Gerhard Wickler

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.

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.

KEWI: A Knowledge Engineering Tool for Modelling AI Planning Tasks

This paper introduces the Knowledge Engineering Web Interface (KEWI) which primarily aims to be used for modelling automated planning tasks in a semi-formal framework. The conceptual model used to represent the declarative and procedural knowledge in KEWI is described formally. The model consists of three layers: a rich ontology, a model of basic actions, and more complex methods. It is this structured conceptual model based on the rich ontology that facilitates knowledge engineering. The focus of this paper is to show how the central knowledge model used in KEWI differs from a model directly encoded in PDDL, the language accepted by most existing planning engines. Specifically, the rich ontology enables a more concise and natural style of representation. For operational use, KEWI automatically generates PDDL. Initial experiments show that the generated PDDL can be processed by a planner without incurring significant drawbacks.