Complex applications in many areas, including scientific computations and business-related web services, are created from collections of components to form computational workflows. In many cases end users have requirements and preferences that depend on how the workflow unfolds, and that cannot be specified beforehand. Workflow editors therefore need to be augmented with intelligent assistance in order to help users in several key aspects of the task, namely: 1) keeping track of detailed constraints across selected components and their connections; 2) accommodating flexibly different strategies to construct workflows; e.g., from general knowledge of necessary tasks, from desired results, or from available data; and 3) taking partial or incomplete descriptions of workflows and understanding the steps needed for their completion. We have developed a system called CAT (Composition Analysis Tool) that analyzes workflows and generates error messages and suggestions in order to help users compose complete and consistent workflows. Our approach combines knowledge bases, which have rich representations of components and constraints, together with planning techniques that can track the relations and constraints among individual components. We have formalized our approach based on AI planning principles, allowing us to formulate claims about the underlying algorithms as well as the resulting workflows.

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In this paper, we present a case study of KBS engines built thanks to our knowledge-based system (KBS) development platform Lama. The Lama platform enables the software-level composition of KBS engines (hence PSMs) for a given task from reusable reasoning blocks. This paper emphasizes engine design activity and shows how our platform helps easily create new engines or modify existing ones. Indeed, designers need help to build the problem-solving method (PSM) of an engine, appropriately for an application domain. The current available tools neither integrate designers' knowledge at the right level, nor ooeer development, integration and testing facilities to implement KBS engines. Our experience tackles problems such as PSM and ontology design and (partial) reuse. This involves aspects such as the choice of an appropriate set of reasoning blocks to cover the needs of a problem-solving task, with the right level of granularity. As a first experimental field, we focus on program supervision...

There are several works whose goal is to specify complete and sound planning systems based on general purpose theorem provers. Some planners implemented in this way can have a close correspondence with existing partial-ordered planning algorithms. To improve the efficiency of logic-based planners we would like to use some of the results achieved by the AI planning community over the past twenty years in terms of algorithm design. We claim that a knowledge level analysis of problem-solving methods for planning, can help to identify what is the role of each piece of knowledge in a system and provide a common language to map, classify and compare different systems. In this paper we analyze an abductive event calculus planner using a library of problem-solving methods for planning.

Introduction to the Project Planform is a 26 month long project with a planned starting date of October 1st, 1999. It will be led by Prof Lee McCluskey at University of Huddersfield, Ms Ruth Aylett at the University of Salford, and Dr Maria Fox and Dr Derek Long at the University of Durham. There will be three other post- doctoral members of the team, one at each of the three Universities. The project is funded by the EPSRC, but it also will be supported by the UK National Air Traffic Services Ltd, and CogSys Ltd. The total value of the project is estimated at around 400,000 pounds. Project Relevance Projects such as those sponsored by ARPI [1] and NASA [13] have shown that large-scale planning systems - such as SIPE-II and O-Plan - developed in research centres, can be cost effective. AI Planning systems such as these are domain-independent, that is, their algorithms and representational facilities are logically separate from the model of a particular applicat

We present a compilation-based approach to reducing the representational distance between application domain experts and AI planning technology. The approach combines a representation designed to match the structure of human expertise in the construction industry with an established planning technique. The design of this representation is derived from a study carried out with experts in the industry. This study shows that expertise in the industry is centred on the components of a building and organised into a subcomponent structure. We demonstrate by encoding the results of this study into a HTN formalism that such formalisms fragment expert knowledge. This fragmentation leads to a large representational distance between expert and formalism, making the task of encoding and maintaining a planner knowledge base a complex one. Our solution is to provide a representation designed around the modelling requirements of the construction industry and then to compile HTN sche...

We present a compilation-based approach that combines a formalism designed to match the structure of human expertise in the construction industry with an established planning technique. The domain's modelling requirements are derived from a study carried out with domain experts. This study shows that expertise in the industry is centred on the components of a building organised into a subcomponent structure. Encoding the results of this study into a HTN formalism demonstrates that such formalism fragment expert knowledge. This fragmentation leads to a large semantic distance between expert and formalism, making the task of encoding and maintaining a planner knowledge base a complex one. Our solution is to provide a formalism designed around the modelling requirements of the constriction industry and to then compile HTN schemas from that representation. We argue that this union reduces the semantic gap between knowledge engineer and formalism, thus lowering the complexity of the knowledge encoding and maintenance tasks, whilst still exploiting powerful AI planning techniques. We conclude by encouraging further work of this type with the aim of providing a library of domain oriented formalisms form which the knowledge engineer may choose an appropriate representation for a domain.