Stephen Potter

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.

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.

I-Room: A Virtual Space for Intelligent Interaction

The I-Room is a virtual environment intended to support a range of collaborative activities, especially those that involve sense making, deliberation, and decision making. The I-Room case studies described in this paper all employ virtual worlds technology to provide this interaction space and show how this can be augmented with external knowledge-based and intelligent systems.

An Architecture for an Integrated Fire Emergency Response System for the Built Environment

FireGrid is a modern concept that aims to leverage a number of modern technologies to aid fire emergency response. In this paper we provide a brief introduction to the FireGrid project. A number of different technologies such as wireless sensor networks, grid-enabled High Performance Computing (HPC) implementation of fire models, and artificial intelligence tools need to be integrated to build up a modern fire emergency response system. We propose a system architecture that provides the framework for integration of the various technologies. We describe the components of the generic FireGrid system architecture in detail. Finally we present a small-scale demonstration experiment which has been completed to highlight the concept and application of the FireGrid system to an actual fire. Although our proposed system architecture provides a versatile framework for integration, a number of new and interesting research problems need to be solved before actual deployment of the system. We outline some of the challenges involved which require significant interdisciplinary collaborations.

Information-gathering: from sensor data to decision support in three simple steps

In this paper we describe the information-gathering problem which can be characterized as transforming large amounts of data obtained from sensors into accurate, concise, timely and meaningful information that can be used by decision makers faced with a specific task and a number of options for performing that task. The approach to this information-gathering problem as described here consists of three phases: data validation, data aggregation and abstraction, and information interpretation. Each of these phases will be described in general, and for each of these phases we describe techniques that are reasonably generic to be applicable in many domains, but domain specific knowledge will of course always be needed too.

Planning and Choosing: Augmenting HTN-Based Agents with Mental Attitudes

This paper describes a new agent framework that fuses an HTN planner, through its underlying conceptual model, with the mental attitudes of the BDI agent architecture, thus exploiting the strengths of each. On the one hand, the practical and proven ability to reason about actions that is the strength of HTN planning fleshes out the option generation function in the inference loop of the BDI model; on the other hand, the mental attitudes make explicit the knowledge that plays an essential role in plan selection, an important aspect that is not considered in the traditional formulation of the planning problem. The result is a coherent framework that allows for the design and implementation of activity-centric rational agents.

Knowledge based phylogenetic classification mining

Phylsyst is an intelligent system that mines phylogenetic classifications. Its idea stems from the work of phylogeneticists of the Societe Francaise de Systematique and proposes to test an innovative method for inferring phylogenetic classifications. The main idea in Phylsyst is to represent the reasoning of an expert phylogeneticist constructing a cladogram following Hennig principles. Several methods of artificial intelligence concur to Phylsyst’s efficient implementation of a phylogeneticist expert reasoning, the main one being data mining. Although phylogenetic tree mining has been little addressed in the data mining community, we hypothesize that this community has much to contribute to the worldwide efforts worldwide to Assemble the Tree Of Life. Phylsyst is such an attempt, and has been successfully distributed worldwide as a digital supplement to a special issue of Biosystema journal.

Knowledge and Reasoning: Issues Raised in Automating the Conceptual Design of Fluid Power Systems

Abstract Much progress has been made in the area of computer-aided designer support, but little has been made in that of design automation. Where progress has been made, it has been largely in the analytical aspects of the task (for example, simulation and stress analysis)—tasks for which computers are more suited than humans. Less tractable is automation of the early, conceptual, phase of design, heavily reliant as it is on the expert knowledge of the design practitioner. Emulating this computationally is the domain of Artificial Intelligence (AI) and requires a detailed understanding of the nature of the design process (Darlington et al, 1998). This paper discusses some of the issues raised during an investigation in to the automation of the configuration phase of fluid power system design, and identifies some of the hurdles to be cleared before automation, supported by AI, becomes a reality. Two models, developed by the authors, are chosen to illustrate the way in which very different approaches can be taken to automating the same task with an emphasis on the knowledge that is used by designers, which must be acquired and used in automation.

Cognitive Theory as a Guide to Automating the Configuration Design Process

The automation of the design process is extremely difficult; design tasks are complex and ill-defined, and generally performed by experts who have many years’ experience. Design is a typically human endeavour, and as humans offer the only example of flexible and successful design systems, any attempt to automate the process should be informed by the theories and studies of human cognition. In this paper, the authors put forward this argument in greater depth, before presenting a general cognitive framework for one particular design task, that of configuration design, the task of selecting and connecting a set of domain components to satisfy a given set of requirements. This framework has permitted the implementation of an automated configuration design tool for the domain of fluid power systems.

Critical reasoning: AI for emergency response

Effective response to emergencies depends upon the availability of accurate and focused information. The goal of the FireGrid project is to provide an architecture by which the results of computer models of physical phenomena can be made available to decision-makers leading the response to fire emergencies in the built environment. In this paper we discuss the application of a number of AI techniques in the development of FireGrid systems, and include algorithms developed for reasoning about dynamic situations. It is intended that this paper will be of technical interest to those who have to construct agents that are able to reason about the complexities of the real world, and of more general appeal to those interested in the ontological and representational commitments and compromises that underlie this reasoning.