework for representing the provenance of data in service-oriented architectures and a model for describing the goal-oriented delegation and engagement of agents in multi-agent systems. Using these solutions, we present algorithms to answer common questions regarding responsibility and success of a process and evaluate the approach with a simulated healthcare example.

• Security Issues in a SOA-based Provenance System

  Authors: Victor Tan, Paul Groth, Simon Miles, Sheng Jiang, Steve Munroe, Sofia Tsasakou, Luc Moreau

  Recent work has begun exploring the characterization and utilization of provenance in systems based on the Service Oriented Architecture (such as Web Services and Grid based environments). One of theRecent work has begun exploring the characterization and utilization of provenance in systems based on the Service Oriented Architecture (such as Web Services and Grid based environments). One of the salient issues related to provenance use within any given system is its security. Provenance presents some unique security requirements of its own, which are additionally dependent on the architectural and environmental context that a provenance system operates in. We discuss the security considerations pertaining to a Service Oriented Architecture based provenance system. Concurrently, we outline possible approaches to address them.